https://learnlab.org/mediawiki-1.44.2/api.php?action=feedcontributions&feedformat=atom&user=Perfetti 2026-04-30T16:13:07Z User contributions MediaWiki 1.44.2 https://learnlab.org/mediawiki-1.44.2/index.php?title=Implicit_instruction&diff=7652 2008-04-05T14:24:06Z

<p>Perfetti: </p> <hr /> <div>Implicit instruction occurs in instructional tasks that do not provide specific guidance on what is to be learned from the task. It may provide [[example]]s, uses, instances, illustrations, or visualizations of a [[knowledge components]] without a direct statement (or rule) that specifically directs the learner on what is to be learned ([[knowledge component]]). It contrasts with [[explicit instruction]]. <br /> <br /> The National Reading Panel identified five main methods for teaching vocabulary (NRP, 2000, p. 4-3), the first two of which provide an illustration of the difference between explicit and implicit instruction:<br /> <br /> * &quot;Explicit Instruction: Students are given definitions or other attributes of words to be learned.<br /> * '''''Implicit Instruction''''': Students are exposed to words or given opportunities to do a great deal of reading.&quot;<br /> <br /> Implicit instruction and implicit learning (see below) are not the same. Implicit instruction affords implicit learning, but implicit instruction can also be processed explicitly and lead to explicit learning. For instance, when a student is given a [[worked examples|worked example]], a form of implicit instruction, and spontaneously decides to explain the reasoning behind the steps taken (see [[self-explanation]]), that student is engaged in explicit learning (trying to generate a verbal rule) in the face of implicit instruction.<br /> <br /> Ellis (1994) provides definitions of implicit and explicit learning:<br /> <br /> &quot;Implicit learning is acquisition of knowledge about the underlying structure of a complex stimulus environment by a process which takes place naturally, simply and without conscious operations. Explicit learning is a more conscious operation where the individual makes and tests hypotheses in a search for structure. Knowledge attainment can thus take place implicitly (a nonconscious and automatic abstraction of the structural nature of the material arrived at from experience of instances), explicitly through selective learning (the learner searching for information and building then testing hypotheses), or, because we can communicate using language, explicitly via given rules (assimilation of a rule following explicit instruction).&quot; (Ellis, 1994, p. 1f)<br /> <br /> See also the distinction in ACT-R (e.g., Anderson &amp; Lebiere, 1998) between procedural knowledge, which is implicitly processed and learned, and declarative knowledge, which includes (but is not limited to) explicit verbal knowledge and is open to (but does not require) explicit processing and learning.<br /> <br /> Often instructional forms or [[sources]] combine implicit and explicit instruction, for instance, examples and rules (see [[example-rule coordination]]) or diagrams and text (see [[visual-verbal coordination]]), and the potential benefits of such combinations are a topic of the [[Coordinative Learning]] cluster.<br /> <br /> === References ===<br /> <br /> * Anderson, J. R., &amp; Lebiere, C. (1998). ''The Atomic Components of Thought.'' Hillsdale, NJ: Erlbaum.<br /> *Ellis, N. C. (ed.) (1994). ''Implicit and Explicit Learning of Languages.'' San Diego/CA: Academic Press.<br /> <br /> [[Category:Glossary]]<br /> [[Category:Independent Variables]]<br /> [[Category:PSLC General]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Explicit_instruction&diff=7651 2008-04-05T14:18:35Z

<p>Perfetti: </p> <hr /> <div>Explicit instruction occurs in an instructional task that provides the learner with specific information or directions about what is to be learned from the task. Explicit instruction often comes in the form of rules or verbal statements that provide guidance to the student about what is to be learned. Although explicit instruction contrasts with [[implicit instruction]], instructional tasks are often graded with elements of each. Thus instruction can be relatively explicit or relatively implicit. Explicit instruction works through [[feature focusing]], drawing the learner's attention to the valid or critical features ([[feature validity]]of the content to be learned. Implicit learning can be designed to promote feature focusing, as well, although often it does not.<br /> <br /> [[Instructional explanation]]s are one specific type of explicit instruction.<br /> <br /> [[Category:Glossary]]<br /> [[Category:Independent Variables]]<br /> [[Category:Help Tutor]]<br /> [[Category:PSLC General]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=6725 2008-01-11T02:27:45Z

<p>Perfetti: /* Hypotheses */</p> <hr /> <div>= The PSLC Refinement and Fluency cluster =<br /> <br /> == Abstract ==<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> <br /> 1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components. <br /> <br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> <br /> 3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br /> <br /> 4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information (&quot;facts&quot;) or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.<br /> <br /> 5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns. <br /> <br /> 6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.<br /> <br /> 7. [[cue validity]]: In both explicit and implicit instruction, the validity of a cue for a knowledge component affects the learning of that knowledge component. (Cue validity is related to [[feature validity]].)<br /> <br /> 8. [[focusing]]: Instruction that directs (focuses) the learner's attention to valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 9. learning to learn: The acquisition of skills and strategies that can generalize across learning tasks can promote new learning. Examples may be deep analysis, help-seeking, use of advance organizers, and, most generally, meta-cognitive strategies. <br /> <br /> 10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while inhibiting others.<br /> <br /> The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> &lt;br&gt;&lt;center&gt;[[Image:rf-theory.jpg]]&lt;/center&gt;<br /> <br /> ==Significance==<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> == Glossary ==<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> == Research question ==<br /> The overall research question is how can instruction optimally support the acquisition, refinement, and fluent use of complex targeted knowledge, taking into account the learner’s existing knowledge in relation to the knowledge demands of the target domain? In examining this general question, the studies focus on features of the learning situation, including the following: the cognitive demands of targeted knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br /> <br /> == Independent variables ==<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> == Dependent variables ==<br /> The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br /> <br /> == Hypotheses ==<br /> Instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. More specifically, the initial acquisition of knowledge and its refinement benefit from instructional activities that require the learner to attend to and encode [[valid features]] of the learning content. The fluency corollary: Fluency builds on the knowledge components acquired and refined in learning, strengthening and integrating these components through practice.<br /> <br /> <br /> Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br /> <br /> 1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> <br /> 2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby &quot;divergent coding systems&quot; here may be the same as &quot;multiple input sources&quot; in co-training.)<br /> <br /> 3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> <br /> 4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> <br /> 5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> <br /> 6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br /> <br /> 7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 8. Learning to learn hypothesis: The acquisition of certain skills in one context support future learning in other contexts. Such skills include problem analysis, help-seeking, or advance organizers. <br /> <br /> 9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> 10. Active learning hypothesis: Even in simple tasks, learning is more robust when the learner actively engages in the learning material.<br /> <br /> == Explanation ==<br /> All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br /> <br /> == Descendents ==<br /> <br /> === Explicit instruction ===<br /> '''A. Explicit vs Implicit.''' These projects typically compare a more explict form of instruction with a more implict form <br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> * [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br /> <br /> '''B. Explicit attention manipulations''' studies typically vary features available to learner<br /> * [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> * [[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu) [Also Coordinative learning]<br /> * [[Learning French gender cues with prototypes]] (Presson, MacWhinney)<br /> <br /> '''C. Explicit instruction: Practice and Scheduling''' Typical studies control practice events and provide feedback<br /> * [[Optimizing the practice schedule]] (Pavlik et al.) [[Applying optimal scheduling of practice in the Chinese Learnlab|1]]<br /> * [[French gender cues | French grammatical gender cue learning]] (Presson, MacWhinney)<br /> * [[Japanese fluency]] (Yoshimura-MacWhinney)<br /> * [[Fostering fluency in second language learning]] (De Jong, Perfetti)<br /> * [[Using learning curves to optimize problem assignment]] (Cen &amp; Koedinger)<br /> <br /> === Knowledge accessibility ===<br /> '''A. Background knowledge''' These projects directly study effects of learners' background knowledge<br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> * [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Also in Interactive Communication]<br /> * [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br /> <br /> '''B. Availability of knowledge during learning'''<br /> * [[Optimizing the practice schedule]] (Pavlik et al.) [[Understanding paired associate transfer effects based on shared stimulus components|2]], [[Applying optimal scheduling of practice in the Chinese Learnlab|1]], [[Understanding encoding inhibition, retrieval inhibition and destructive interference effects of errors during practice|3]]<br /> * [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br /> * [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> * [[Arithmetical fluency project]] (Fiez)<br /> * [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, &amp; Liu)<br /> * [[Integrated Learning of Chinese]] (Liu, Perfetti, Wang, Wu)<br /> <br /> === Active processing ===<br /> These projects also include some addressing issues of learner control<br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> *[[Note-Taking_Technologies | Note-taking Project Page (Bauer &amp; Koedinger)]] [Also in Coordinative Learning]<br /> **[[Note-Taking: Restriction and Selection]] (completed)<br /> **[[Note-Taking: Focusing On Concepts]] (planned)<br /> **[[Note-Taking: Focusing On Quantity]] (planned)<br /> *[[Handwriting Algebra Tutor]] (Anthony, Yang &amp; Koedinger) [Also in Coordinative Learning]<br /> **[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) <br /> **[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress)<br /> <br /> ===Other===<br /> <br /> * [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br /> <br /> == Annotated bibliography ==<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=6720 2008-01-10T22:29:53Z

<p>Perfetti: /* Hypotheses */</p> <hr /> <div>= The PSLC Refinement and Fluency cluster =<br /> <br /> == Abstract ==<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> <br /> 1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components. <br /> <br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> <br /> 3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br /> <br /> 4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information (&quot;facts&quot;) or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.<br /> <br /> 5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns. <br /> <br /> 6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.<br /> <br /> 7. [[cue validity]]: In both explicit and implicit instruction, the validity of a cue for a knowledge component affects the learning of that knowledge component. (Cue validity is related to [[feature validity]].)<br /> <br /> 8. [[focusing]]: Instruction that directs (focuses) the learner's attention to valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 9. learning to learn: The acquisition of skills and strategies that can generalize across learning tasks can promote new learning. Examples may be deep analysis, help-seeking, use of advance organizers, and, most generally, meta-cognitive strategies. <br /> <br /> 10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while inhibiting others.<br /> <br /> The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> &lt;br&gt;&lt;center&gt;[[Image:rf-theory.jpg]]&lt;/center&gt;<br /> <br /> ==Significance==<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> == Glossary ==<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> == Research question ==<br /> The overall research question is how can instruction optimally support the acquisition, refinement, and fluent use of complex targeted knowledge, taking into account the learner’s existing knowledge in relation to the knowledge demands of the target domain? In examining this general question, the studies focus on features of the learning situation, including the following: the cognitive demands of targeted knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br /> <br /> == Independent variables ==<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> == Dependent variables ==<br /> The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br /> <br /> == Hypotheses ==<br /> Instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. More specifically, the initial acquisition of knowledge and its refinement benefit from instructional activities that require the learner to encode the relevant knowledge components by attending to [[valid features]] of the learning content. The fluency corollary: <br /> <br /> <br /> Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br /> <br /> 1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> <br /> 2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby &quot;divergent coding systems&quot; here may be the same as &quot;multiple input sources&quot; in co-training.)<br /> <br /> 3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> <br /> 4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> <br /> 5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> <br /> 6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br /> <br /> 7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 8. Learning to learn hypothesis: The acquisition of certain skills in one context support future learning in other contexts. Such skills include problem analysis, help-seeking, or advance organizers. <br /> <br /> 9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> 10. Active learning hypothesis: Even in simple tasks, learning is more robust when the learner actively engages in the learning material.<br /> <br /> == Explanation ==<br /> All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br /> <br /> == Descendents ==<br /> <br /> === Explicit instruction ===<br /> '''A. Explicit vs Implicit.''' These projects typically compare a more explict form of instruction with a more implict form <br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> * [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br /> <br /> '''B. Explicit attention manipulations''' studies typically vary features available to learner<br /> * [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> * [[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu) [Also Coordinative learning]<br /> * [[Learning French gender cues with prototypes]] (Presson, MacWhinney)<br /> <br /> '''C. Explicit instruction: Practice and Scheduling''' Typical studies control practice events and provide feedback<br /> * [[Optimizing the practice schedule]] (Pavlik et al.) [[Applying optimal scheduling of practice in the Chinese Learnlab|1]]<br /> * [[French gender cues | French grammatical gender cue learning]] (Presson, MacWhinney)<br /> * [[Japanese fluency]] (Yoshimura-MacWhinney)<br /> * [[Fostering fluency in second language learning]] (De Jong, Perfetti)<br /> * [[Using learning curves to optimize problem assignment]] (Cen &amp; Koedinger)<br /> <br /> === Knowledge accessibility ===<br /> '''A. Background knowledge''' These projects directly study effects of learners' background knowledge<br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> * [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Also in Interactive Communication]<br /> * [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br /> <br /> '''B. Availability of knowledge during learning'''<br /> * [[Optimizing the practice schedule]] (Pavlik et al.) [[Understanding paired associate transfer effects based on shared stimulus components|2]], [[Applying optimal scheduling of practice in the Chinese Learnlab|1]], [[Understanding encoding inhibition, retrieval inhibition and destructive interference effects of errors during practice|3]]<br /> * [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br /> * [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> * [[Arithmetical fluency project]] (Fiez)<br /> * [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, &amp; Liu)<br /> <br /> === Active processing ===<br /> These projects also include some addressing issues of learner control<br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> *[[Note-Taking_Technologies | Note-taking Project Page (Bauer &amp; Koedinger)]] [Also in Coordinative Learning]<br /> **[[Note-Taking: Restriction and Selection]] (completed)<br /> **[[Note-Taking: Focusing On Concepts]] (planned)<br /> **[[Note-Taking: Focusing On Quantity]] (planned)<br /> *[[Handwriting Algebra Tutor]] (Anthony, Yang &amp; Koedinger) [Also in Coordinative Learning]<br /> **[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) <br /> **[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress)<br /> <br /> ===Other===<br /> <br /> * [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br /> <br /> == Annotated bibliography ==<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=6713 2008-01-10T20:29:50Z

<p>Perfetti: /* Research question */</p> <hr /> <div>= The PSLC Refinement and Fluency cluster =<br /> <br /> == Abstract ==<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> <br /> 1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components. <br /> <br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> <br /> 3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br /> <br /> 4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information (&quot;facts&quot;) or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.<br /> <br /> 5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns. <br /> <br /> 6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.<br /> <br /> 7. [[cue validity]]: In both explicit and implicit instruction, the validity of a cue for a knowledge component affects the learning of that knowledge component. (Cue validity is related to [[feature validity]].)<br /> <br /> 8. [[focusing]]: Instruction that directs (focuses) the learner's attention to valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 9. learning to learn: The acquisition of skills and strategies that can generalize across learning tasks can promote new learning. Examples may be deep analysis, help-seeking, use of advance organizers, and, most generally, meta-cognitive strategies. <br /> <br /> 10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while inhibiting others.<br /> <br /> The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> &lt;br&gt;&lt;center&gt;[[Image:rf-theory.jpg]]&lt;/center&gt;<br /> <br /> ==Significance==<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> == Glossary ==<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> == Research question ==<br /> The overall research question is how can instruction optimally support the acquisition, refinement, and fluent use of complex targeted knowledge, taking into account the learner’s existing knowledge in relation to the knowledge demands of the target domain? In examining this general question, the studies focus on features of the learning situation, including the following: the cognitive demands of targeted knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br /> <br /> == Independent variables ==<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> == Dependent variables ==<br /> The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br /> <br /> == Hypotheses ==<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. A corollary of this hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. <br /> <br /> Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br /> <br /> 1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> <br /> 2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby &quot;divergent coding systems&quot; here may be the same as &quot;multiple input sources&quot; in co-training.)<br /> <br /> 3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> <br /> 4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> <br /> 5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> <br /> 6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br /> <br /> 7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 8. Learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> <br /> 9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> == Explanation ==<br /> All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br /> <br /> == Descendents ==<br /> <br /> === Explicit instruction ===<br /> '''A. Explicit vs Implicit.''' These projects typically compare a more explict form of instruction with a more implict form <br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> * [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br /> <br /> '''B. Explicit attention manipulations''' studies typically vary features available to learner<br /> * [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> * [[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu) [Also Coordinative learning]<br /> * [[Learning French gender cues with prototypes]] (Presson, MacWhinney)<br /> <br /> '''C. Explicit instruction: Practice and Scheduling''' Typical studies control practice events and provide feedback<br /> * [[Optimizing the practice schedule]] (Pavlik et al.) [[Applying optimal scheduling of practice in the Chinese Learnlab|1]]<br /> * [[French gender cues | French grammatical gender cue learning]] (Presson, MacWhinney)<br /> * [[Japanese fluency]] (Yoshimura-MacWhinney)<br /> * [[Fostering fluency in second language learning]] (De Jong, Perfetti)<br /> * [[Using learning curves to optimize problem assignment]] (Cen &amp; Koedinger)<br /> <br /> === Knowledge accessibility ===<br /> '''A. Background knowledge''' These projects directly study effects of learners' background knowledge<br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> * [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Also in Interactive Communication]<br /> * [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br /> <br /> '''B. Availability of knowledge during learning'''<br /> * [[Optimizing the practice schedule]] (Pavlik et al.) [[Understanding paired associate transfer effects based on shared stimulus components|2]], [[Applying optimal scheduling of practice in the Chinese Learnlab|1]], [[Understanding encoding inhibition, retrieval inhibition and destructive interference effects of errors during practice|3]]<br /> * [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br /> * [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> * [[Arithmetical fluency project]] (Fiez)<br /> * [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, &amp; Liu)<br /> <br /> === Active processing ===<br /> These projects also include some addressing issues of learner control<br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> *[[Note-Taking_Technologies | Note-taking Project Page (Bauer &amp; Koedinger)]] [Also in Coordinative Learning]<br /> **[[Note-Taking: Restriction and Selection]] (completed)<br /> **[[Note-Taking: Focusing On Concepts]] (planned)<br /> **[[Note-Taking: Focusing On Quantity]] (planned)<br /> *[[Handwriting Algebra Tutor]] (Anthony, Yang &amp; Koedinger) [Also in Coordinative Learning]<br /> **[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) <br /> **[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress)<br /> <br /> ===Other===<br /> <br /> * [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br /> <br /> == Annotated bibliography ==<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=6712 2008-01-10T20:28:44Z

<p>Perfetti: /* Research question */</p> <hr /> <div>= The PSLC Refinement and Fluency cluster =<br /> <br /> == Abstract ==<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> <br /> 1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components. <br /> <br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> <br /> 3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br /> <br /> 4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information (&quot;facts&quot;) or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.<br /> <br /> 5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns. <br /> <br /> 6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.<br /> <br /> 7. [[cue validity]]: In both explicit and implicit instruction, the validity of a cue for a knowledge component affects the learning of that knowledge component. (Cue validity is related to [[feature validity]].)<br /> <br /> 8. [[focusing]]: Instruction that directs (focuses) the learner's attention to valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 9. learning to learn: The acquisition of skills and strategies that can generalize across learning tasks can promote new learning. Examples may be deep analysis, help-seeking, use of advance organizers, and, most generally, meta-cognitive strategies. <br /> <br /> 10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while inhibiting others.<br /> <br /> The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> &lt;br&gt;&lt;center&gt;[[Image:rf-theory.jpg]]&lt;/center&gt;<br /> <br /> ==Significance==<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> == Glossary ==<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> == Research question ==<br /> The overall research question is how can instruction optimally support the acquisition, refinement, and fluent use of complex targeted knowledge, taking into account the learner’s existing knowledge in relation to the knowledge demands of the target domain? In examining this general question, the studies focus on features of the learning situation including the following: the cognitive demands of targeted knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br /> <br /> == Independent variables ==<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> == Dependent variables ==<br /> The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br /> <br /> == Hypotheses ==<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. A corollary of this hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. <br /> <br /> Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br /> <br /> 1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> <br /> 2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby &quot;divergent coding systems&quot; here may be the same as &quot;multiple input sources&quot; in co-training.)<br /> <br /> 3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> <br /> 4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> <br /> 5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> <br /> 6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br /> <br /> 7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 8. Learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> <br /> 9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> == Explanation ==<br /> All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br /> <br /> == Descendents ==<br /> <br /> === Explicit instruction ===<br /> '''A. Explicit vs Implicit.''' These projects typically compare a more explict form of instruction with a more implict form <br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> * [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br /> <br /> '''B. Explicit attention manipulations''' studies typically vary features available to learner<br /> * [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> * [[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu) [Also Coordinative learning]<br /> * [[Learning French gender cues with prototypes]] (Presson, MacWhinney)<br /> <br /> '''C. Explicit instruction: Practice and Scheduling''' Typical studies control practice events and provide feedback<br /> * [[Optimizing the practice schedule]] (Pavlik et al.) [[Applying optimal scheduling of practice in the Chinese Learnlab|1]]<br /> * [[French gender cues | French grammatical gender cue learning]] (Presson, MacWhinney)<br /> * [[Japanese fluency]] (Yoshimura-MacWhinney)<br /> * [[Fostering fluency in second language learning]] (De Jong, Perfetti)<br /> * [[Using learning curves to optimize problem assignment]] (Cen &amp; Koedinger)<br /> <br /> === Knowledge accessibility ===<br /> '''A. Background knowledge''' These projects directly study effects of learners' background knowledge<br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> * [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Also in Interactive Communication]<br /> * [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br /> <br /> '''B. Availability of knowledge during learning'''<br /> * [[Optimizing the practice schedule]] (Pavlik et al.) [[Understanding paired associate transfer effects based on shared stimulus components|2]], [[Applying optimal scheduling of practice in the Chinese Learnlab|1]], [[Understanding encoding inhibition, retrieval inhibition and destructive interference effects of errors during practice|3]]<br /> * [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br /> * [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> * [[Arithmetical fluency project]] (Fiez)<br /> * [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, &amp; Liu)<br /> <br /> === Active processing ===<br /> These projects also include some addressing issues of learner control<br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> *[[Note-Taking_Technologies | Note-taking Project Page (Bauer &amp; Koedinger)]] [Also in Coordinative Learning]<br /> **[[Note-Taking: Restriction and Selection]] (completed)<br /> **[[Note-Taking: Focusing On Concepts]] (planned)<br /> **[[Note-Taking: Focusing On Quantity]] (planned)<br /> *[[Handwriting Algebra Tutor]] (Anthony, Yang &amp; Koedinger) [Also in Coordinative Learning]<br /> **[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) <br /> **[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress)<br /> <br /> ===Other===<br /> <br /> * [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br /> <br /> == Annotated bibliography ==<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=6399 2007-12-04T18:54:28Z

<p>Perfetti: /* Explicit instruction */</p> <hr /> <div>= The PSLC Refinement and Fluency cluster =<br /> <br /> == Abstract ==<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> <br /> 1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components. <br /> <br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> <br /> 3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br /> <br /> 4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information (&quot;facts&quot;) or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.<br /> <br /> 5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns. <br /> <br /> 6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.<br /> <br /> 7. [[cue validity]]: In both explicit and implicit instruction, the validity of a cue for a knowledge component affects the learning of that knowledge component. (Cue validity is related to [[feature validity]].)<br /> <br /> 8. [[focusing]]: Instruction that directs (focuses) the learner's attention to valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 9. learning to learn: The acquisition of skills and strategies that can generalize across learning tasks can promote new learning. Examples may be deep analysis, help-seeking, use of advance organizers, and, most generally, meta-cognitive strategies. <br /> <br /> 10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while inhibiting others.<br /> <br /> The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> &lt;br&gt;&lt;center&gt;[[Image:rf-theory.jpg]]&lt;/center&gt;<br /> <br /> ==Significance==<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> == Glossary ==<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> == Research question ==<br /> The overall research question is how can instruction optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain? In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the cognitive demands of knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br /> <br /> == Independent variables ==<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> == Dependent variables ==<br /> The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br /> <br /> == Hypotheses ==<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. A corollary of this hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. <br /> <br /> Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br /> <br /> 1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> <br /> 2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby &quot;divergent coding systems&quot; here may be the same as &quot;multiple input sources&quot; in co-training.)<br /> <br /> 3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> <br /> 4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> <br /> 5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> <br /> 6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br /> <br /> 7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 8. Learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> <br /> 9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> == Explanation ==<br /> All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br /> <br /> == Descendents ==<br /> <br /> === Explicit instruction ===<br /> '''A. Explicit vs Implicit.''' These projects typically compare a more explict form of instruction with a more implict form <br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> * [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br /> <br /> '''B. Explicit attention manipulations''' studies typically vary features available to learner<br /> * [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> * [[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu) [Also Coordinative learning]<br /> <br /> '''C. Explicit instruction: Practice and Scheduling''' Typical studies control practice events and provide feedback<br /> * [[Optimizing the practice schedule]] (Pavlik et al.)<br /> * [[French gender cues | French grammatical gender cue learning through optimized practice]] (Presson, MacWhinney)<br /> * [[Learning French gender cues with prototypes]] (Presson, MacWhinney)<br /> * [[Japanese fluency]] (Yoshimura-MacWhinney)<br /> * [[Fostering fluency in second language learning]] (De Jong, Perfetti)<br /> * [[Using learning curves to optimize problem assignment]] (Cen &amp; Koedinger)<br /> <br /> === Knowledge accessibility ===<br /> '''A. Background knowledge''' These projects directly study effects of learners' background knowledge<br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)* [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Also in Interactive Communication]<br /> * [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br /> <br /> '''B. Availability of knowledge during learning'''<br /> * [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br /> * [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> * [[Arithmetical fluency project]] (Fiez)<br /> * [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, &amp; Liu)<br /> <br /> === Active processing ===<br /> These projects also include some addressing issues of learner control<br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> **[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) [Also in Coordinative Learning]<br /> *[[Note-Taking_Technologies | Note-taking Project Page (Bauer &amp; Koedinger)]] [Also in Coordinative Learning]<br /> **[[Note-Taking: Restriction and Selection]] (completed)<br /> **[[Note-Taking: Focusing On Concepts]] (planned)<br /> **[[Note-Taking: Focusing On Quantity]] (planned)<br /> *[[Handwriting Algebra Tutor]] (Anthony, Yang &amp; Koedinger)<br /> **[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress) [Also in Coordinative Learning]<br /> <br /> <br /> ===Other===<br /> <br /> * [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br /> <br /> == Annotated bibliography ==<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=6392 2007-12-04T16:22:11Z

<p>Perfetti: /* Descendents */</p> <hr /> <div>= The PSLC Refinement and Fluency cluster =<br /> <br /> == Abstract ==<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> <br /> 1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components. <br /> <br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> <br /> 3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br /> <br /> 4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information (&quot;facts&quot;) or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.<br /> <br /> 5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns. <br /> <br /> 6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.<br /> <br /> 7. [[cue validity]]: In both explicit and implicit instruction, the validity of a cue for a knowledge component affects the learning of that knowledge component. (Cue validity is related to [[feature validity]].)<br /> <br /> 8. [[focusing]]: Instruction that directs (focuses) the learner's attention to valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 9. learning to learn: The acquisition of skills and strategies that can generalize across learning tasks can promote new learning. Examples may be deep analysis, help-seeking, use of advance organizers, and, most generally, meta-cognitive strategies. <br /> <br /> 10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while inhibiting others.<br /> <br /> The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> &lt;br&gt;&lt;center&gt;[[Image:rf-theory.jpg]]&lt;/center&gt;<br /> <br /> ==Significance==<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> == Glossary ==<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> == Research question ==<br /> The overall research question is how can instruction optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain? In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the cognitive demands of knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br /> <br /> == Independent variables ==<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> == Dependent variables ==<br /> The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br /> <br /> == Hypotheses ==<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. A corollary of this hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. <br /> <br /> Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br /> <br /> 1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> <br /> 2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby &quot;divergent coding systems&quot; here may be the same as &quot;multiple input sources&quot; in co-training.)<br /> <br /> 3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> <br /> 4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> <br /> 5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> <br /> 6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br /> <br /> 7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 8. Learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> <br /> 9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> == Explanation ==<br /> All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br /> <br /> == Descendents ==<br /> <br /> === Explicit instruction ===<br /> '''A. Explicit vs Implicit.''' These projects typically compare a more explict form of instruction with a more implict form <br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> * [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br /> <br /> '''B. Explicit attention manipulations'''<br /> [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> **[[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu) [Also Coordinative learning]<br /> <br /> '''C. Explicit instruction: Practice and Scheduling''' <br /> * [[Optimizing the practice schedule]] (Pavlik et al.)<br /> * [[French gender cues | French Grammatical Gender Cue Learning Through Optimized Practice]] (Presson-MacWhinney)<br /> * [[Japanese fluency]] (Yoshimura-MacWhinney)<br /> * [[Fostering fluency in second language learning]] (De Jong, Perfetti)<br /> * [[Using learning curves to optimize problem assignment]] (Cen &amp; Koedinger)<br /> <br /> === Knowledge accessibility ===<br /> '''A. Background knowledge''' These projects directly study effects of learners' background knowledge<br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)* [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Also in Interactive Communication]<br /> * [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br /> <br /> '''B. Availability of knowledge during learning'''<br /> * [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br /> * [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> * [[Arithmetical fluency project]] (Fiez)<br /> * [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, &amp; Liu)<br /> <br /> === Active processing ===<br /> These projects also include some addressing issues of learner control<br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> **[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) [Also in Coordinative Learning]<br /> *[[Note-Taking_Technologies | Note-taking Project Page (Bauer &amp; Koedinger)]] [Also in Coordinative Learning]<br /> **[[Note-Taking: Restriction and Selection]] (completed)<br /> **[[Note-Taking: Focusing On Concepts]] (planned)<br /> **[[Note-Taking: Focusing On Quantity]] (planned)<br /> *[[Handwriting Algebra Tutor]] (Anthony, Yang &amp; Koedinger)<br /> **[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress) [Also in Coordinative Learning]<br /> <br /> <br /> ===Other===<br /> <br /> * [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br /> <br /> == Annotated bibliography ==<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=6391 2007-12-04T16:17:21Z

<p>Perfetti: /* Descendents */</p> <hr /> <div>= The PSLC Refinement and Fluency cluster =<br /> <br /> == Abstract ==<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> <br /> 1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components. <br /> <br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> <br /> 3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br /> <br /> 4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information (&quot;facts&quot;) or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.<br /> <br /> 5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns. <br /> <br /> 6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.<br /> <br /> 7. [[cue validity]]: In both explicit and implicit instruction, the validity of a cue for a knowledge component affects the learning of that knowledge component. (Cue validity is related to [[feature validity]].)<br /> <br /> 8. [[focusing]]: Instruction that directs (focuses) the learner's attention to valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 9. learning to learn: The acquisition of skills and strategies that can generalize across learning tasks can promote new learning. Examples may be deep analysis, help-seeking, use of advance organizers, and, most generally, meta-cognitive strategies. <br /> <br /> 10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while inhibiting others.<br /> <br /> The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> &lt;br&gt;&lt;center&gt;[[Image:rf-theory.jpg]]&lt;/center&gt;<br /> <br /> ==Significance==<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> == Glossary ==<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> == Research question ==<br /> The overall research question is how can instruction optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain? In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the cognitive demands of knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br /> <br /> == Independent variables ==<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> == Dependent variables ==<br /> The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br /> <br /> == Hypotheses ==<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. A corollary of this hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. <br /> <br /> Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br /> <br /> 1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> <br /> 2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby &quot;divergent coding systems&quot; here may be the same as &quot;multiple input sources&quot; in co-training.)<br /> <br /> 3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> <br /> 4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> <br /> 5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> <br /> 6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br /> <br /> 7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 8. Learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> <br /> 9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> == Explanation ==<br /> All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br /> <br /> == Descendents ==<br /> <br /> === Explicit instruction ===<br /> '''A. Explicit vs Implicit.''' These projects typically compare a more explict form of instruction with a more implict form <br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> * [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br /> <br /> '''B. Explicit attention manipulations'''<br /> [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> **[[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu) [Also Coordinative learning]<br /> <br /> '''C. Explicit instruction: Practice and Scheduling''' <br /> * [[Optimizing the practice schedule]] (Pavlik et al.)<br /> * [[French gender cues | French Grammatical Gender Cue Learning Through Optimized Practice]] (Presson-MacWhinney)<br /> * [[Japanese fluency]] (Yoshimura-MacWhinney)<br /> * [[Fostering fluency in second language learning]] (De Jong, Perfetti)<br /> * [[Using learning curves to optimize problem assignment]] (Cen &amp; Koedinger)<br /> <br /> === Knowledge accessibility ===<br /> These projects include background knowledge &amp; knowledge suppression and often also involve novel knowledge component and cognitive task analysis.<br /> * [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)* [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Also in Interactive Communication]<br /> * [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> * [[Arithmetical fluency project]] (Fiez)<br /> * [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, &amp; Liu)<br /> * [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br /> <br /> === Active processing ===<br /> These projects also include some addressing issues of learner control<br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> **[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) [Also in Coordinative Learning]<br /> *[[Note-Taking_Technologies | Note-taking Project Page (Bauer &amp; Koedinger)]] [Also in Coordinative Learning]<br /> **[[Note-Taking: Restriction and Selection]] (completed)<br /> **[[Note-Taking: Focusing On Concepts]] (planned)<br /> **[[Note-Taking: Focusing On Quantity]] (planned)<br /> *[[Handwriting Algebra Tutor]] (Anthony, Yang &amp; Koedinger)<br /> **[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress) [Also in Coordinative Learning]<br /> <br /> <br /> ===Other===<br /> <br /> * [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br /> <br /> == Annotated bibliography ==<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=6390 2007-12-04T16:16:02Z

<p>Perfetti: /* Descendents */</p> <hr /> <div>= The PSLC Refinement and Fluency cluster =<br /> <br /> == Abstract ==<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> <br /> 1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components. <br /> <br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> <br /> 3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br /> <br /> 4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information (&quot;facts&quot;) or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.<br /> <br /> 5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns. <br /> <br /> 6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.<br /> <br /> 7. [[cue validity]]: In both explicit and implicit instruction, the validity of a cue for a knowledge component affects the learning of that knowledge component. (Cue validity is related to [[feature validity]].)<br /> <br /> 8. [[focusing]]: Instruction that directs (focuses) the learner's attention to valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 9. learning to learn: The acquisition of skills and strategies that can generalize across learning tasks can promote new learning. Examples may be deep analysis, help-seeking, use of advance organizers, and, most generally, meta-cognitive strategies. <br /> <br /> 10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while inhibiting others.<br /> <br /> The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> &lt;br&gt;&lt;center&gt;[[Image:rf-theory.jpg]]&lt;/center&gt;<br /> <br /> ==Significance==<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> == Glossary ==<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> == Research question ==<br /> The overall research question is how can instruction optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain? In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the cognitive demands of knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br /> <br /> == Independent variables ==<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> == Dependent variables ==<br /> The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br /> <br /> == Hypotheses ==<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. A corollary of this hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. <br /> <br /> Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br /> <br /> 1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> <br /> 2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby &quot;divergent coding systems&quot; here may be the same as &quot;multiple input sources&quot; in co-training.)<br /> <br /> 3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> <br /> 4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> <br /> 5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> <br /> 6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br /> <br /> 7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 8. Learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> <br /> 9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> == Explanation ==<br /> All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br /> <br /> == Descendents ==<br /> <br /> === Explicit instruction ===<br /> A. Explicit vs Implicit. These projects typically compare a more explict form of instruction with a more implict form <br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> * [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br /> <br /> B. Explicit attention manipulations<br /> [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> **[[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu) [Also Coordinative learning]<br /> <br /> C. Explicit instruction: Practice and Scheduling <br /> * [[Optimizing the practice schedule]] (Pavlik et al.)<br /> * [[French gender cues | French Grammatical Gender Cue Learning Through Optimized Practice]] (Presson-MacWhinney)<br /> * [[Japanese fluency]] (Yoshimura-MacWhinney)<br /> * [[Fostering fluency in second language learning]] (De Jong, Perfetti)<br /> * [[Using learning curves to optimize problem assignment]] (Cen &amp; Koedinger)<br /> <br /> === Knowledge accessibility ===<br /> These projects include background knowledge &amp; knowledge suppression and often also involve novel knowledge component and cognitive task analysis.<br /> * [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)* [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Also in Interactive Communication]<br /> * [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> * [[Arithmetical fluency project]] (Fiez)<br /> * [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, &amp; Liu)<br /> * [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br /> <br /> === Active processing ===<br /> These projects also include some addressing issues of learner control<br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> **[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) [Also in Coordinative Learning]<br /> *[[Note-Taking_Technologies | Note-taking Project Page (Bauer &amp; Koedinger)]] [Also in Coordinative Learning]<br /> **[[Note-Taking: Restriction and Selection]] (completed)<br /> **[[Note-Taking: Focusing On Concepts]] (planned)<br /> **[[Note-Taking: Focusing On Quantity]] (planned)<br /> *[[Handwriting Algebra Tutor]] (Anthony, Yang &amp; Koedinger)<br /> **[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress) [Also in Coordinative Learning]<br /> <br /> <br /> ===Other===<br /> <br /> * [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br /> <br /> == Annotated bibliography ==<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Mastery&diff=6389 2007-12-04T01:30:04Z

<p>Perfetti: New page: Mastery refers to a level of learning that is high enough that further instruction is not necessary. A learner can show mastery of specific knowledge components or on a set of knowledge co...</p> <hr /> <div>Mastery refers to a level of learning that is high enough that further instruction is not necessary. A learner can show mastery of specific knowledge components or on a set of knowledge components. [[category: glossary]] [[Category:PSLC General]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Fluency_pressure&diff=6388 2007-12-04T01:20:42Z

<p>Perfetti: </p> <hr /> <div>Fluency Pressure is present when an instructional manipulation requires the learner to make responses at a rate that exceeds the learner's current rate. An example comes from de Jong's study of English second language learners, who were given increasingly shorter periods of time in which to produce a short &quot;speech&quot;, an activity that, by hypothesis, will increase the fluency of production. [[category : glossary]] [[category : PSLC General]] [[Category:Refinement and Fluency]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Fluency_pressure&diff=6387 2007-12-04T01:20:14Z

<p>Perfetti: </p> <hr /> <div>Fluency Pressure is present when an instructional manipulation requires the learner to make responses at a rate that exceeds the learner's current rate. An example comes from de Jong's study of English second language learners, who were given increasingly shorter periods of time in which to produce a short &quot;speech&quot;, an activity that, by hypothesis, will increase the fluency of production. [[category : glossary]] [[category : PSLC General]] [[Refinement and Fluency]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Fluency_pressure&diff=6386 2007-12-04T01:19:12Z

<p>Perfetti: </p> <hr /> <div>Fluency Pressure is present when an instructional manipulation requires the learner to make responses at a rate that exceeds the learner's current rate. An example comes from de Jong's study of English second language learners, who were given increasingly shorter periods of time in which to produce a short &quot;speech&quot;, an activity that, by hypothesis, will increase the fluency of production. [[category : glossary]] [[category : PSLC General]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Fluency_pressure&diff=6385 2007-12-04T01:14:17Z

<p>Perfetti: </p> <hr /> <div>Fluency Pressure is present when an instructional manipulation requires the learner to make responses at a rate that exceeds the learner's current rate. An example comes from de Jong's study of English second language learners, who were given increasingly shorter periods of time in which to produce a short &quot;speech&quot;, an activity that, by hypothesis, will increase the fluency of production. [[category : glossary]] [[category : PSLC general]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Fluency_pressure&diff=6384 2007-12-04T01:11:45Z

<p>Perfetti: </p> <hr /> <div>Fluency Pressure is present when an instructional manipulation requires the learner to make responses at a rate that exceeds the learner's current rate. An example comes from de Jong's study of English second language learners, who were given increasingly shorter periods of time in which to produce a short &quot;speech&quot;, an activity that, by hypothesis, will increase the fluency of production. [[category : glossary]] [[category : refinement and fluency]] [[category : PSLC general]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Fluency_pressure&diff=6383 2007-12-04T01:10:15Z

<p>Perfetti: New page: Fluency Pressure is present when an instructional manipulation requires the learner to make responses at a rate that exceeds the learner&#039;s current rate. An example comes from de Jong&#039;s stu...</p> <hr /> <div>Fluency Pressure is present when an instructional manipulation requires the learner to make responses at a rate that exceeds the learner's current rate. An example comes from de Jong's study of English second language learners, who were given increasingly shorter periods of time in which to produce a short &quot;speech&quot;, an activity that, by hypothesis, will increase the fluency of production. [[category : glossary]] [[category : PSLC general]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Fostering_fluency_in_second_language_learning&diff=6382 2007-12-04T01:07:17Z

<p>Perfetti: </p> <hr /> <div>{| class=&quot;wikitable&quot; border=&quot;1&quot; style=&quot;margin: 2em auto 2em auto&quot;<br /> |-<br /> ! Project title<br /> | Fostering fluency in second language learning:<br /> Testing two types of instruction<br /> |- <br /> ! PI<br /> | De Jong (postdoc)<br /> |-<br /> ! Co-PI<br /> | Perfetti (faculty)<br /> |-<br /> ! Others with &gt; 160 hours<br /> | Claire Siskin<br /> |-<br /> ! Study start dates<br /> | September 2006, January 2007, February 2007<br /> |-<br /> ! Study end dates<br /> | November 2006, February 2007, March 2007<br /> |-<br /> ! Learnlab<br /> | [[ESL]]<br /> |-<br /> ! Number of participants<br /> | 120<br /> |-<br /> ! Total Participant Hours<br /> | 320 hours<br /> |-<br /> ! Datashop?<br /> | Expected date 8/15<br /> |}<br /> <br /> <br /> == Abstract==<br /> Many studies have investigated the effect of exposure to language on [[fluency]]. It has been established, for instance, that [[fluency]] increases after a period of immersion or study abroad (Freed et al., 2004; Segalowitz &amp; Freed, 2004). Nevertheless, only very few types of instruction have been designed to increase oral [[fluency]], and even fewer have been tested.<br /> <br /> One such type of instruction is Nation’s 4/3/2 procedure, in which learners prepare a four-minute talk and repeat it twice to different partners, first in three minutes, then in two minutes (Nation, 1989). He found that the number of hesitations decreased in the retellings, and that sentences were more complex. We may characterize such an outcome as resulting from the [[fluency pressure]] exerted by the 4/3/2 procedure. It was not investigated, however, whether this transferred to new speeches, which is what we will do in this project. Another task that may increase [[fluency]] is shadowing, in which student talk (and read) along with a recording of a short speech by a native speaker. Shadowing should increase the feature strength of formulaic sequences, resulting in faster access to them in subsequent production tasks. Native-like locations of pauses may also be acquired.<br /> <br /> In Study 1 we will investigate what characteristics of [[fluency]] are affected by the 4/3/2 procedure. Measures include the number of syllables per second (speech rate); mean length of fluent runs between pauses; phonation/time ratio; number of interphrasal and intraphrasal pauses; morphosyntactic accuracy; and number of embedded clauses (syntactic complexity). The posttest will test transfer to a different topic. In Study 2 we will investigate whether a pretraining of formulaic sequences further enhances [[fluency]] (e.g., ''the point is that'', ''what I’m saying is that'', and ''and so on''). If students can use these sequences fast and effortlessly, this frees up [[cognitive headroom|headroom]] which can then be used to construct sentences. The effect will be that there will be fewer and shorter pauses, and/or that sentences will be more complex. In Study 3, it will be investigated whether shadowing leads to increased use of formulaic sequences ([[chunking]]) and native-like pauses in subsequent production tasks.<br /> <br /> == Glossary ==<br /> <br /> ; 4/3/2 procedure: A teaching method in which students talk about a topic for four minutes. Then they repeat their speech in three minutes, and again in 2 minutes.<br /> ; Shadowing: Repeating speech while it is being spoken.<br /> ; Formulaic sequence: A sequence, continuous or discontinuous, of words or other elements, which is, or appears to be, prefabricated (see Wray, 2002, p. 9), e.g., ''The point is that'', ''What I'm trying to say is that'', and ''Take something like''.<br /> ; Articulation rate: Number of syllables per second<br /> ; Phonation/time ratio: The percentage of time spent speaking as a percentage proportion to the time taken to produce the speech sample<br /> ; Morphosyntactic accuracy: In this study we will investigate subject-verb agreement, tense errors, definite/indefinite articles<br /> ; Syntactic complexity: In this study we will investigate the number of embedded finite and non-finite clauses<br /> <br /> == Research questions ==<br /> <br /> * Study 1:<br /> ** a. What characteristics of [[fluency]] are affected by repetition of a short speech under increasing time pressure (the 4/3/2 procedure)?<br /> ** b. Does knowledge [[refinement]] take place during the 4/3/2 training, in terms of morphosyntactic accuracy and syntactic complexity?<br /> * Study 2:<br /> ** a. Does pretraining of formulaic sequences lead to an increase in their use in the subsequent 4/3/2 procedure and posttest? If so, does this increase overall [[fluency]]?<br /> ** b. Does proficiency level affect [[fluency]] development during the 4/3/2 procedure?<br /> * Study 3:<br /> ** a. What characteristics of [[fluency]] are affected by shadowing a text with formulaic sequences and a pausing pattern characteristic of spontaneous speech?<br /> ** b. Does shadowing texts with formulaic sequences lead to an increase in their use in the posttest? If so, does this increase overall [[fluency]]?<br /> <br /> For studies 2 and 3, questionnaire data will be collected about the students' contact with the second language (English) and their first language, in terms of ''types of contact'' (e.g., listening to the radio, talking to friends, talking to strangers) and ''amount of contact'' (number of days per week, number of hours per day). We will explore whether these [[individual differences]] affect pretest performance and fluency development.<br /> <br /> == Background and significance ==<br /> <br /> Many studies in the field of second language acquisition that have studied [[fluency]] have investigated the effect of study abroad, immersion and regular classroom practice on [[fluency]] (Freed, Segalowitz, and Dewey, 2004; Segalowitz &amp; Freed, 2004). Very few studies, however, have investigated specific activities that lead to [[fluency]], which can be done in classrooms. Two such activities are tested in this project.<br /> <br /> The first activity that is tested is the 4/3/2 procedure as proposed by Nation (1989). He investigated the development of [[fluency]] during this task, but used a limited number of measures and did not test the long-term effect: he only analyzed [[fluency]] during the task itself, not during the following weeks. This project will test the long-term effect and will include more measures, such as length and location of pauses. An attempt will be made to link these measures to cognitive mechanisms.<br /> <br /> Whereas Study 1 focuses on a general effect of the 4/3/2 procedure on [[fluency]] development, Studies 2 and 3 focus on specific aspects of the training. Study 2 investigates how a pretraining of a set of formulaic sequences affects performance during and after the 4/3/2 procedure. Study 3 investigates whether the presence of the same set of formulaic sequences leads to increased use of those sequences in later speaking tasks, and whether such an increase affects [[fluency]] measures.<br /> <br /> == Dependent variables ==<br /> * Temporal measures of [[fluency]]:<br /> ** Articulation rate: number of syllables per second<br /> ** Pauses:<br /> ***mean length of fluent runs between pauses<br /> ***mean length of pauses<br /> ***phonation/time ratio<br /> ***number of interphrasal and intraphrasal pauses<br /> ** Formulaic sequences: number of appropriate formulaic sequences repeated from training<br /> * Accuracy: morphosyntactic accuracy (subject-verb agreement, tense errors, definite/indefinite articles; see Mizera, 2006: 71)<br /> * Complexity: number of embedded finite and non-finite clauses (cf. Nation, 1989)<br /> <br /> * ''Near transfer, immediate, [[normal post-test]]'': after completing the last training session, students performed a similar task (spontaneous speech about a given topic), to test whether any gains in [[fluency]] during the training task were maintained in a new instance of the same task. This test was given one week and four weeks after the last training session, each time with a different topic. These recordings were made as part of the Recorded Speaking Activities (RSAs) from the project &quot;[[The self-correction of speech errors (McCormick, O’Neill &amp; Siskin)]]&quot;.<br /> <br /> == Independent variables ==<br /> * Studies 1-3: Pretest vs. immediate posttest vs. [[long-term retention]] posttest<br /> * Study 1: Repetition vs. No Repetition<br /> :: In the Repetition condition students talk about one topic three times. In the No Repetition condition, students talk about three different topics.<br /> * Study 2:<br /> ** a. Pretraining vs. no pretraining of formulaic sequences<br /> ::In the Formulaic Sequences condition, students receive a short training of a number of formulaic sequences before they start the [[fluency]] training (4/3/2 task). In the No Formulaic Sequences condition, students do not receive this pretraining, and only do the 4/3/2 task.<br /> :* b. Low intermediate vs. high intermediate proficiency level<br /> :: Low intermediate students are enrolled in ELI Speaking courses at level 3, high intermediate at level 4.<br /> * Study 3: Shadowing text with formulaic sequences vs. without formulaic sequences<br /> ::In the Formulaic Sequences condition, students shadow texts that contain formulaic sequences. In the No Formulaic Sequences condition, students shadow the same texts, from which the formulaic sequences that are being studied have been removed.<br /> <br /> == Hypotheses ==<br /> * Study 1: It is hypothesized that repetition of a short speech (independent variable) under increasing time pressure increases articulation rate and sentence complexity (dependent variables), and decreases the number and length of pauses (dependent variables). The reason is that repetition will--temporarily--increase the [[availability]] of vocabulary and sentence structures (leading to increase speech rate, short and fewer pauses), leaving more [[cognitive headroom|headroom]] for other processes (higher accuracy and syntactic complexity).<br /> * Study 2: It is hypothesized that the presence of a pretraining of formulaic sequences (independent variable) leads to an increase in their use in subsequent spontaneous speech (dependent variable). Effortless use of these sequences will free up [[cognitive headroom|headroom]] for sentence structure planning, which may lead to overall more fluent performance, in terms of speed and pausing patterns (dependent variables). Thus, the training of formulaic sequences may accelerate [[accelerated future learning|future learning]].<br /> * Study 2: Students at different proficiency levels may benefit in different ways from the 4/3/2 training. At lower proficiency levels, repetition may facilitate the use of particular words and grammar, leading to more instances of correct usage of vocabulary, morphosyntax and syntax. At higher proficiency levels, on the other hand, repetition may lead to a greater number of reformulations resulting in higher complexity.<br /> * Study 3: It is hypothesized that shadowing a speech that contains formulaic sequences (independent variable) leads to an increase in their use in subsequent spontaneous speech (dependent variable). Since effortless use of these sequences will free up [[cognitive headroom|headroom]] for sentence structure planning, performance may become more fluent overall, in terms of speed and pausing patterns (dependent variables). Thus, shadowing may accelerate [[accelerated future learning|future learning]]. In addition, shadowing a text with target-language pausing patterns is expected to lead to a more native-like pausing pattern in subsequent spontaneous speech, mainly in terms of position (dependent variables: interphrasal and intraphrasal pauses).<br /> <br /> * ''Near transfer, immediate'': In all studies, a posttest is administered about a week after the last training session. This will be a similar task—a 2-minute monologue—with new content—a new topic.<br /> * ''Near transfer, retention'': In Studies 1 and 2, another posttest is administered two to three weeks after the immediate posttest (three to four weeks after the last training session). Again, this will be a similar task—a 2-minute monologue—with new content—a new topic.<br /> * ''[[accelerated future learning|Acceleration of future learning]]'': In Study 2, the students in the experimental condition first receive a pretraining of a number of formulaic sequences. It will be tested whether their [[fluency]], accuracy and syntactic complexity increases more during subsequent training, than of students who do not receive this pre-training.<br /> <br /> == Findings ==<br /> Data collection for Study 1 was completed in November, 2006. Data are currently being collected for Studies 2 and 3 (Spring, 2007).<br /> <br /> Preliminary results of Study 1 show that, on the immediate posttest, students in the Repetition condition are able to produce the same length of fluent runs with shorter pauses. Also, they fill relatively more time with speech (increased phonation/time ratio). It seems, therefore, that they speak more fluently than students in the No Repetition condition. However, on the delayed posttest, the No Repetition condition seems to have caught up with the Repetition condition, also having shorter pause lengths, with stable lengths of fluent runs.<br /> <br /> Both groups reach a higher articulation rate, measured in syllables per minutes, on the delayed posttest. This may have bee due to their continued Speaking classes in the English Language Institute, and may not have been related to this study.<br /> <br /> It should be noted that the posttests were administered one and four weeks after the last session of the [[fluency]] training, and involved a new topic, which the students had not talked about during the 4/3/2 training.<br /> <br /> {|+ Preliminary results Study 1<br /> ! &amp;nbsp; !! align=&quot;center&quot; colspan=&quot;3&quot; | No Repetition (n=10) !! colspan=&quot;3&quot; | Repetition (n=9)<br /> |-<br /> ! align=&quot;center&quot; | &amp;nbsp; !! Pretest !! Immediate !! Delayed !! Pretest !! Immediate !! Delayed<br /> |-<br /> ! align=&quot;center&quot; | &amp;nbsp; !! &amp;nbsp; !! Posttest !! Posttest !! &amp;nbsp; !! Posttest !! Posttest2<br /> |-<br /> ! align=&quot;left&quot; | Length of fluent runs (in syllables)<br /> | align=&quot;center&quot; | 4.26 || align=&quot;center&quot; | 4.05 || align=&quot;center&quot; | 4.26 || align=&quot;center&quot; | 4.26 || align=&quot;center&quot; | 4.97 || align=&quot;center&quot; | 4.75<br /> |-<br /> ! align=&quot;left&quot; | Pause length (in sec.) *<br /> | align=&quot;center&quot; | 1.12 || align=&quot;center&quot; | 1.11 || align=&quot;center&quot; | '''.99''' || align=&quot;center&quot; | 1.19 || align=&quot;center&quot; | '''0.95''' || align=&quot;center&quot; | 1.01<br /> |-<br /> ! align=&quot;left&quot; | Phonation/time ratio *<br /> | align=&quot;center&quot; | 0.57 || align=&quot;center&quot; | 0.55 || align=&quot;center&quot; | 0.56 || align=&quot;center&quot; | 0.56 || align=&quot;center&quot; | '''0.62''' || align=&quot;center&quot; | 0.60<br /> |-<br /> ! align=&quot;left&quot; | Syllables per minute<br /> | align=&quot;center&quot; | 197 || align=&quot;center&quot; | 194 || align=&quot;center&quot; | '''204''' || align=&quot;center&quot; | 192 || align=&quot;center&quot; | 191 || align=&quot;center&quot; | '''199'''<br /> |}<br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Significant interaction Condition x Time<br /> <br /> == Explanation ==<br /> This project is part of the Refinement and Fluency cluster. The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The general hypothesis is that the structure of instructional activities affects learning.<br /> This project addresses the core issues of task analysis, [[fluency]] from basics, [[in vivo experiment|in vivo]] evaluation, and scheduling of practice. The 4/3/2 task has been analysed into its components. In Study 1, the effect of the component of repetition is investigated. Practice with the basic skills of using vocabulary and grammar is expected to increase [[fluency]]. This will be the case in the Repetition condition, where students have the opportunity to re-use the words, formulaic sequences and grammar in subsequent recordings. In Study 2, students are encouraged to use formulaic sequences that have been taught before training. In Study 3 it is investigated whether shadowing promotes the use of formulaic sequences in spontaneous speech. All three studies take place in an [[in vivo experiment|in vivo]] setting.<br /> <br /> == Further information ==<br /> <br /> For a summer intern project in June and July 2007, Kara Schultz did a multiple case study of six students from Study 1. The project was a first step towards more in-depth analyses of the data of all three studies in the ESL fluency project, addressing the following research questions:<br /> * Does the absence of the need to generate new semantic content in the two retellings during the 4/3/2 task free up headroom, resulting in changes in fluency, morphosyntactic accuracy, and complexity?<br /> * If so, what types of changes occur, and what are the causes for these changes?<br /> * Is there long-term retention of the changes (one week)?<br /> <br /> In June 2007 the executive committee approved our letter of intent, in which we proposed follow-up studies that investigate the effect of time pressure and the role of specific knowledge components (vocabulary, grammar) in oral fluency. We will submit a full project plan on August 1, 2007.<br /> <br /> <br /> '''Peer-reviewed presentations'''<br /> <br /> De Jong, N., McCormick, D., O'Neill, C., and Bradin Siskin, C., ''Self-correction and fluency in ESL speaking development''. Paper presented at the American Association for Applied Linguistics 2007 Conference in Costa Mesa, California, April 2007.<br /> <br /> <br /> '''Other presentations'''<br /> <br /> Presentation of the software component at the ''Multimedia Showcase'' sponsored by the Robert Henderson Media Center at the University of Pittsburgh, September 2006<br /> <br /> == Descendants ==<br /> [[Fluency Summer Intern Project]]<br /> <br /> == Annotated bibliography ==<br /> <br /> Nation, I.S.P. (1989). Improving speaking fluency. ''System'', ''17'', 377-384.<br /> <br /> Freed, B. F., Dewey, D. P., Segalowitz, N., &amp; Halter, R. (2004). The language contact profile. ''Studes in Second Language Acquisition'', ''26'', 349-356.<br /> <br /> Freed, B. F., Segalowitz, N., &amp; Dewey, D. P. (2004). Context of learning and second language fluency in French: Comparing regular classroom, study abroad, and intensive domestic immersion programs. ''Studies in Second Language Acquisition'', ''26'', 275-301.<br /> <br /> Mizera, G. J. (2006). ''Working memory and L2 oral fluency''. Unpublished doctoral dissertation. University of Pittsburgh, Pittsburgh.<br /> <br /> Segalowitz, N., &amp; Freed, B. F. (2004). Context, contact, and cognition in oral fluency acquisition. ''Studies in Second Language Acquisition'', ''26'', 173-199.</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Fluency_Pressure&diff=6381 2007-12-04T01:01:13Z

<p>Perfetti: </p> <hr /> <div>Fluency Pressure is present when an instructional manipulation requires the learner to make responses at a rate that exceeds the learner's current rate. An example comes from de Jong's study of English second language learners, who were given increasingly shorter periods of time in which to produce a short &quot;speech&quot;, an activity that, by hypothesis, will increase the fluency of production.<br /> [[catgegory: glossary]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Fluency_Pressure&diff=6380 2007-12-04T00:58:29Z

<p>Perfetti: </p> <hr /> <div>Fluency Pressure is present when an instructional manipulation requires the learner to make responses at a rate that exceeds the learner's current rate. An example comes from de Jong's study of English second language learners, who were given increasingly shorter periods of time in which to produce a short &quot;speech&quot;, an activity that, by hypothesis, will increase the fluency of production.[[catgegory:glossary/refinement and fluency]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Fluency_Pressure&diff=6379 2007-12-04T00:12:47Z

<p>Perfetti: </p> <hr /> <div>Fluency Pressure is present when an instructional manipulation requires the learner to make responses at a rate that exceeds the learner's current rate. An example comes from de Jong's study of English second language learners, who were given increasingly shorter periods of time in which to produce a short &quot;speech&quot;, an activity that, by hypothesis, will increase the fluency of production.[[catgegory:glossary]] [[refinement and fluency]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Fluency_Pressure&diff=6378 2007-12-04T00:09:25Z

<p>Perfetti: </p> <hr /> <div>Fluency Pressure is present when an instructional manipulation requires the learner to make responses at a rate that exceeds the learner's current rate. An example comes from de Jong's study of English second language learners, who were given increasingly shorter periods of time in which to produce a short &quot;speech&quot;, an activity that, by hypothesis, will increase the fluency of production.[[catgegory:glossary]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=6377 2007-12-04T00:03:38Z

<p>Perfetti: </p> <hr /> <div>= The PSLC Refinement and Fluency cluster =<br /> <br /> == Abstract ==<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> <br /> 1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components. <br /> <br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> <br /> 3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br /> <br /> 4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information (&quot;facts&quot;) or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.<br /> <br /> 5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns. <br /> <br /> 6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.<br /> <br /> 7. [[cue validity]]: In both explicit and implicit instruction, the validity of a cue for a knowledge component affects the learning of that knowledge component. (Cue validity is related to [[feature validity]].)<br /> <br /> 8. [[focusing]]: Instruction that directs (focuses) the learner's attention to valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 9. learning to learn: The acquisition of skills and strategies that can generalize across learning tasks can promote new learning. Examples may be deep analysis, help-seeking, use of advance organizers, and, most generally, meta-cognitive strategies. <br /> <br /> 10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while inhibiting others.<br /> <br /> The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> &lt;br&gt;&lt;center&gt;[[Image:rf-theory.jpg]]&lt;/center&gt;<br /> <br /> ==Significance==<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> == Glossary ==<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> == Research question ==<br /> The overall research question is how can instruction optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain? In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the cognitive demands of knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br /> <br /> == Independent variables ==<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> == Dependent variables ==<br /> The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br /> <br /> == Hypotheses ==<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. A corollary of this hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. <br /> <br /> Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br /> <br /> 1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> <br /> 2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby &quot;divergent coding systems&quot; here may be the same as &quot;multiple input sources&quot; in co-training.)<br /> <br /> 3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> <br /> 4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> <br /> 5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> <br /> 6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br /> <br /> 7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 8. Learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> <br /> 9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> == Explanation ==<br /> All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br /> <br /> == Descendents ==<br /> <br /> === Explicit instruction ===<br /> These projects often involve manipulations of attention &amp; discrimination. <br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> * [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> *[[Handwriting Algebra Tutor]] (Anthony, Yang &amp; Koedinger)<br /> **[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) [Also in Coordinative Learning]<br /> **[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress) [Also in Coordinative Learning]<br /> *[[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu)<br /> <br /> === Knowledge accessibility ===<br /> These projects include background knowledge &amp; knowledge suppression and often also involve novel knowledge component and cognitive task analysis.<br /> * [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br /> * [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Also in Interactive Communication]<br /> * [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> * [[Arithmetical fluency project]] (Fiez)<br /> * [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, &amp; Liu)<br /> * [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br /> <br /> === Active processing ===<br /> These projects also include some addressing issues of learner control<br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> *[[Note-Taking_Technologies | Note-taking Project Page (Bauer &amp; Koedinger)]] [Also in Coordinative Learning]<br /> **[[Note-Taking: Restriction and Selection]] (completed)<br /> **[[Note-Taking: Focusing On Concepts]] (planned)<br /> **[[Note-Taking: Focusing On Quantity]] (planned)<br /> <br /> === Optimal scheduling &amp; fluency pressure ===<br /> * [[Optimizing the practice schedule]] (Pavlik et al.)<br /> * [[French gender cues | French Grammatical Gender Cue Learning Through Optimized Practice]] (Presson-MacWhinney)<br /> * [[Japanese fluency]] (Yoshimura-MacWhinney)<br /> * [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br /> * [[Fostering fluency in second language learning]] (De Jong, Perfetti)<br /> * [[Using learning curves to optimize problem assignment]] (Cen &amp; Koedinger)<br /> <br /> ===Other===<br /> * [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br /> <br /> == Annotated bibliography ==<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Instructional_Principles_and_Hypotheses&diff=6376 2007-12-03T22:32:54Z

<p>Perfetti: </p> <hr /> <div>===Creating Instructional Principle and Hypothesis Pages===<br /> The PSLC is starting to maintain a collection of instructional principle pages. Each instructional principle page should be structured with the following headers:<br /> <br /> #Brief statement of principle<br /> #Description of principle<br /> ##Operational definition<br /> ##Examples<br /> #Experimental support<br /> ##Level of support (either low, medium, or high) (See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ &quot;Organizing Instruction and Study to Improve Student Learning&quot;] for definitions of levels of support.)<br /> ##Laboratory experiment support<br /> ##In vivo experiment support<br /> #Theoretical rationale (these entries should link to one or more [[:Category:Learning Processes|learning processes]])<br /> #Conditions of application<br /> #Caveats, limitations, open issues, or dissenting views<br /> #Variations (descendants)<br /> #Generalizations (ascendants)<br /> #References<br /> <br /> If you have a study page, your hypothesis section should make reference to at least one of these instructional principle pages. You should edit your hypothesis section to be sure it points to an instructional principle page. Then you should edit that instructional principle page so that it 1) at least has the structure above (even if all sections aren't filled in) and 2) fill in or edit sections so they are consistent with your views. A template you can copy is provided further below. <br /> <br /> We want to keep the number of principles down, at least at the highest level if generalization, so try to reference the most general instructional principle that is appropriate. In addition to facilitating our goal of greater shared vocabulary and unification, doing so will also make it so you have less editing work to do! By pointing to more general instructional principles, others will be contributing to structuring and filling in that page in addition to you. You may also point to (from your hypothesis section) more specific instructional principle pages relevant to your study.<br /> <br /> Be sure that the *Examples* and *Experimental Support* sections of the instructional principle page you point to also points back to your study page.<br /> <br /> Please also add references to the literature outside of PSLC to the *Reference* section of instructional principles pages you edit. You might simply copy these from your study page's reference section and/or papers your write. By doing so, you can help others (and others can help you) identify relevant research in the field.<br /> <br /> ===List of Instructional Principles and Hypotheses===<br /> <br /> From [[:Category:Independent Variables]]<br /> <br /> ==== Cross-cutting all 3 clusters ====<br /> <br /> * [[Tutoring feedback]] <br /> ** [[Peer tutoring]]<br /> ** [[Prompted Self-explanation]]<br /> <br /> ==== [[Coordinative Learning]] ====<br /> <br /> * [[Example-rule coordination]] - (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ &quot;Organizing Instruction and Study to Improve Student Learning&quot;] as a great source for relevant references. See particularly the recommendations on interleaving worked examples and multimedia (written primarily by Ken Koedinger).) <br /> ** [[Worked example principle]].&amp;nbsp; See also [[Worked examples]] and [[Learning by worked-out examples]].<br /> ** [[Self-explanation]] - listed as &quot;prompted self-explanation&quot; in matrices, which is a better name for an instructional method/principle (something an instructor does or an instructional designer creates) whereas &quot;self-explanation&quot; is a better name for a learning process (something a student's mind does) <br /> *** [[Corrective self-explanation]]<br /> * [[Visual-verbal integration]] <br /> ** [[Contiguous Representation]]<br /> * [[Feature focusing]]<br /> <br /> ====[[Interactive Communication]]====<br /> *[[Collaboration]]<br /> **[[Peer tutoring]]<br /> *[[Collaboration scripts]]<br /> *[[Collaboratively observe]]<br /> **[[Vicarious learning]]<br /> *[[Deep/Reflection questions]] - term in matrix (to the left) is not the same as glossary entry, [[Deep-level question]]. (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ &quot;Organizing Instruction and Study to Improve Student Learning&quot;] as a great source for relevant references. See particularly the &quot;deep questioning&quot; recommendation (written primarily by Art Graesser).)<br /> **[[Reflection questions]]<br /> ***[[Post-practice reflection]]<br /> **[[Knowledge Construction Dialogues]]<br /> **[[Self-explanation]] - listed as &quot;prompted self-explanation&quot; in matrices (see above)<br /> ***[[Elaborated Explanations]] - should this be a learning process (something a student does) rather than an instructional method (something instruction does)? &quot;Prompting for X&quot; can make a learning process into an instructional method (whether the method works or not is a separate question).<br /> ***[[Jointly constructed explanation]] - also perhaps a learning process? <br /> *[[Instructional explanation]]<br /> <br /> ====[[Refinement and Fluency]]====<br /> *[[Error correction support]] <br /> *[[Explicit instruction]]<br /> *[[Fluency Pressure]]<br /> *[[Feedback Timing]] in matrix, but not in glossary. <br /> *[[Feature focusing]]<br /> *[[Knowledge Accessibility]] in matrix, but not in glossary. See [[Accessibility]]<br /> *[[Optimized scheduling]] in glossary, but not listed as independent variable - (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ &quot;Organizing Instruction and Study to Improve Student Learning&quot;] as a great source for relevant references. See particularly the spacing recommendation (written primarily by Hal Pashler). However, recent work by Pavlik qualifies conclusions of Pashler because most (if not all) of the research referenced does not control for time on task.)<br /> ** See also [[Learning event scheduling]] and [[Instructional schedule]]<br /> <br /> ====Unclassified====<br /> [[Assistance]]<br /> [[Availability]]<br /> [[Fading]]<br /> [[Implicit instruction]]<br /> [[Instructional method]]<br /> [[Scaffolding]]<br /> <br /> ===Template===<br /> You can copy the following into an instructional principle page you want to edit and then insert existing text into appropriate sections and add text in other sections.<br /> <br /> &lt;pre&gt;<br /> ==Brief statement of principle==<br /> ==Description of principle==<br /> ===Operational definition===<br /> ===Examples===<br /> ==Experimental support==<br /> ===Laboratory experiment support===<br /> ===In vivo experiment support===<br /> ==Theoretical rationale== <br /> (These entries should link to one or more [[:Category:Learning Processes|learning processes]].)<br /> ==Conditions of application==<br /> ==Caveats, limitations, open issues, or dissenting views==<br /> ==Variations (descendants)==<br /> ==Generalizations (ascendants)==<br /> ==References==<br /> &lt;/pre&gt;<br /> <br /> ===A (temporary!) note on editing instructional principles and hypotheses pages===<br /> When you edit one of the existing &quot;instructional principle&quot; or &quot;independent variable&quot; pages listed above, note that many of the current (as of October, 2007) pages describe an instructional method and need to be appropriately modified to describe an instructional principle. An instructional principle entry should state a hypothesis about how a target instructional method is better than some other baseline or control method. For example, Mayer's multimedia principle states that using diagrams in text (one instructional method) leads to better learning than text along (another instructional method) under certain circumstances. <br /> <br /> Instructional principles are like (or may fill) the *hypothesis* section of study pages, though the hypothesis of a study may be more study or domain specific whereas the associated instructional principle will be study-neutral and likely more domain general. The current (as of October, 2007) instructional method pages are like (or may fill) the *independent variables* section of the study pages. <br /> <br /> Perhaps we need both types of pages (instructional principles and instructional methods), but on other hand, this may lead to just too many pages.<br /> <br /> ===Learning Processes===<br /> <br /> Here's a (probably incomplete) list of learning processes with entries in the glossary. These should be used in the &quot;theoretical rationale&quot; section of instructional principles pages.<br /> <br /> [[Co-training]], [[Cognitive headroom]], [[Integration]], [[Refinement]], [[Sense making]], [[self-explanation]]<br /> <br /> A potentially different list of learning processes can be found at [[:Category:Learning Processes]].</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Fluency_Pressure&diff=6375 2007-12-03T22:31:24Z

<p>Perfetti: New page: Fluency Pressure is present when an instructional manipulation requires the learner to make responses at a rate that exceeds the learner&#039;s current rate. An example comes from de Jong&#039;s stu...</p> <hr /> <div>Fluency Pressure is present when an instructional manipulation requires the learner to make responses at a rate that exceeds the learner's current rate. An example comes from de Jong's study of English second language learners, who were given increasingly shorter periods of time in which to produce a short &quot;speech&quot;, an activity that, by hypothesis, will increase the fluency of production.</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Peer_tutoring&diff=6368 2007-12-03T16:27:01Z

<p>Perfetti: </p> <hr /> <div>Peer tutoring is an approach to [[collaboration|collaborative learning]] in which students tutor each other. Sometimes students are supported by a [[collaboration scripts|collaboration script]] that helps to guide the process of peer collaboration.<br /> <br /> <br /> [[Category:Glossary]]<br /> [[Category:Independent Variables]]<br /> [[Category:Scripted Collaborative Problem Solving]]<br /> [[Category:Interactive Communication]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Active_Processing&diff=6367 2007-12-03T16:24:57Z

<p>Perfetti: </p> <hr /> <div>Active processing refers to sets of procedures in which a learner acts on instructional inputs to generate, re-organize, self-explain, or otherwise goes beyond the encoding of presented material. Active processing in learning or testing may result in more learning. See the [[testing effect]].<br /> <br /> [[Category:Glossary]]<br /> [[Category:Learning Processes]]<br /> [[Category:PSLC General]]<br /> [[Category:Refinement and Fluency]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Active_Processing&diff=6365 2007-12-03T16:20:35Z

<p>Perfetti: </p> <hr /> <div>Active processing refers to sets of procedures in which a learner acts on instructional inputs to generate, re-organize, self-explain, or otherwise go beyond the minimal encoding and rehearsal of presented material. Active processing in learning or testing may result in more learning. See the [[testing effect]].<br /> <br /> [[Category:Glossary]]<br /> [[Category:Learning Processes]]<br /> [[Category:PSLC General]]<br /> [[Category:Refinement and Fluency]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=6364 2007-12-03T16:07:16Z

<p>Perfetti: /* Research question */</p> <hr /> <div>= The PSLC Refinement and Fluency cluster =<br /> <br /> == Abstract ==<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> <br /> 1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components. <br /> <br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> <br /> 3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br /> <br /> 4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information (&quot;facts&quot;) or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.<br /> <br /> 5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns. <br /> <br /> 6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.<br /> <br /> 7. [[cue validity]]: In both explicit and implicit instruction, the validity of a cue for a knowledge component affects the learning of that knowledge component. (Cue validity is related to [[feature validity]].)<br /> <br /> 8. [[focusing]]: Instruction that directs (focuses) the learner's attention to valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 9. learning to learn: The acquisition of skills and strategies that can generalize across learning tasks can promote new learning. Examples may be deep analysis, help-seeking, use of advance organizers, and, most generally, meta-cognitive strategies. <br /> <br /> 10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while inhibiting others.<br /> <br /> The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> &lt;br&gt;&lt;center&gt;[[Image:rf-theory.jpg]]&lt;/center&gt;<br /> <br /> ==Significance==<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> == Glossary ==<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> == Research question ==<br /> The overall research question is how can instruction optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain? In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the cognitive demands of knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br /> <br /> == Independent variables ==<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> == Dependent variables ==<br /> The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br /> <br /> == Hypotheses ==<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. A corollary of this hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. <br /> <br /> Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br /> <br /> 1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> <br /> 2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby &quot;divergent coding systems&quot; here may be the same as &quot;multiple input sources&quot; in co-training.)<br /> <br /> 3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> <br /> 4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> <br /> 5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> <br /> 6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br /> <br /> 7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 8. Learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> <br /> 9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> == Explanation ==<br /> All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br /> <br /> == Descendents ==<br /> <br /> === Explicit instruction ===<br /> These projects often involve manipulations of attention &amp; discrimination. <br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> * [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> *[[Handwriting Algebra Tutor]] (Anthony, Yang &amp; Koedinger)<br /> **[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) [Also in Coordinative Learning]<br /> **[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress) [Also in Coordinative Learning]<br /> *[[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu)<br /> <br /> === Knowledge accessibility ===<br /> These projects include background knowledge &amp; knowledge suppression and often also involve novel knowledge component and cognitive task analysis.<br /> * [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br /> * [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Also in Interactive Communication]<br /> * [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> * [[Arithmetical fluency project]] (Fiez)<br /> * [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, &amp; Liu)<br /> * [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br /> <br /> === Active processing ===<br /> These projects also include some addressing issues of learner control<br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> *[[Note-Taking_Technologies | Note-taking Project Page (Bauer &amp; Koedinger)]] [Also in Coordinative Learning]<br /> **[[Note-Taking: Restriction and Selection]] (completed)<br /> **[[Note-Taking: Focusing On Concepts]] (planned)<br /> **[[Note-Taking: Focusing On Quantity]] (planned)<br /> <br /> === Optimal scheduling &amp; fluency pressure ===<br /> * [[Optimizing the practice schedule]] (Pavlik et al.)<br /> * [[French gender cues | French Grammatical Gender Cue Learning Through Optimized Practice]] (Presson-MacWhinney)<br /> * [[Japanese fluency]] (Yoshimura-MacWhinney)<br /> * [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br /> * [[Fostering fluency in second language learning]] (De Jong, Perfetti)<br /> * [[Using learning curves to optimize problem assignment]] (Cen &amp; Koedinger)<br /> * [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br /> <br /> == Annotated bibliography ==<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Strength&diff=6363 2007-12-03T15:42:55Z

<p>Perfetti: </p> <hr /> <div>The strength of a [[knowledge component]] is a continuous metric of how well it has been learned. As a knowledge component strengthens, its retrieval speed increases, moving toward [[fluency]] and increased [[long-term retention]]. As its strength is increased, retrieval of the knowledge component require less cognitive demand and thus yield more [[automaticity]], leaving more [[cognitive headroom]] for learning other, often more complex, knowledge components. <br /> <br /> [[Category:Glossary]]<br /> [[Category:Learning Process]]<br /> [[Category:Refinement and Fluency]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Reliability&diff=6362 2007-12-03T15:37:35Z

<p>Perfetti: </p> <hr /> <div>[[Category:Glossary]]<br /> [[Category:Refinement and Fluency]]<br /> Yielding the same or compatible results in different clinical experiments or statistical trials.<br /> <br /> Reliability is the consistency of measurement, or the degree to which an instrument measures the same each time it is used under the same condition with the same subjects. In short, it is the repeatability of the measurement. A measure is considered reliable if a person's score on the same test given twice is similar. It is important to remember that reliability is not measured, it is estimated. (Social Research Methods[http://www.socialresearchmethods.net/tutorial/Colosi/lcolosi2.htm])</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=6361 2007-12-03T15:23:40Z

<p>Perfetti: /* Abstract */</p> <hr /> <div>= The PSLC Refinement and Fluency cluster =<br /> <br /> == Abstract ==<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> <br /> 1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components. <br /> <br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> <br /> 3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br /> <br /> 4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information (&quot;facts&quot;) or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.<br /> <br /> 5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns. <br /> <br /> 6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.<br /> <br /> 7. [[cue validity]]: In both explicit and implicit instruction, the validity of a cue for a knowledge component affects the learning of that knowledge component. (Cue validity is related to [[feature validity]].)<br /> <br /> 8. [[focusing]]: Instruction that directs (focuses) the learner's attention to valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 9. learning to learn: The acquisition of skills and strategies that can generalize across learning tasks can promote new learning. Examples may be deep analysis, help-seeking, use of advance organizers, and, most generally, meta-cognitive strategies. <br /> <br /> 10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while inhibiting others.<br /> <br /> The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> &lt;br&gt;&lt;center&gt;[[Image:rf-theory.jpg]]&lt;/center&gt;<br /> <br /> ==Significance==<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> == Glossary ==<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> == Research question ==<br /> The overall research question is how can instruction optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain? In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the demands placed on learners of specific knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br /> <br /> == Independent variables ==<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> == Dependent variables ==<br /> The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br /> <br /> == Hypotheses ==<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. A corollary of this hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. <br /> <br /> Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br /> <br /> 1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> <br /> 2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby &quot;divergent coding systems&quot; here may be the same as &quot;multiple input sources&quot; in co-training.)<br /> <br /> 3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> <br /> 4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> <br /> 5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> <br /> 6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br /> <br /> 7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 8. Learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> <br /> 9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> == Explanation ==<br /> All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br /> <br /> == Descendents ==<br /> <br /> === Explicit instruction ===<br /> These projects often involve manipulations of attention &amp; discrimination. <br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> * [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> *[[Handwriting Algebra Tutor]] (Anthony, Yang &amp; Koedinger)<br /> **[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) [Also in Coordinative Learning]<br /> **[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress) [Also in Coordinative Learning]<br /> *[[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu)<br /> <br /> === Knowledge accessibility ===<br /> These projects include background knowledge &amp; knowledge suppression and often also involve novel knowledge component and cognitive task analysis.<br /> * [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br /> * [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Also in Interactive Communication]<br /> * [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> * [[Arithmetical fluency project]] (Fiez)<br /> * [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, &amp; Liu)<br /> * [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br /> <br /> === Active processing ===<br /> These projects also include some addressing issues of learner control<br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> *[[Note-Taking_Technologies | Note-taking Project Page (Bauer &amp; Koedinger)]] [Also in Coordinative Learning]<br /> **[[Note-Taking: Restriction and Selection]] (completed)<br /> **[[Note-Taking: Focusing On Concepts]] (planned)<br /> **[[Note-Taking: Focusing On Quantity]] (planned)<br /> <br /> === Optimal scheduling &amp; fluency pressure ===<br /> * [[Optimizing the practice schedule]] (Pavlik et al.)<br /> * [[French gender cues | French Grammatical Gender Cue Learning Through Optimized Practice]] (Presson-MacWhinney)<br /> * [[Japanese fluency]] (Yoshimura-MacWhinney)<br /> * [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br /> * [[Fostering fluency in second language learning]] (De Jong, Perfetti)<br /> * [[Using learning curves to optimize problem assignment]] (Cen &amp; Koedinger)<br /> * [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br /> <br /> == Annotated bibliography ==<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=6360 2007-12-03T15:14:01Z

<p>Perfetti: /* Abstract */</p> <hr /> <div>= The PSLC Refinement and Fluency cluster =<br /> <br /> == Abstract ==<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> <br /> 1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components. <br /> <br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> <br /> 3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br /> <br /> 4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information (&quot;facts&quot;) or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.<br /> <br /> 5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns. <br /> <br /> 6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.<br /> <br /> 7. [[cue validity]]: In both explicit and implicit instruction, the validity of cue for the knowledge component affects the learning of that knowledge component. <br /> <br /> 8. [[focusing]]: Instruction that focuses the learner's attention on valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 9. learning to learn: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> <br /> 10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> &lt;br&gt;&lt;center&gt;[[Image:rf-theory.jpg]]&lt;/center&gt;<br /> <br /> ==Significance==<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> == Glossary ==<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> == Research question ==<br /> The overall research question is how can instruction optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain? In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the demands placed on learners of specific knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br /> <br /> == Independent variables ==<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> == Dependent variables ==<br /> The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br /> <br /> == Hypotheses ==<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. A corollary of this hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. <br /> <br /> Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br /> <br /> 1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> <br /> 2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby &quot;divergent coding systems&quot; here may be the same as &quot;multiple input sources&quot; in co-training.)<br /> <br /> 3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> <br /> 4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> <br /> 5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> <br /> 6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br /> <br /> 7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 8. Learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> <br /> 9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> == Explanation ==<br /> All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br /> <br /> == Descendents ==<br /> <br /> === Explicit instruction ===<br /> These projects often involve manipulations of attention &amp; discrimination. <br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> * [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> *[[Handwriting Algebra Tutor]] (Anthony, Yang &amp; Koedinger)<br /> **[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) [Also in Coordinative Learning]<br /> **[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress) [Also in Coordinative Learning]<br /> *[[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu)<br /> <br /> === Knowledge accessibility ===<br /> These projects include background knowledge &amp; knowledge suppression and often also involve novel knowledge component and cognitive task analysis.<br /> * [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br /> * [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Also in Interactive Communication]<br /> * [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> * [[Arithmetical fluency project]] (Fiez)<br /> * [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, &amp; Liu)<br /> * [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br /> <br /> === Active processing ===<br /> These projects also include some addressing issues of learner control<br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> *[[Note-Taking_Technologies | Note-taking Project Page (Bauer &amp; Koedinger)]] [Also in Coordinative Learning]<br /> **[[Note-Taking: Restriction and Selection]] (completed)<br /> **[[Note-Taking: Focusing On Concepts]] (planned)<br /> **[[Note-Taking: Focusing On Quantity]] (planned)<br /> <br /> === Optimal scheduling &amp; fluency pressure ===<br /> * [[Optimizing the practice schedule]] (Pavlik et al.)<br /> * [[French gender cues | French Grammatical Gender Cue Learning Through Optimized Practice]] (Presson-MacWhinney)<br /> * [[Japanese fluency]] (Yoshimura-MacWhinney)<br /> * [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br /> * [[Fostering fluency in second language learning]] (De Jong, Perfetti)<br /> * [[Using learning curves to optimize problem assignment]] (Cen &amp; Koedinger)<br /> * [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br /> <br /> == Annotated bibliography ==<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=6359 2007-12-03T15:11:39Z

<p>Perfetti: /* Abstract */</p> <hr /> <div>= The PSLC Refinement and Fluency cluster =<br /> <br /> == Abstract ==<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> <br /> 1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components. <br /> <br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> <br /> 3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br /> <br /> 4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information (&quot;facts&quot;) or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.<br /> <br /> 5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns. <br /> <br /> 6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.<br /> <br /> 7. [[cue validity]]: In both explicit and implicit instruction, the validity of cue for the knowledge component affects the learning of that knowledge knowledge component. <br /> <br /> 8. [[focusing]]: Instruction that focuses the learner's attention on valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 9. learning to learn: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> <br /> 10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> &lt;br&gt;&lt;center&gt;[[Image:rf-theory.jpg]]&lt;/center&gt;<br /> <br /> ==Significance==<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> == Glossary ==<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> == Research question ==<br /> The overall research question is how can instruction optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain? In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the demands placed on learners of specific knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br /> <br /> == Independent variables ==<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> == Dependent variables ==<br /> The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br /> <br /> == Hypotheses ==<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. A corollary of this hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. <br /> <br /> Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br /> <br /> 1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> <br /> 2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby &quot;divergent coding systems&quot; here may be the same as &quot;multiple input sources&quot; in co-training.)<br /> <br /> 3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> <br /> 4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> <br /> 5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> <br /> 6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br /> <br /> 7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 8. Learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> <br /> 9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> == Explanation ==<br /> All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br /> <br /> == Descendents ==<br /> <br /> === Explicit instruction ===<br /> These projects often involve manipulations of attention &amp; discrimination. <br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> * [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> *[[Handwriting Algebra Tutor]] (Anthony, Yang &amp; Koedinger)<br /> **[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) [Also in Coordinative Learning]<br /> **[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress) [Also in Coordinative Learning]<br /> *[[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu)<br /> <br /> === Knowledge accessibility ===<br /> These projects include background knowledge &amp; knowledge suppression and often also involve novel knowledge component and cognitive task analysis.<br /> * [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br /> * [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Also in Interactive Communication]<br /> * [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> * [[Arithmetical fluency project]] (Fiez)<br /> * [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, &amp; Liu)<br /> * [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br /> <br /> === Active processing ===<br /> These projects also include some addressing issues of learner control<br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> *[[Note-Taking_Technologies | Note-taking Project Page (Bauer &amp; Koedinger)]] [Also in Coordinative Learning]<br /> **[[Note-Taking: Restriction and Selection]] (completed)<br /> **[[Note-Taking: Focusing On Concepts]] (planned)<br /> **[[Note-Taking: Focusing On Quantity]] (planned)<br /> <br /> === Optimal scheduling &amp; fluency pressure ===<br /> * [[Optimizing the practice schedule]] (Pavlik et al.)<br /> * [[French gender cues | French Grammatical Gender Cue Learning Through Optimized Practice]] (Presson-MacWhinney)<br /> * [[Japanese fluency]] (Yoshimura-MacWhinney)<br /> * [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br /> * [[Fostering fluency in second language learning]] (De Jong, Perfetti)<br /> * [[Using learning curves to optimize problem assignment]] (Cen &amp; Koedinger)<br /> * [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br /> <br /> == Annotated bibliography ==<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=6358 2007-12-03T14:57:50Z

<p>Perfetti: /* Abstract */</p> <hr /> <div>= The PSLC Refinement and Fluency cluster =<br /> <br /> == Abstract ==<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> <br /> 1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance. To design effective instruction, learning tasks are anlayzed into simpler task components. <br /> <br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> <br /> 3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br /> <br /> 4. [[explicit instruction]]: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> <br /> 5. [[implicit instruction]]: On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> <br /> 6. immediacy of feedback: A corollary of the emphasis on in vivo evaluation, scheduling, and explicit instruction is the idea that immediate feedback facilitates learning.<br /> <br /> 7. [[cue validity]]: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.<br /> <br /> 8. [[focusing]]: Instruction that focuses the learner's attention on valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 9. learning to learn: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> <br /> 10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> &lt;br&gt;&lt;center&gt;[[Image:rf-theory.jpg]]&lt;/center&gt;<br /> <br /> ==Significance==<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> == Glossary ==<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> == Research question ==<br /> The overall research question is how can instruction optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain? In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the demands placed on learners of specific knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br /> <br /> == Independent variables ==<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> == Dependent variables ==<br /> The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br /> <br /> == Hypotheses ==<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. A corollary of this hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. <br /> <br /> Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br /> <br /> 1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> <br /> 2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby &quot;divergent coding systems&quot; here may be the same as &quot;multiple input sources&quot; in co-training.)<br /> <br /> 3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> <br /> 4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> <br /> 5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> <br /> 6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br /> <br /> 7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> <br /> 8. Learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> <br /> 9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> == Explanation ==<br /> All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br /> <br /> == Descendents ==<br /> <br /> === Explicit instruction ===<br /> These projects often involve manipulations of attention &amp; discrimination. <br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> * [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> *[[Handwriting Algebra Tutor]] (Anthony, Yang &amp; Koedinger)<br /> **[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) [Also in Coordinative Learning]<br /> **[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress) [Also in Coordinative Learning]<br /> *[[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu)<br /> <br /> === Knowledge accessibility ===<br /> These projects include background knowledge &amp; knowledge suppression and often also involve novel knowledge component and cognitive task analysis.<br /> * [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br /> * [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Also in Interactive Communication]<br /> * [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> * [[Arithmetical fluency project]] (Fiez)<br /> * [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, &amp; Liu)<br /> * [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br /> <br /> === Active processing ===<br /> These projects also include some addressing issues of learner control<br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> *[[Note-Taking_Technologies | Note-taking Project Page (Bauer &amp; Koedinger)]] [Also in Coordinative Learning]<br /> **[[Note-Taking: Restriction and Selection]] (completed)<br /> **[[Note-Taking: Focusing On Concepts]] (planned)<br /> **[[Note-Taking: Focusing On Quantity]] (planned)<br /> <br /> === Optimal scheduling &amp; fluency pressure ===<br /> * [[Optimizing the practice schedule]] (Pavlik et al.)<br /> * [[French gender cues | French Grammatical Gender Cue Learning Through Optimized Practice]] (Presson-MacWhinney)<br /> * [[Japanese fluency]] (Yoshimura-MacWhinney)<br /> * [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br /> * [[Fostering fluency in second language learning]] (De Jong, Perfetti)<br /> * [[Using learning curves to optimize problem assignment]] (Cen &amp; Koedinger)<br /> * [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br /> <br /> == Annotated bibliography ==<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Fluency&diff=5169 2007-05-23T14:17:11Z

<p>Perfetti: </p> <hr /> <div>[[Category:Glossary]]<br /> [[Category:Refinement and Fluency]]<br /> Fluency refers to a task performance that is executed smoothly and in a way consistent with expertise. Fluency also applies to learners who characteristically achieve fluent performance in some task.</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Fluency&diff=5168 2007-05-23T14:16:38Z

<p>Perfetti: </p> <hr /> <div>[[Category:Glossary]]<br /> [[Category:Refinement and Fluency]]<br /> Fluency refers to a task performance that is executed smoothly and in a way consistent with expertise. Fluency also applies to learners who characteristically achieve fluent perfirmance in some task.</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=1976 2006-10-16T15:14:31Z

<p>Perfetti: /* Explanation */</p> <hr /> <div>== The PSLC Refinement and Fluency cluster ==<br /> <br /> === Abstract ===<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, refinement, and fluent control of critical knowledge components. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> 1. task analysis: To design effective instruction, we must analyze learning tasks into their simplest components.<br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> 3. scheduling of practice: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> 4. explicit instruction: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> 5. implicit instruction: On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> 6. immediacy of feedback: A corollary of the emphasis on in vivo evaluation, scheduling, and explicit instruction is the idea that immediate feedback facilitates learning.<br /> 7. cue validity: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.<br /> 8. focusing: Instruction that focuses the learner's attention on valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> 9. learning to learn: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> 10. transfer: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that robust learning occurs when the learning event space is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> ===Significance===<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for fluency-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> === Glossary ===<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> === Research question ===<br /> The research pursued in this cluster tests the empirical adequacy of the propositions (see Abstract) that are derived from its overall hypothesis. The overall research question is to how instruction can optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain. In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the demands placed on learners of specific knowledge components, the scheduling of practice, the timing and extent of explicit teaching events relative to implicit learning opportunities, and the role of feedback.<br /> <br /> === Independent variables ===<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> === Dependent variables ===<br /> The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br /> <br /> === Hypotheses ===<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. A corollary of this hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. <br /> <br /> Specific hypotheses about the organization of instruction derive from task analyses of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br /> <br /> 1. scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> 2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems.<br /> 3. explicit instruction hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> 4. implicit instruction hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> 5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> 6. cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.<br /> 7. Focusing hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> 8. learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> 9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> === Explanation ===<br /> All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the long term retention of the associations.<br /> <br /> === Descendents ===<br /> <br /> * [[Using syntactic priming to increase robust learning]] (de Jong, Perfetti, DeKeyser)<br /> <br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> <br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> <br /> *[[French gender cues]] (Presson-MacWhinney)<br /> <br /> *[[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> <br /> *[[Japanese fluency]] (Yoshimura-MacWhinney)<br /> <br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> <br /> * [[Optimizing the practice schedule]] (Pavlik-MacWhinney)<br /> <br /> * [[Semantic grouping during vocabulary training]] (Tokowicz-Degani)<br /> <br /> *[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Was in Coordinative Learning and in Interactive Communication]<br /> <br /> *[[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> <br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> <br /> * [[arithmetical fluency project]] (Fiez)<br /> <br /> * [[HandwritingEquationSolving|A multimodal (handwriting) interface for solving equations]] (Anthony, Yang, &amp; Koedinger) [Was in CL]<br /> <br /> === Annotated bibliography ===<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=1975 2006-10-16T15:04:29Z

<p>Perfetti: /* Hypotheses */</p> <hr /> <div>== The PSLC Refinement and Fluency cluster ==<br /> <br /> === Abstract ===<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, refinement, and fluent control of critical knowledge components. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> 1. task analysis: To design effective instruction, we must analyze learning tasks into their simplest components.<br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> 3. scheduling of practice: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> 4. explicit instruction: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> 5. implicit instruction: On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> 6. immediacy of feedback: A corollary of the emphasis on in vivo evaluation, scheduling, and explicit instruction is the idea that immediate feedback facilitates learning.<br /> 7. cue validity: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.<br /> 8. focusing: Instruction that focuses the learner's attention on valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> 9. learning to learn: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> 10. transfer: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that robust learning occurs when the learning event space is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> ===Significance===<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for fluency-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> === Glossary ===<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> === Research question ===<br /> The research pursued in this cluster tests the empirical adequacy of the propositions (see Abstract) that are derived from its overall hypothesis. The overall research question is to how instruction can optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain. In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the demands placed on learners of specific knowledge components, the scheduling of practice, the timing and extent of explicit teaching events relative to implicit learning opportunities, and the role of feedback.<br /> <br /> === Independent variables ===<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> === Dependent variables ===<br /> The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br /> <br /> === Hypotheses ===<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. A corollary of this hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. <br /> <br /> Specific hypotheses about the organization of instruction derive from task analyses of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br /> <br /> 1. scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> 2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems.<br /> 3. explicit instruction hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> 4. implicit instruction hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> 5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> 6. cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.<br /> 7. Focusing hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> 8. learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> 9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> === Explanation ===<br /> Attention to features of the task domain as a knowledge component is processed leads to associating those features with the knowledge component. If the features are valid, then forming or strengthening such associations facilitates retrieval during subsequent assessment or instruction, and thus leads to more robust learning. However, the robustness is also dependent on the scheduling of learning events that promotes the long term retentiono of the associations.<br /> <br /> === Descendents ===<br /> <br /> * [[Using syntactic priming to increase robust learning]] (de Jong, Perfetti, DeKeyser)<br /> <br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> <br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> <br /> *[[French gender cues]] (Presson-MacWhinney)<br /> <br /> *[[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> <br /> *[[Japanese fluency]] (Yoshimura-MacWhinney)<br /> <br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> <br /> * [[Optimizing the practice schedule]] (Pavlik-MacWhinney)<br /> <br /> * [[Semantic grouping during vocabulary training]] (Tokowicz-Degani)<br /> <br /> *[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Was in Coordinative Learning and in Interactive Communication]<br /> <br /> *[[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> <br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> <br /> * [[arithmetical fluency project]] (Fiez)<br /> <br /> * [[HandwritingEquationSolving|A multimodal (handwriting) interface for solving equations]] (Anthony, Yang, &amp; Koedinger) [Was in CL]<br /> <br /> === Annotated bibliography ===<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=1974 2006-10-16T14:49:31Z

<p>Perfetti: /* Dependent variables */</p> <hr /> <div>== The PSLC Refinement and Fluency cluster ==<br /> <br /> === Abstract ===<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, refinement, and fluent control of critical knowledge components. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> 1. task analysis: To design effective instruction, we must analyze learning tasks into their simplest components.<br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> 3. scheduling of practice: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> 4. explicit instruction: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> 5. implicit instruction: On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> 6. immediacy of feedback: A corollary of the emphasis on in vivo evaluation, scheduling, and explicit instruction is the idea that immediate feedback facilitates learning.<br /> 7. cue validity: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.<br /> 8. focusing: Instruction that focuses the learner's attention on valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> 9. learning to learn: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> 10. transfer: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that robust learning occurs when the learning event space is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> ===Significance===<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for fluency-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> === Glossary ===<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> === Research question ===<br /> The research pursued in this cluster tests the empirical adequacy of the propositions (see Abstract) that are derived from its overall hypothesis. The overall research question is to how instruction can optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain. In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the demands placed on learners of specific knowledge components, the scheduling of practice, the timing and extent of explicit teaching events relative to implicit learning opportunities, and the role of feedback.<br /> <br /> === Independent variables ===<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> === Dependent variables ===<br /> The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br /> <br /> === Hypotheses ===<br /> The general hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. The specific hypotheses are:<br /> # '''task analysis''': To design effective instruction, we must analyze learning tasks into their simplest components.<br /> # '''fluency from basics''': For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> # '''in vivo evaluation''': The work in this cluster is targeted toward the in vivo evaluation of instruction in basic skills.<br /> # '''scheduling of practice''': The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> # '''resonance''': The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems.<br /> # '''explicit instruction''': Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> # '''implicit instruction''': On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> # '''immediacy of feedback''': Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> # '''cue validity''': In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.<br /> # '''focusing''': Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> # '''learning to learn''': The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> # '''transfer''': A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> === Explanation ===<br /> Attention to features of the task domain as a knowledge component is processed leads to associating those features with the knowledge component. If the features are valid, then forming or strengthening such associations facilitates retrieval during subsequent assessment or instruction, and thus leads to more robust learning. However, the robustness is also dependent on the scheduling of learning events that promotes the long term retentiono of the associations.<br /> <br /> === Descendents ===<br /> <br /> * [[Using syntactic priming to increase robust learning]] (de Jong, Perfetti, DeKeyser)<br /> <br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> <br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> <br /> *[[French gender cues]] (Presson-MacWhinney)<br /> <br /> *[[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> <br /> *[[Japanese fluency]] (Yoshimura-MacWhinney)<br /> <br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> <br /> * [[Optimizing the practice schedule]] (Pavlik-MacWhinney)<br /> <br /> * [[Semantic grouping during vocabulary training]] (Tokowicz-Degani)<br /> <br /> *[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Was in Coordinative Learning and in Interactive Communication]<br /> <br /> *[[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> <br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> <br /> * [[arithmetical fluency project]] (Fiez)<br /> <br /> * [[HandwritingEquationSolving|A multimodal (handwriting) interface for solving equations]] (Anthony, Yang, &amp; Koedinger) [Was in CL]<br /> <br /> === Annotated bibliography ===<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=1973 2006-10-16T14:48:55Z

<p>Perfetti: /* Independent variables */</p> <hr /> <div>== The PSLC Refinement and Fluency cluster ==<br /> <br /> === Abstract ===<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, refinement, and fluent control of critical knowledge components. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> 1. task analysis: To design effective instruction, we must analyze learning tasks into their simplest components.<br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> 3. scheduling of practice: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> 4. explicit instruction: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> 5. implicit instruction: On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> 6. immediacy of feedback: A corollary of the emphasis on in vivo evaluation, scheduling, and explicit instruction is the idea that immediate feedback facilitates learning.<br /> 7. cue validity: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.<br /> 8. focusing: Instruction that focuses the learner's attention on valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> 9. learning to learn: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> 10. transfer: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that robust learning occurs when the learning event space is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> ===Significance===<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for fluency-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> === Glossary ===<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> === Research question ===<br /> The research pursued in this cluster tests the empirical adequacy of the propositions (see Abstract) that are derived from its overall hypothesis. The overall research question is to how instruction can optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain. In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the demands placed on learners of specific knowledge components, the scheduling of practice, the timing and extent of explicit teaching events relative to implicit learning opportunities, and the role of feedback.<br /> <br /> === Independent variables ===<br /> At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br /> <br /> === Dependent variables ===<br /> The dependent variables in these studies are typically percentage correct and time to mastery of a structure at a certain level.<br /> <br /> === Hypotheses ===<br /> The general hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. The specific hypotheses are:<br /> # '''task analysis''': To design effective instruction, we must analyze learning tasks into their simplest components.<br /> # '''fluency from basics''': For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> # '''in vivo evaluation''': The work in this cluster is targeted toward the in vivo evaluation of instruction in basic skills.<br /> # '''scheduling of practice''': The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> # '''resonance''': The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems.<br /> # '''explicit instruction''': Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> # '''implicit instruction''': On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> # '''immediacy of feedback''': Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> # '''cue validity''': In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.<br /> # '''focusing''': Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> # '''learning to learn''': The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> # '''transfer''': A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> === Explanation ===<br /> Attention to features of the task domain as a knowledge component is processed leads to associating those features with the knowledge component. If the features are valid, then forming or strengthening such associations facilitates retrieval during subsequent assessment or instruction, and thus leads to more robust learning. However, the robustness is also dependent on the scheduling of learning events that promotes the long term retentiono of the associations.<br /> <br /> === Descendents ===<br /> <br /> * [[Using syntactic priming to increase robust learning]] (de Jong, Perfetti, DeKeyser)<br /> <br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> <br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> <br /> *[[French gender cues]] (Presson-MacWhinney)<br /> <br /> *[[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> <br /> *[[Japanese fluency]] (Yoshimura-MacWhinney)<br /> <br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> <br /> * [[Optimizing the practice schedule]] (Pavlik-MacWhinney)<br /> <br /> * [[Semantic grouping during vocabulary training]] (Tokowicz-Degani)<br /> <br /> *[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Was in Coordinative Learning and in Interactive Communication]<br /> <br /> *[[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> <br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> <br /> * [[arithmetical fluency project]] (Fiez)<br /> <br /> * [[HandwritingEquationSolving|A multimodal (handwriting) interface for solving equations]] (Anthony, Yang, &amp; Koedinger) [Was in CL]<br /> <br /> === Annotated bibliography ===<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=1972 2006-10-16T14:42:28Z

<p>Perfetti: /* Research question */</p> <hr /> <div>== The PSLC Refinement and Fluency cluster ==<br /> <br /> === Abstract ===<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, refinement, and fluent control of critical knowledge components. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> 1. task analysis: To design effective instruction, we must analyze learning tasks into their simplest components.<br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> 3. scheduling of practice: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> 4. explicit instruction: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> 5. implicit instruction: On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> 6. immediacy of feedback: A corollary of the emphasis on in vivo evaluation, scheduling, and explicit instruction is the idea that immediate feedback facilitates learning.<br /> 7. cue validity: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.<br /> 8. focusing: Instruction that focuses the learner's attention on valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> 9. learning to learn: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> 10. transfer: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that robust learning occurs when the learning event space is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> ===Significance===<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for fluency-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> === Glossary ===<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> === Research question ===<br /> The research pursued in this cluster tests the empirical adequacy of the propositions (see Abstract) that are derived from its overall hypothesis. The overall research question is to how instruction can optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain. In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the demands placed on learners of specific knowledge components, the scheduling of practice, the timing and extent of explicit teaching events relative to implicit learning opportunities, and the role of feedback.<br /> <br /> === Independent variables ===<br /> Alternative structures of instructional events based on alternative analyses of task demands, relevant knowledge components, and learner background. Assessing the learner’s background is essentially part of the learning task analysis.<br /> <br /> === Dependent variables ===<br /> The dependent variables in these studies are typically percentage correct and time to mastery of a structure at a certain level.<br /> <br /> === Hypotheses ===<br /> The general hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. The specific hypotheses are:<br /> # '''task analysis''': To design effective instruction, we must analyze learning tasks into their simplest components.<br /> # '''fluency from basics''': For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> # '''in vivo evaluation''': The work in this cluster is targeted toward the in vivo evaluation of instruction in basic skills.<br /> # '''scheduling of practice''': The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> # '''resonance''': The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems.<br /> # '''explicit instruction''': Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> # '''implicit instruction''': On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> # '''immediacy of feedback''': Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> # '''cue validity''': In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.<br /> # '''focusing''': Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> # '''learning to learn''': The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> # '''transfer''': A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> === Explanation ===<br /> Attention to features of the task domain as a knowledge component is processed leads to associating those features with the knowledge component. If the features are valid, then forming or strengthening such associations facilitates retrieval during subsequent assessment or instruction, and thus leads to more robust learning. However, the robustness is also dependent on the scheduling of learning events that promotes the long term retentiono of the associations.<br /> <br /> === Descendents ===<br /> <br /> * [[Using syntactic priming to increase robust learning]] (de Jong, Perfetti, DeKeyser)<br /> <br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> <br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> <br /> *[[French gender cues]] (Presson-MacWhinney)<br /> <br /> *[[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> <br /> *[[Japanese fluency]] (Yoshimura-MacWhinney)<br /> <br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> <br /> * [[Optimizing the practice schedule]] (Pavlik-MacWhinney)<br /> <br /> * [[Semantic grouping during vocabulary training]] (Tokowicz-Degani)<br /> <br /> *[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Was in Coordinative Learning and in Interactive Communication]<br /> <br /> *[[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> <br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> <br /> * [[arithmetical fluency project]] (Fiez)<br /> <br /> * [[HandwritingEquationSolving|A multimodal (handwriting) interface for solving equations]] (Anthony, Yang, &amp; Koedinger) [Was in CL]<br /> <br /> === Annotated bibliography ===<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=1971 2006-10-16T14:19:13Z

<p>Perfetti: /* Abstract */</p> <hr /> <div>== The PSLC Refinement and Fluency cluster ==<br /> <br /> === Abstract ===<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, refinement, and fluent control of critical knowledge components. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br /> 1. task analysis: To design effective instruction, we must analyze learning tasks into their simplest components.<br /> 2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> 3. scheduling of practice: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> 4. explicit instruction: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> 5. implicit instruction: On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> 6. immediacy of feedback: A corollary of the emphasis on in vivo evaluation, scheduling, and explicit instruction is the idea that immediate feedback facilitates learning.<br /> 7. cue validity: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.<br /> 8. focusing: Instruction that focuses the learner's attention on valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> 9. learning to learn: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> 10. transfer: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that robust learning occurs when the learning event space is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> ===Significance===<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for fluency-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> === Glossary ===<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> === Research question ===<br /> The research pursued in this cluster tests the empirical adequacy of the nine core assumptions listed in the Abstract. Studies <br /> focusing on these various issues include the following:<br /> # '''task analysis''': All studies in the cluster rely on task analysis to generate stimuli, instructional procedures, and evaluation methods.<br /> # '''fluency from basics''': All studies in the cluster focus on basic skills, such as vocabulary, dictation, grammatical categorization, and auditory learning. The Yoshimura-MacWhinney study of sentence production shows how fluency on the sentence level arises from consolidation of lower basic vocabulary skills. The MacWhinney study of dication points in a similar direction for the influences of phonemics patterns on the overall sentence dictation.<br /> # '''in vivo orientation''': All studies in this cluster target the development of in vivo instruction.<br /> # '''scheduling of practice''': The Pavlik and MacWhinney studies emphasize the role of scheduling in minimize the time students require to achieve mastery of a basic skill.<br /> # '''resonance''': Studies that examine resonance between representations as a facilitator of learning include: the study of Hanzi character learning by Liu et al., and the Pavlik-MacWhinney study of vocabulary learning in Chinese,<br /> # '''explicit instruction''': The studies examining explicit instruction include the Presson-MacWhinney study of French gender cue learning, the Zhang-MacWhinney study of pinyin dictation, and the Mitamura-Wylie study of article selection.<br /> # '''implicit instruction''': No studies are currently examining the generalized effect of implicit instruction.<br /> # '''immediacy of feedback''': All the studies in this cluster provide immediate feedback, often with careful diagnosis, based on tracking of individual subject performance.<br /> # '''cue validity''': The Presson-MacWhinney gender study, the Zhang-MacWhinney pinyin dictation study, and the Mitamura-Wylie study of article selection all specifically examine the role of cue validity in predicting initial learning and robustness.<br /> # '''focusing''': The study of articulatory cues to consonant and tone production by Liu et al. emphasize the role of focusing. The Tokowicz-Degani study of vocabulary learning examines the role of novelty in increasing attentional focusing during learning episodes.<br /> # '''learning to learn''': The Roll-Aleven-McLaren study of help-seeking examines how students learn to learn, and<br /> # '''transfer''': The Zhang-MacWhinney study of pinyin dictation examines negative transfer from English phonology to the learning of Mandarin phonology. The Mitamura-Wylie study of article selection examines negative effects of L1 article usage on learning of English article usage.<br /> <br /> === Independent variables ===<br /> Alternative structures of instructional events based on alternative analyses of task demands, relevant knowledge components, and learner background. Assessing the learner’s background is essentially part of the learning task analysis.<br /> <br /> === Dependent variables ===<br /> The dependent variables in these studies are typically percentage correct and time to mastery of a structure at a certain level.<br /> <br /> === Hypotheses ===<br /> The general hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. The specific hypotheses are:<br /> # '''task analysis''': To design effective instruction, we must analyze learning tasks into their simplest components.<br /> # '''fluency from basics''': For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> # '''in vivo evaluation''': The work in this cluster is targeted toward the in vivo evaluation of instruction in basic skills.<br /> # '''scheduling of practice''': The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> # '''resonance''': The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems.<br /> # '''explicit instruction''': Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> # '''implicit instruction''': On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> # '''immediacy of feedback''': Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> # '''cue validity''': In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.<br /> # '''focusing''': Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> # '''learning to learn''': The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> # '''transfer''': A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> === Explanation ===<br /> Attention to features of the task domain as a knowledge component is processed leads to associating those features with the knowledge component. If the features are valid, then forming or strengthening such associations facilitates retrieval during subsequent assessment or instruction, and thus leads to more robust learning. However, the robustness is also dependent on the scheduling of learning events that promotes the long term retentiono of the associations.<br /> <br /> === Descendents ===<br /> <br /> * [[Using syntactic priming to increase robust learning]] (de Jong, Perfetti, DeKeyser)<br /> <br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> <br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> <br /> *[[French gender cues]] (Presson-MacWhinney)<br /> <br /> *[[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> <br /> *[[Japanese fluency]] (Yoshimura-MacWhinney)<br /> <br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> <br /> * [[Optimizing the practice schedule]] (Pavlik-MacWhinney)<br /> <br /> * [[Semantic grouping during vocabulary training]] (Tokowicz-Degani)<br /> <br /> *[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Was in Coordinative Learning and in Interactive Communication]<br /> <br /> *[[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> <br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> <br /> * [[arithmetical fluency project]] (Fiez)<br /> <br /> * [[HandwritingEquationSolving|A multimodal (handwriting) interface for solving equations]] (Anthony, Yang, &amp; Koedinger) [Was in CL]<br /> <br /> === Annotated bibliography ===<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=1970 2006-10-16T13:20:39Z

<p>Perfetti: /* Significance */</p> <hr /> <div>== The PSLC Refinement and Fluency cluster ==<br /> <br /> === Abstract ===<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, refinement, and fluent control of critical knowledge components. The core issue examined in this cluster include: <br /> # '''task analysis''': To design effective instruction, we must analyze learning tasks into their simplest components.<br /> # '''fluency from basics''': For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> # '''in vivo evaluation''': The work in this cluster is targeted toward the in vivo evaluation of instruction in basic skills.<br /> # '''scheduling of practice''': The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> # '''resonance''': The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems.<br /> # '''explicit instruction''': Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> # '''implicit instruction''': On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> # '''immediacy of feedback''': A corollary of the emphasis on in vivo evaluation, scheduling, and explicit instruction is the idea that immediate feedback, which is a strong point of computerized instruction, facilitates learning.<br /> # '''cue validity''': In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.<br /> # '''focusing''': Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> # '''learning to learn''': The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> # '''transfer''': A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that robust learning occurs when the learning event space is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> ===Significance===<br /> A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for fluency-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> === Glossary ===<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> === Research question ===<br /> The research pursued in this cluster tests the empirical adequacy of the nine core assumptions listed in the Abstract. Studies <br /> focusing on these various issues include the following:<br /> # '''task analysis''': All studies in the cluster rely on task analysis to generate stimuli, instructional procedures, and evaluation methods.<br /> # '''fluency from basics''': All studies in the cluster focus on basic skills, such as vocabulary, dictation, grammatical categorization, and auditory learning. The Yoshimura-MacWhinney study of sentence production shows how fluency on the sentence level arises from consolidation of lower basic vocabulary skills. The MacWhinney study of dication points in a similar direction for the influences of phonemics patterns on the overall sentence dictation.<br /> # '''in vivo orientation''': All studies in this cluster target the development of in vivo instruction.<br /> # '''scheduling of practice''': The Pavlik and MacWhinney studies emphasize the role of scheduling in minimize the time students require to achieve mastery of a basic skill.<br /> # '''resonance''': Studies that examine resonance between representations as a facilitator of learning include: the study of Hanzi character learning by Liu et al., and the Pavlik-MacWhinney study of vocabulary learning in Chinese,<br /> # '''explicit instruction''': The studies examining explicit instruction include the Presson-MacWhinney study of French gender cue learning, the Zhang-MacWhinney study of pinyin dictation, and the Mitamura-Wylie study of article selection.<br /> # '''implicit instruction''': No studies are currently examining the generalized effect of implicit instruction.<br /> # '''immediacy of feedback''': All the studies in this cluster provide immediate feedback, often with careful diagnosis, based on tracking of individual subject performance.<br /> # '''cue validity''': The Presson-MacWhinney gender study, the Zhang-MacWhinney pinyin dictation study, and the Mitamura-Wylie study of article selection all specifically examine the role of cue validity in predicting initial learning and robustness.<br /> # '''focusing''': The study of articulatory cues to consonant and tone production by Liu et al. emphasize the role of focusing. The Tokowicz-Degani study of vocabulary learning examines the role of novelty in increasing attentional focusing during learning episodes.<br /> # '''learning to learn''': The Roll-Aleven-McLaren study of help-seeking examines how students learn to learn, and<br /> # '''transfer''': The Zhang-MacWhinney study of pinyin dictation examines negative transfer from English phonology to the learning of Mandarin phonology. The Mitamura-Wylie study of article selection examines negative effects of L1 article usage on learning of English article usage.<br /> <br /> === Independent variables ===<br /> Alternative structures of instructional events based on alternative analyses of task demands, relevant knowledge components, and learner background. Assessing the learner’s background is essentially part of the learning task analysis.<br /> <br /> === Dependent variables ===<br /> The dependent variables in these studies are typically percentage correct and time to mastery of a structure at a certain level.<br /> <br /> === Hypotheses ===<br /> The general hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. The specific hypotheses are:<br /> # '''task analysis''': To design effective instruction, we must analyze learning tasks into their simplest components.<br /> # '''fluency from basics''': For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> # '''in vivo evaluation''': The work in this cluster is targeted toward the in vivo evaluation of instruction in basic skills.<br /> # '''scheduling of practice''': The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> # '''resonance''': The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems.<br /> # '''explicit instruction''': Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> # '''implicit instruction''': On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> # '''immediacy of feedback''': Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> # '''cue validity''': In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.<br /> # '''focusing''': Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> # '''learning to learn''': The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> # '''transfer''': A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> === Explanation ===<br /> Attention to features of the task domain as a knowledge component is processed leads to associating those features with the knowledge component. If the features are valid, then forming or strengthening such associations facilitates retrieval during subsequent assessment or instruction, and thus leads to more robust learning. However, the robustness is also dependent on the scheduling of learning events that promotes the long term retentiono of the associations.<br /> <br /> === Descendents ===<br /> <br /> * [[Using syntactic priming to increase robust learning]] (de Jong, Perfetti, DeKeyser)<br /> <br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> <br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> <br /> *[[French gender cues]] (Presson-MacWhinney)<br /> <br /> *[[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> <br /> *[[Japanese fluency]] (Yoshimura-MacWhinney)<br /> <br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> <br /> * [[Optimizing the practice schedule]] (Pavlik-MacWhinney)<br /> <br /> * [[Semantic grouping during vocabulary training]] (Tokowicz-Degani)<br /> <br /> *[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Was in Coordinative Learning and in Interactive Communication]<br /> <br /> *[[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> <br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> <br /> * [[arithmetical fluency project]] (Fiez)<br /> <br /> * [[HandwritingEquationSolving|A multimodal (handwriting) interface for solving equations]] (Anthony, Yang, &amp; Koedinger) [Was in CL]<br /> <br /> === Annotated bibliography ===<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti https://learnlab.org/mediawiki-1.44.2/index.php?title=Refinement_and_Fluency&diff=1969 2006-10-16T13:14:24Z

<p>Perfetti: /* Abstract */</p> <hr /> <div>== The PSLC Refinement and Fluency cluster ==<br /> <br /> === Abstract ===<br /> The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, refinement, and fluent control of critical knowledge components. The core issue examined in this cluster include: <br /> # '''task analysis''': To design effective instruction, we must analyze learning tasks into their simplest components.<br /> # '''fluency from basics''': For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> # '''in vivo evaluation''': The work in this cluster is targeted toward the in vivo evaluation of instruction in basic skills.<br /> # '''scheduling of practice''': The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> # '''resonance''': The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems.<br /> # '''explicit instruction''': Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> # '''implicit instruction''': On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> # '''immediacy of feedback''': A corollary of the emphasis on in vivo evaluation, scheduling, and explicit instruction is the idea that immediate feedback, which is a strong point of computerized instruction, facilitates learning.<br /> # '''cue validity''': In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.<br /> # '''focusing''': Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> # '''learning to learn''': The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> # '''transfer''': A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br /> <br /> This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that robust learning occurs when the learning event space is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br /> <br /> ===Significance===<br /> <br /> A core theme in this cluster is that instruction in basic skills can faciliate the overall solidication of knowledge in a domain. Computerized instruction is particularly good at providing practice and feedback for basic skills according to a schedule that closely matches observed student abilities. In the area of second language learning, these strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, it appears that an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br /> <br /> === Glossary ===<br /> [[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br /> <br /> === Research question ===<br /> The research pursued in this cluster tests the empirical adequacy of the nine core assumptions listed in the Abstract. Studies <br /> focusing on these various issues include the following:<br /> # '''task analysis''': All studies in the cluster rely on task analysis to generate stimuli, instructional procedures, and evaluation methods.<br /> # '''fluency from basics''': All studies in the cluster focus on basic skills, such as vocabulary, dictation, grammatical categorization, and auditory learning. The Yoshimura-MacWhinney study of sentence production shows how fluency on the sentence level arises from consolidation of lower basic vocabulary skills. The MacWhinney study of dication points in a similar direction for the influences of phonemics patterns on the overall sentence dictation.<br /> # '''in vivo orientation''': All studies in this cluster target the development of in vivo instruction.<br /> # '''scheduling of practice''': The Pavlik and MacWhinney studies emphasize the role of scheduling in minimize the time students require to achieve mastery of a basic skill.<br /> # '''resonance''': Studies that examine resonance between representations as a facilitator of learning include: the study of Hanzi character learning by Liu et al., and the Pavlik-MacWhinney study of vocabulary learning in Chinese,<br /> # '''explicit instruction''': The studies examining explicit instruction include the Presson-MacWhinney study of French gender cue learning, the Zhang-MacWhinney study of pinyin dictation, and the Mitamura-Wylie study of article selection.<br /> # '''implicit instruction''': No studies are currently examining the generalized effect of implicit instruction.<br /> # '''immediacy of feedback''': All the studies in this cluster provide immediate feedback, often with careful diagnosis, based on tracking of individual subject performance.<br /> # '''cue validity''': The Presson-MacWhinney gender study, the Zhang-MacWhinney pinyin dictation study, and the Mitamura-Wylie study of article selection all specifically examine the role of cue validity in predicting initial learning and robustness.<br /> # '''focusing''': The study of articulatory cues to consonant and tone production by Liu et al. emphasize the role of focusing. The Tokowicz-Degani study of vocabulary learning examines the role of novelty in increasing attentional focusing during learning episodes.<br /> # '''learning to learn''': The Roll-Aleven-McLaren study of help-seeking examines how students learn to learn, and<br /> # '''transfer''': The Zhang-MacWhinney study of pinyin dictation examines negative transfer from English phonology to the learning of Mandarin phonology. The Mitamura-Wylie study of article selection examines negative effects of L1 article usage on learning of English article usage.<br /> <br /> === Independent variables ===<br /> Alternative structures of instructional events based on alternative analyses of task demands, relevant knowledge components, and learner background. Assessing the learner’s background is essentially part of the learning task analysis.<br /> <br /> === Dependent variables ===<br /> The dependent variables in these studies are typically percentage correct and time to mastery of a structure at a certain level.<br /> <br /> === Hypotheses ===<br /> The general hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. The specific hypotheses are:<br /> # '''task analysis''': To design effective instruction, we must analyze learning tasks into their simplest components.<br /> # '''fluency from basics''': For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br /> # '''in vivo evaluation''': The work in this cluster is targeted toward the in vivo evaluation of instruction in basic skills.<br /> # '''scheduling of practice''': The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br /> # '''resonance''': The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems.<br /> # '''explicit instruction''': Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br /> # '''implicit instruction''': On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br /> # '''immediacy of feedback''': Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br /> # '''cue validity''': In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.<br /> # '''focusing''': Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br /> # '''learning to learn''': The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.<br /> # '''transfer''': A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br /> <br /> === Explanation ===<br /> Attention to features of the task domain as a knowledge component is processed leads to associating those features with the knowledge component. If the features are valid, then forming or strengthening such associations facilitates retrieval during subsequent assessment or instruction, and thus leads to more robust learning. However, the robustness is also dependent on the scheduling of learning events that promotes the long term retentiono of the associations.<br /> <br /> === Descendents ===<br /> <br /> * [[Using syntactic priming to increase robust learning]] (de Jong, Perfetti, DeKeyser)<br /> <br /> * [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br /> <br /> * [[Basic skills training|French dictation training]] (MacWhinney)<br /> <br /> *[[French gender cues]] (Presson-MacWhinney)<br /> <br /> *[[Chinese pinyin dictation]] (Zhang-MacWhinney)<br /> <br /> *[[Japanese fluency]] (Yoshimura-MacWhinney)<br /> <br /> * [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br /> <br /> * [[Optimizing the practice schedule]] (Pavlik-MacWhinney)<br /> <br /> * [[Semantic grouping during vocabulary training]] (Tokowicz-Degani)<br /> <br /> *[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven &amp; McLaren)]] [Was in Coordinative Learning and in Interactive Communication]<br /> <br /> *[[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br /> <br /> * [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br /> <br /> * [[arithmetical fluency project]] (Fiez)<br /> <br /> * [[HandwritingEquationSolving|A multimodal (handwriting) interface for solving equations]] (Anthony, Yang, &amp; Koedinger) [Was in CL]<br /> <br /> === Annotated bibliography ===<br /> Forthcoming<br /> <br /> [[Category:Cluster]]</div>

Perfetti