Theory Wiki - User contributions [en]

Learning event space

2007-01-04T18:01:56Z

Adele:

[[Category:Glossary]]
[[Category:PSLC General]]
The set of paths that students have available for a particular learning event (from The PSLC Theoretical Framework
June 15, 2006).

Root node

2007-01-04T17:19:07Z

Adele: /* Explanation */

== PSLC theoretical hierarchy’s Root Node ==

=== Abstract ===
PSLC research is primarily concerned with finding out what instructional environments, methods or activities causes students’ learning to be robust. Although normal learning can be measured with immediate, near-transfer post-tests, we measure [[robustness]] with three addition measures: [[long-term retention]], far [[transfer]] and [[accelerated future learning]].

=== Glossary ===

[[:Category:PSLC General|PSLC General Glossary]]

[[:Category:Glossary|Full Glossary]]

=== Research question ===
What instructional activities or methods cause students’ learning to be robust?

=== Dependent variables ===
Measures of basic learning (an immediate, near-transfer post-test) and measures of [[robust learning]] (long-term retention, far transfer and accelerated future learning)

=== Independent variables ===
Independent variables in PSLC are primarily instructional activities, methods, or treatments. Studies might also include independent variables that measure individual differences, like a language students' first language.

=== Hypotheses ===
Learning will be robust if the instructional activities are designed to include appropriate paths, and the students tend to follow those paths during instruction.

=== Explanation ===
Instructional activities influence the depth and generality of the students’ acquired [[knowledge component]]s, the knowledge components’ strength and [[feature validity]], and the student’s motivation. These in turn influence the students’ performance on measures of [[robust learning]]. That is, we take a cognitive stance, rather than a radically distributed or situated stance.

At the macro-level, instruction produces robust learning if it increases the frequency of:
*[[sense making]] processes: rederivation, adaptation and self-supervised learning
*and [[foundational skill building]] processes: strengthening, deep feature perception and [[cognitive headroom]].

At the micro-level, instruction produces robust learning if:
*The instruction is designed so that the [[learning event space]] has some target paths that would cause an ideal student to acquire knowledge that is deep, general, strong and retrieval-feature-valid.
*Most students follow a target path most of the time. There are many factors outside the easy control of the experimenter or instructor, such as motivation and recall, that affect whether students actually follow the target paths designed into the instruction.

=== Descendents ===
*[[Interactive Communication]].
*[[Coordinative Learning]].
*[[Refinement and Fluency]].

=== Annotated bibliography ===
Forthcoming.

Root node

2007-01-04T17:18:18Z

Adele: /* Explanation */

== PSLC theoretical hierarchy’s Root Node ==

=== Abstract ===
PSLC research is primarily concerned with finding out what instructional environments, methods or activities causes students’ learning to be robust. Although normal learning can be measured with immediate, near-transfer post-tests, we measure [[robustness]] with three addition measures: [[long-term retention]], far [[transfer]] and [[accelerated future learning]].

=== Glossary ===

[[:Category:PSLC General|PSLC General Glossary]]

[[:Category:Glossary|Full Glossary]]

=== Research question ===
What instructional activities or methods cause students’ learning to be robust?

=== Dependent variables ===
Measures of basic learning (an immediate, near-transfer post-test) and measures of [[robust learning]] (long-term retention, far transfer and accelerated future learning)

=== Independent variables ===
Independent variables in PSLC are primarily instructional activities, methods, or treatments. Studies might also include independent variables that measure individual differences, like a language students' first language.

=== Hypotheses ===
Learning will be robust if the instructional activities are designed to include appropriate paths, and the students tend to follow those paths during instruction.

=== Explanation ===
Instructional activities influence the depth and generality of the students’ acquired [[knowledge component]]s, the knowledge components’ strength and [[feature validity]], and the student’s motivation. These in turn influence the students’ performance on measures of [[robust learning]]. That is, we take a cognitive stance, rather than a radically distributed or situated stance.

At the macro-level, instruction produces robust learning if it increases the frequency of:
*[[sense making]] processes: rederivation, adaptation and self-supervised learning
*and [[foundational skill building]] processes: strengthening, deep feature perception and [[cognitive headroom]].

At the micro-level, instruction produces robust learning if:
*The instruction is designed so that the learning event space has some target paths that would cause an ideal student to acquire knowledge that is deep, general, strong and retrieval-feature-valid.
*Most students follow a target path most of the time. There are many factors outside the easy control of the experimenter or instructor, such as motivation and recall, that affect whether students actually follow the target paths designed into the instruction.

=== Descendents ===
*[[Interactive Communication]].
*[[Coordinative Learning]].
*[[Refinement and Fluency]].

=== Annotated bibliography ===
Forthcoming.

Root node

2007-01-04T17:17:48Z

Adele: /* Explanation */

== PSLC theoretical hierarchy’s Root Node ==

=== Abstract ===
PSLC research is primarily concerned with finding out what instructional environments, methods or activities causes students’ learning to be robust. Although normal learning can be measured with immediate, near-transfer post-tests, we measure [[robustness]] with three addition measures: [[long-term retention]], far [[transfer]] and [[accelerated future learning]].

=== Glossary ===

[[:Category:PSLC General|PSLC General Glossary]]

[[:Category:Glossary|Full Glossary]]

=== Research question ===
What instructional activities or methods cause students’ learning to be robust?

=== Dependent variables ===
Measures of basic learning (an immediate, near-transfer post-test) and measures of [[robust learning]] (long-term retention, far transfer and accelerated future learning)

=== Independent variables ===
Independent variables in PSLC are primarily instructional activities, methods, or treatments. Studies might also include independent variables that measure individual differences, like a language students' first language.

=== Hypotheses ===
Learning will be robust if the instructional activities are designed to include appropriate paths, and the students tend to follow those paths during instruction.

=== Explanation ===
Instructional activities influence the depth and generality of the students’ acquired [[knowledge components]], the knowledge components’ strength and [[feature validity]], and the student’s motivation. These in turn influence the students’ performance on measures of [[robust learning]]. That is, we take a cognitive stance, rather than a radically distributed or situated stance.

At the macro-level, instruction produces robust learning if it increases the frequency of:
*[[sense making]] processes: rederivation, adaptation and self-supervised learning
*and [[foundational skill building]] processes: strengthening, deep feature perception and [[cognitive headroom]].

At the micro-level, instruction produces robust learning if:
*The instruction is designed so that the learning event space has some target paths that would cause an ideal student to acquire knowledge that is deep, general, strong and retrieval-feature-valid.
*Most students follow a target path most of the time. There are many factors outside the easy control of the experimenter or instructor, such as motivation and recall, that affect whether students actually follow the target paths designed into the instruction.

=== Descendents ===
*[[Interactive Communication]].
*[[Coordinative Learning]].
*[[Refinement and Fluency]].

=== Annotated bibliography ===
Forthcoming.

Root node

2007-01-04T17:16:54Z

Adele: /* Dependent variables */

== PSLC theoretical hierarchy’s Root Node ==

=== Abstract ===
PSLC research is primarily concerned with finding out what instructional environments, methods or activities causes students’ learning to be robust. Although normal learning can be measured with immediate, near-transfer post-tests, we measure [[robustness]] with three addition measures: [[long-term retention]], far [[transfer]] and [[accelerated future learning]].

=== Glossary ===

[[:Category:PSLC General|PSLC General Glossary]]

[[:Category:Glossary|Full Glossary]]

=== Research question ===
What instructional activities or methods cause students’ learning to be robust?

=== Dependent variables ===
Measures of basic learning (an immediate, near-transfer post-test) and measures of [[robust learning]] (long-term retention, far transfer and accelerated future learning)

=== Independent variables ===
Independent variables in PSLC are primarily instructional activities, methods, or treatments. Studies might also include independent variables that measure individual differences, like a language students' first language.

=== Hypotheses ===
Learning will be robust if the instructional activities are designed to include appropriate paths, and the students tend to follow those paths during instruction.

=== Explanation ===
Instructional activities influence the depth and generality of the students’ acquired knowledge components, the knowledge components’ strength and [[feature validity]], and the student’s motivation. These in turn influence the students’ performance on measures of [[robust learning]]. That is, we take a cognitive stance, rather than a radically distributed or situated stance.

At the macro-level, instruction produces robust learning if it increases the frequency of:
*[[sense making]] processes: rederivation, adaptation and self-supervised learning
*and [[foundational skill building]] processes: strengthening, deep feature perception and [[cognitive headroom]].

At the micro-level, instruction produces robust learning if:
*The instruction is designed so that the learning event space has some target paths that would cause an ideal student to acquire knowledge that is deep, general, strong and retrieval-feature-valid.
*Most students follow a target path most of the time. There are many factors outside the easy control of the experimenter or instructor, such as motivation and recall, that affect whether students actually follow the target paths designed into the instruction.

=== Descendents ===
*[[Interactive Communication]].
*[[Coordinative Learning]].
*[[Refinement and Fluency]].

=== Annotated bibliography ===
Forthcoming.

Root node

2007-01-04T17:16:05Z

Adele: /* Abstract */

== PSLC theoretical hierarchy’s Root Node ==

=== Abstract ===
PSLC research is primarily concerned with finding out what instructional environments, methods or activities causes students’ learning to be robust. Although normal learning can be measured with immediate, near-transfer post-tests, we measure [[robustness]] with three addition measures: [[long-term retention]], far [[transfer]] and [[accelerated future learning]].

=== Glossary ===

[[:Category:PSLC General|PSLC General Glossary]]

[[:Category:Glossary|Full Glossary]]

=== Research question ===
What instructional activities or methods cause students’ learning to be robust?

=== Dependent variables ===
Measures of basic learning (an immediate, near-transfer post-test) and measures of [[robust learning]] (retention, far-transfer and preparation for future learning)

=== Independent variables ===
Independent variables in PSLC are primarily instructional activities, methods, or treatments. Studies might also include independent variables that measure individual differences, like a language students' first language.

=== Hypotheses ===
Learning will be robust if the instructional activities are designed to include appropriate paths, and the students tend to follow those paths during instruction.

=== Explanation ===
Instructional activities influence the depth and generality of the students’ acquired knowledge components, the knowledge components’ strength and [[feature validity]], and the student’s motivation. These in turn influence the students’ performance on measures of [[robust learning]]. That is, we take a cognitive stance, rather than a radically distributed or situated stance.

At the macro-level, instruction produces robust learning if it increases the frequency of:
*[[sense making]] processes: rederivation, adaptation and self-supervised learning
*and [[foundational skill building]] processes: strengthening, deep feature perception and [[cognitive headroom]].

At the micro-level, instruction produces robust learning if:
*The instruction is designed so that the learning event space has some target paths that would cause an ideal student to acquire knowledge that is deep, general, strong and retrieval-feature-valid.
*Most students follow a target path most of the time. There are many factors outside the easy control of the experimenter or instructor, such as motivation and recall, that affect whether students actually follow the target paths designed into the instruction.

=== Descendents ===
*[[Interactive Communication]].
*[[Coordinative Learning]].
*[[Refinement and Fluency]].

=== Annotated bibliography ===
Forthcoming.

Robustness

2007-01-04T17:14:10Z

Adele:

[[Category:Glossary]]
[[Category:PSLC General]]

see [[robust learning]]

Refinement and Fluency

2007-01-04T17:05:44Z

Adele: /* Significance */

== The PSLC Refinement and Fluency cluster ==

=== Abstract ===
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.

1. task analysis: To design effective instruction, we must analyze learning tasks into their simplest components.

2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.

3. scheduling of practice: The optimal scheduling of practice uses principles of memory [[consolidation]] to maximize robust learning and achieve mastery.

4. [[explicit instruction]]: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.

5. [[implicit instruction]]: On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.

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.

7. [[cue validity]]: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.

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.

9. learning to learn: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.

10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.

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.

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.

===Significance===
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.

=== Glossary ===
[[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.

=== Research question ===
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 teaching events relative to implicit learning opportunities, and the role of feedback.

=== Independent variables ===
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.

=== Dependent variables ===
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.

=== Hypotheses ===
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.

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:

1. scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.

2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems.

3. explicit instruction hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.

4. implicit instruction hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.

5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.

6. cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.

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.

8. learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.

9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.

=== Explanation ===
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.

=== Descendents ===

* [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)

* [[Learning the role of radicals in reading Chinese]] (Liu et al.)

* [[Basic skills training|French dictation training]] (MacWhinney)

*[[French gender cues]] (Presson-MacWhinney)

*[[Chinese pinyin dictation]] (Zhang-MacWhinney)

*[[Japanese fluency]] (Yoshimura-MacWhinney)

* [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)

* [[Optimizing the practice schedule]] (Pavlik-MacWhinney)

* [[Semantic grouping during vocabulary training]] (Tokowicz-Degani)

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Was in Coordinative Learning and in Interactive Communication]

*[[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]

* [[Mental rotations during vocabulary training]] (Tokowicz-Degani)

* [[arithmetical fluency project]] (Fiez)

* [[HandwritingEquationSolving|A multimodal (handwriting) interface for solving equations]] (Anthony, Yang, & Koedinger) [Was in CL]

* [[Fostering fluency in second language learning]] (De Jong, Perfetti)

=== Annotated bibliography ===
Forthcoming

[[Category:Cluster]]

Refinement and Fluency

2007-01-04T17:05:12Z

Adele: /* Abstract */

== The PSLC Refinement and Fluency cluster ==

=== Abstract ===
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.

1. task analysis: To design effective instruction, we must analyze learning tasks into their simplest components.

2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.

3. scheduling of practice: The optimal scheduling of practice uses principles of memory [[consolidation]] to maximize robust learning and achieve mastery.

4. [[explicit instruction]]: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.

5. [[implicit instruction]]: On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.

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.

7. [[cue validity]]: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.

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.

9. learning to learn: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.

10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.

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.

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.

===Significance===
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.

=== Glossary ===
[[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.

=== Research question ===
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 teaching events relative to implicit learning opportunities, and the role of feedback.

=== Independent variables ===
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.

=== Dependent variables ===
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.

=== Hypotheses ===
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.

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:

1. scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.

2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems.

3. explicit instruction hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.

4. implicit instruction hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.

5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.

6. cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.

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.

8. learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.

9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.

=== Explanation ===
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.

=== Descendents ===

* [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)

* [[Learning the role of radicals in reading Chinese]] (Liu et al.)

* [[Basic skills training|French dictation training]] (MacWhinney)

*[[French gender cues]] (Presson-MacWhinney)

*[[Chinese pinyin dictation]] (Zhang-MacWhinney)

*[[Japanese fluency]] (Yoshimura-MacWhinney)

* [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)

* [[Optimizing the practice schedule]] (Pavlik-MacWhinney)

* [[Semantic grouping during vocabulary training]] (Tokowicz-Degani)

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Was in Coordinative Learning and in Interactive Communication]

*[[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]

* [[Mental rotations during vocabulary training]] (Tokowicz-Degani)

* [[arithmetical fluency project]] (Fiez)

* [[HandwritingEquationSolving|A multimodal (handwriting) interface for solving equations]] (Anthony, Yang, & Koedinger) [Was in CL]

* [[Fostering fluency in second language learning]] (De Jong, Perfetti)

=== Annotated bibliography ===
Forthcoming

[[Category:Cluster]]

Interactive Communication

2007-01-04T17:01:49Z

Adele: /* Explanation */

== The PSLC Interactive Communication cluster ==

=== Abstract ===
The studies in the Interactive Communication deal primarily with learning environments where there are two agents, one of which is the student. The other agent is typically a second student, a human tutor or a tutoring system. They communicate, either in a natural language or a formal language, such as mathematical expression or menus. We are trying to find out why such instructional, dyadic, interactive communication is sometimes highly effective and sometimes less effective. Sometimes we study highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare whole forms of communciation. Our hypothesis is simply that interactive communication is effective if it introduces the right content, or it steers students to consider the right content, or it increases the effectiveness of the cognitive processes used in encoding or studying a piece of content.

=== Background and Significance ===
Instructional dialogue has mostly been studied in classrooms (e.g., Lave & Wenger, 1991; Leinhardt, 1990) and workplaces (e.g., Hutchins, 1995; Nunes, Schliemann & Carraher, 1993). In order to investigate more tractable albeit still complex situations, most of our work focuses on dyadic dialogues, namely dialogues between: (a) a human tutor and a human student, (b) two human students, or (c) A computer tutor and a human student. Moreover, the dialogue are task-oriented (Grosz & Sidner, 19??) in that the participants are working together on a task rather than simply conversing with no shared goals or with opposing goals.

Given that many studies of the structure of dyadic instructional dialogue exist (e.g., Fox, 1993; Graesser, Person & Magliano, 1995; MacArthur, Stasz, & Zmuidzinas, 1990), we are focusing on what properties of interactive communication promote [[robust learning]]. Earlier studies (e.g., VanLehn, Graesser et al., in press; Katz, Connelly & Allbritton, 2003; Evens & Michael, 2006; Cohen, Kulik & Kulik, 1982) found surprisingly mixed results. Although most studies showed that interactive communication was more effective than less interactive instruction, it was not always better. Thus, the next step in this important line of research is to determine when different types of interactive communication are effective and why.

=== Glossary ===
To be developed, but will probably include:

* ''Agent'': Something that can perform the instructional activity. Typically a student, a tutor, a tutoring system or a simulated student. In the extreme case, an agent can be a passive medium, such as text or a video, that presents a performance of the activity. For instance, if the instructional activity is solving physics problems, then a worked example, such as the ones shown in a textbook, is an agent.

* ''Communication''.

* ''Initiative''. This measures the ratio of the work initiated by the two agents. A dialogue with lots of student initiative is one where the student spontaneously initiates work on the activity. A dialogue with lots of tutor initiative is one where the tutor either does the work or requests (in the speech act sense of “request”) the student to do the work. The “initiative” term comes from linguistics, whereas a synonymous distinction, learn control vs. teacher control, comes from education.

* ''Zone of proximal development''.
1) When instruction is laid out on a scale of difficulty from easy to hard, there is a region where the instruction is too hard for the student to learn effectively from it without help, but still just easy enough that the student can learn if given help, typically from a second agent. This region is called the zone of proximal development (ZPD), a term from developmental psychology.

2) Vygotsky (1978) maintained the child follows the adult's example and gradually develops the ability to do certain tasks without help or assistance. He called the difference between what a child can do with help and what he or she can do without guidance the "zone of proximal development" (ZPD). (sourc-North Central Regional Educational Laboratory)

3) The gap between a learner's current or actual development level determined by independent problem-solving and the learner's emerging or potential level of development.

=== Research question ===
The research problem addressed by this cluster is: What properties of interactive communication promote robust learning?

=== Independent ===
* Some studies in the Interactive Communication cluster examine the impact on robust learning of different types of interactive communication, by contrasting two forms of interactive communication. Examples include compare scripted vs. unscripted peer collaborative problem solving.

* Other studies compare instruction with and without specific kinds interactive communication, e.g., by having students work alone or in pairs, or comparing [[self-explanation]] done alone to dyadic, interactive explanation generation.

* A third class of manipulation holds most of an interactive communication constant and varies only a small part of it. For instance, a video may be viewed with and without interactive prompts inserted at key points. It should perhaps be noted that, as in the physical sciences, this study-it-in-isolation strategy is risky. Just as a heart extracted from an animal doesn’t behave exactly like one that still resides in the animal, the process studied in isolation may not behave exactly like the one that occurs in interactive communication. Nonetheless, significant progress has been made in the physical sciences by using this isolation strategy, so it may help the science of learning as well.

=== Dependent variables ===
Measures of normal and robust learning.

=== Hypothesis ===
When student engage in collaborative learning with another agent, the learning will be more robust is the manipulation either (1) introduces essential content, or (2) guides students to cover content that they would otherwise avoid, or (3) increases the effectiveness of the cognitive processes used to encode or study the content. These 3 methods are described in more detail below.

=== Explanation ===
If we view a short episode of interactive communication as a [[learning event space]], there could be three reasons why one treatment might be more effective than another:

(1) The learning event spaces might have different paths with different content. For instance, if one person contributes critical information that the other person lacks, then their joint learning event space has paths that are absent in the learning event space of the second person if that person were working solo. That is, the ''topology'' of one space might be better than the topology of the other.

(2) If the learning event spaces in the two conditions are the same, then the interactive communication treatment might cause the students to traverse different paths than the control students. That is, the ''path choices'' of one treatment might be better than the path choices of the other.

(3) If the learning event spaces are the same and the students take the same paths, they still might learn more in one condition than another because of the way that they traversed the path. For instance, having a partner observe the student as the student traverse a path might cause the student to be more attentive to details and to remember more. That is, the ''path effects'' might differ in the treatment vs. the control.

=== Descendents ===

*[[Craig_questions|Deep-level questions during example studying (Craig & Chi)]]

*[[Craig_observing|Learning from Problem Solving while Observing Worked Examples (Craig Gadgil, & Chi)]]

*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]

*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]]

*[[Hausmann_Diss|The effects of elaborative dialog on problem solving and learning (Hausmann & Chi, 2005)]]

*[[Reflective Dialogues (Katz)]]

*[[Post-practice reflection (Katz)]]

*[[Rummel_Scripted_Collaborative_Problem_Solving|Collaborative Extensions to the Cognitive Tutor Algebra: Scripted Collaborative Problem Solving (Rummel, Diziol, McLaren, & Spada)]]

*[[Walker_A_Peer_Tutoring_Addition|Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition (Walker, McLaren, Koedinger, & Rummel)]]

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Moved to Refinement and Fluency, Was in Coordinative Learning]

*[[FrenchCulture|Understanding culture from film (Ogan, Aleven & Jones)]]

*[[Does learning from worked-out examples improve tutored problem solving?]] (Renkl, Aleven & Salden) [Was in Coordinative Learning]

*[[Visual-Verbal Learning (Aleven & Butcher Project) | Visual-Verbal Learning (Aleven & Butcher)]] -- ''Elaborated Explanation condition is the relevant manipulation''

*The self-correction of speech errors (McCormick, O’Neill & Siskin) [Was in Fluency and in Coordinative Learning]

*[[Ringenberg_Examples-as-Help | Scaffolding Problem Solving with Embedded Example to Promote Deep Learning (Ringenberg & VanLehn)]]

=== Annotated bibliography ===
Forthcoming

[[Category:Cluster]]

Interactive Communication

2007-01-04T17:00:34Z

Adele: /* Background and Significance */

== The PSLC Interactive Communication cluster ==

=== Abstract ===
The studies in the Interactive Communication deal primarily with learning environments where there are two agents, one of which is the student. The other agent is typically a second student, a human tutor or a tutoring system. They communicate, either in a natural language or a formal language, such as mathematical expression or menus. We are trying to find out why such instructional, dyadic, interactive communication is sometimes highly effective and sometimes less effective. Sometimes we study highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare whole forms of communciation. Our hypothesis is simply that interactive communication is effective if it introduces the right content, or it steers students to consider the right content, or it increases the effectiveness of the cognitive processes used in encoding or studying a piece of content.

=== Background and Significance ===
Instructional dialogue has mostly been studied in classrooms (e.g., Lave & Wenger, 1991; Leinhardt, 1990) and workplaces (e.g., Hutchins, 1995; Nunes, Schliemann & Carraher, 1993). In order to investigate more tractable albeit still complex situations, most of our work focuses on dyadic dialogues, namely dialogues between: (a) a human tutor and a human student, (b) two human students, or (c) A computer tutor and a human student. Moreover, the dialogue are task-oriented (Grosz & Sidner, 19??) in that the participants are working together on a task rather than simply conversing with no shared goals or with opposing goals.

Given that many studies of the structure of dyadic instructional dialogue exist (e.g., Fox, 1993; Graesser, Person & Magliano, 1995; MacArthur, Stasz, & Zmuidzinas, 1990), we are focusing on what properties of interactive communication promote [[robust learning]]. Earlier studies (e.g., VanLehn, Graesser et al., in press; Katz, Connelly & Allbritton, 2003; Evens & Michael, 2006; Cohen, Kulik & Kulik, 1982) found surprisingly mixed results. Although most studies showed that interactive communication was more effective than less interactive instruction, it was not always better. Thus, the next step in this important line of research is to determine when different types of interactive communication are effective and why.

=== Glossary ===
To be developed, but will probably include:

* ''Agent'': Something that can perform the instructional activity. Typically a student, a tutor, a tutoring system or a simulated student. In the extreme case, an agent can be a passive medium, such as text or a video, that presents a performance of the activity. For instance, if the instructional activity is solving physics problems, then a worked example, such as the ones shown in a textbook, is an agent.

* ''Communication''.

* ''Initiative''. This measures the ratio of the work initiated by the two agents. A dialogue with lots of student initiative is one where the student spontaneously initiates work on the activity. A dialogue with lots of tutor initiative is one where the tutor either does the work or requests (in the speech act sense of “request”) the student to do the work. The “initiative” term comes from linguistics, whereas a synonymous distinction, learn control vs. teacher control, comes from education.

* ''Zone of proximal development''.
1) When instruction is laid out on a scale of difficulty from easy to hard, there is a region where the instruction is too hard for the student to learn effectively from it without help, but still just easy enough that the student can learn if given help, typically from a second agent. This region is called the zone of proximal development (ZPD), a term from developmental psychology.

2) Vygotsky (1978) maintained the child follows the adult's example and gradually develops the ability to do certain tasks without help or assistance. He called the difference between what a child can do with help and what he or she can do without guidance the "zone of proximal development" (ZPD). (sourc-North Central Regional Educational Laboratory)

3) The gap between a learner's current or actual development level determined by independent problem-solving and the learner's emerging or potential level of development.

=== Research question ===
The research problem addressed by this cluster is: What properties of interactive communication promote robust learning?

=== Independent ===
* Some studies in the Interactive Communication cluster examine the impact on robust learning of different types of interactive communication, by contrasting two forms of interactive communication. Examples include compare scripted vs. unscripted peer collaborative problem solving.

* Other studies compare instruction with and without specific kinds interactive communication, e.g., by having students work alone or in pairs, or comparing [[self-explanation]] done alone to dyadic, interactive explanation generation.

* A third class of manipulation holds most of an interactive communication constant and varies only a small part of it. For instance, a video may be viewed with and without interactive prompts inserted at key points. It should perhaps be noted that, as in the physical sciences, this study-it-in-isolation strategy is risky. Just as a heart extracted from an animal doesn’t behave exactly like one that still resides in the animal, the process studied in isolation may not behave exactly like the one that occurs in interactive communication. Nonetheless, significant progress has been made in the physical sciences by using this isolation strategy, so it may help the science of learning as well.

=== Dependent variables ===
Measures of normal and robust learning.

=== Hypothesis ===
When student engage in collaborative learning with another agent, the learning will be more robust is the manipulation either (1) introduces essential content, or (2) guides students to cover content that they would otherwise avoid, or (3) increases the effectiveness of the cognitive processes used to encode or study the content. These 3 methods are described in more detail below.

=== Explanation ===
If we view a short episode of interactive communication as a learning event space, there could be three reasons why one treatment might be more effective than another:

(1) The learning event spaces might have different paths with different content. For instance, if one person contributes critical information that the other person lacks, then their joint learning event space has paths that are absent in the learning event space of the second person if that person were working solo. That is, the ''topology'' of one space might be better than the topology of the other.

(2) If the learning event spaces in the two conditions are the same, then the interactive communication treatment might cause the students to traverse different paths than the control students. That is, the ''path choices'' of one treatment might be better than the path choices of the other.

(3) If the learning event spaces are the same and the students take the same paths, they still might learn more in one condition than another because of the way that they traversed the path. For instance, having a partner observe the student as the student traverse a path might cause the student to be more attentive to details and to remember more. That is, the ''path effects'' might differ in the treatment vs. the control.

=== Descendents ===

*[[Craig_questions|Deep-level questions during example studying (Craig & Chi)]]

*[[Craig_observing|Learning from Problem Solving while Observing Worked Examples (Craig Gadgil, & Chi)]]

*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]

*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]]

*[[Hausmann_Diss|The effects of elaborative dialog on problem solving and learning (Hausmann & Chi, 2005)]]

*[[Reflective Dialogues (Katz)]]

*[[Post-practice reflection (Katz)]]

*[[Rummel_Scripted_Collaborative_Problem_Solving|Collaborative Extensions to the Cognitive Tutor Algebra: Scripted Collaborative Problem Solving (Rummel, Diziol, McLaren, & Spada)]]

*[[Walker_A_Peer_Tutoring_Addition|Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition (Walker, McLaren, Koedinger, & Rummel)]]

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Moved to Refinement and Fluency, Was in Coordinative Learning]

*[[FrenchCulture|Understanding culture from film (Ogan, Aleven & Jones)]]

*[[Does learning from worked-out examples improve tutored problem solving?]] (Renkl, Aleven & Salden) [Was in Coordinative Learning]

*[[Visual-Verbal Learning (Aleven & Butcher Project) | Visual-Verbal Learning (Aleven & Butcher)]] -- ''Elaborated Explanation condition is the relevant manipulation''

*The self-correction of speech errors (McCormick, O’Neill & Siskin) [Was in Fluency and in Coordinative Learning]

*[[Ringenberg_Examples-as-Help | Scaffolding Problem Solving with Embedded Example to Promote Deep Learning (Ringenberg & VanLehn)]]

=== Annotated bibliography ===
Forthcoming

[[Category:Cluster]]

Coordinative Learning

2007-01-04T16:57:19Z

Adele: /* Independent variables */

== The PSLC Coordinative Learning cluster ==

=== Abstract ===
The studies in the Coordinative Learning cluster tend to focus on varying ''a)'' the types of information available to learning or ''b)'' the instructional methods that they employ. In particular, the studies focus on the impact of having learners coordinate two or more types. Given that the student has multiple [[sources]]/methods available, two factors that might impact learning are:

*What is the relationship between the content in the two sources or the content generated by the two methods? Our hypothesis is that the two sources or methods facilitate [[robust learning]] when a [[knowledge component]] is difficult to understand or absent in one and is present or easier to understand in the other.
*When and how does the student coordinate between the two sources or methods? Our hypothesis is that students should be encouraged to compare the two, perhaps by putting them close together in space or time.

At the micro-level, the overall hypothesis is that robust learning occurs when the [[learning event space]] has target paths whose [[sense making]] difficulties complement each other (as expressed in the first bullet above) and the students make path choices that take advantage of these [[complementary]] paths (as in the second bullet, above). This hypothesis is just a specialization of the [[Root_node|general PSLC hypothesis]] to this cluster.

=== Glossary ===
[[:Category:Coordinative Learning|Coordinative Learning]] glossary.

*'''[[Co-training]]'''
*'''[[Complementary]]'''
*'''[[Conceptual tasks]]'''
*'''[[Contiguity]]'''
*'''[[Coordination]]'''
*'''[[External representations]]'''
*'''[[Input sources ]]'''
*'''[[Instructional method]]'''
*'''[[Multimedia sources]]'''
*'''[[Procedural tasks]]'''
*'''[[Self-explanation]]'''
*'''[[Self-supervised learning]]'''
*'''[[Sources]]'''
*'''[[Strategies]]'''
*'''[[Unlabeled examples]]'''

=== Research question ===

When and how does coordinating multiple sources of information or lines of reasoning increase robust learning?

=== Independent variables ===

*Content of the sources (e.g., pictures, diagrams, written text)

*Instructional activities designed to engage students in [[coordination]] (e.g., conceptual vs. [[procedural]] exercises, contiguous presentation of sources, [[self-explanation]])

=== Dependent variables ===
Measures of normal and robust learning.

=== Hypotheses ===
When students are given sources/methods whose [[sense making]] difficulties are complementary and they are engaged in coordinating the sources/methods, then their learning will be more robust than it would otherwise be.

=== Explanation ===

There are both [[sense making]] and [[foundational skill building]] explanations. From the sense making perspective, if the sources/methods yield complementary content and the student is engaged in coordinating them, then the student is more likely to successfully understand the instruction because if a student fails to understand one of the sources/methods, he can use the second to make sense of the first. From a foundational skill building perspective, attending to both sources/methods simultaneously associates [[features]] from both with the learned knowledge components, thus potentially increasing feature validity and hence robust learning.

=== Descendents ===

*[[Visual-Verbal Learning (Aleven & Butcher Project) | Visual-verbal learning in geometry (Aleven & Butcher)]]
*[[Help_Lite (Aleven, Roll)|Hints during tutored problem solving – the effect of fewer hint levels with greater conceptual content (Aleven & Roll)]]
*[[HandwritingEquationSolving | Handwriting in algebra learning (Anthony, Yang & Koedinger)]]
*[[Note-Taking: Restriction and Selection | Note-taking technologies (Bauer & Koedinger)]]
*[[Booth | Knowledge component construction vs. recall (Booth, Siegler, Koedinger & Rittle-Johnson)]]
*[[Visual Representations in Science Learning | Visual Representations in Science Learning (Davenport, Klahr & Koedinger)]]
*[http://www.pitt.edu/~liuying/Node_cotraining.html Co-training of Chinese characters (Liu, Perfetti, Dunlap, Zi, Mitchell)]
*[[Stoichiometry_Study | Studying the Learning Effect of Personalization and Worked Examples in the Solving of Stoichiometry Problems (McLaren, Koedinger & Yaron)]]
*[[REAP_main | The REAP Project: Implicit and explicit instruction on word meanings (Juffs & Eskenazi)]]
*[http://www.pitt.edu/~liuying/Node_talkinghead.html Learning Chinese pronunciation from a “talking head” (Liu, Massaro, Dunlap, Wu, Chen,Chan, Perfetti)] [Was in Fluency]

=== Annotated Bibliography ===
Forthcoming

[[Category:Cluster]]

Coordinative Learning

2007-01-04T16:56:23Z

Adele: /* Abstract */

== The PSLC Coordinative Learning cluster ==

=== Abstract ===
The studies in the Coordinative Learning cluster tend to focus on varying ''a)'' the types of information available to learning or ''b)'' the instructional methods that they employ. In particular, the studies focus on the impact of having learners coordinate two or more types. Given that the student has multiple [[sources]]/methods available, two factors that might impact learning are:

*What is the relationship between the content in the two sources or the content generated by the two methods? Our hypothesis is that the two sources or methods facilitate [[robust learning]] when a [[knowledge component]] is difficult to understand or absent in one and is present or easier to understand in the other.
*When and how does the student coordinate between the two sources or methods? Our hypothesis is that students should be encouraged to compare the two, perhaps by putting them close together in space or time.

At the micro-level, the overall hypothesis is that robust learning occurs when the [[learning event space]] has target paths whose [[sense making]] difficulties complement each other (as expressed in the first bullet above) and the students make path choices that take advantage of these [[complementary]] paths (as in the second bullet, above). This hypothesis is just a specialization of the [[Root_node|general PSLC hypothesis]] to this cluster.

=== Glossary ===
[[:Category:Coordinative Learning|Coordinative Learning]] glossary.

*'''[[Co-training]]'''
*'''[[Complementary]]'''
*'''[[Conceptual tasks]]'''
*'''[[Contiguity]]'''
*'''[[Coordination]]'''
*'''[[External representations]]'''
*'''[[Input sources ]]'''
*'''[[Instructional method]]'''
*'''[[Multimedia sources]]'''
*'''[[Procedural tasks]]'''
*'''[[Self-explanation]]'''
*'''[[Self-supervised learning]]'''
*'''[[Sources]]'''
*'''[[Strategies]]'''
*'''[[Unlabeled examples]]'''

=== Research question ===

When and how does coordinating multiple sources of information or lines of reasoning increase robust learning?

=== Independent variables ===

*Content of the sources (e.g., pictures, diagrams, written text)

*Instructional activities designed to engage students in coordination (e.g., conceptual vs. [[procedural]] exercises, contiguous presentation of sources, self-explanation)

=== Dependent variables ===
Measures of normal and robust learning.

=== Hypotheses ===
When students are given sources/methods whose [[sense making]] difficulties are complementary and they are engaged in coordinating the sources/methods, then their learning will be more robust than it would otherwise be.

=== Explanation ===

There are both [[sense making]] and [[foundational skill building]] explanations. From the sense making perspective, if the sources/methods yield complementary content and the student is engaged in coordinating them, then the student is more likely to successfully understand the instruction because if a student fails to understand one of the sources/methods, he can use the second to make sense of the first. From a foundational skill building perspective, attending to both sources/methods simultaneously associates [[features]] from both with the learned knowledge components, thus potentially increasing feature validity and hence robust learning.

=== Descendents ===

*[[Visual-Verbal Learning (Aleven & Butcher Project) | Visual-verbal learning in geometry (Aleven & Butcher)]]
*[[Help_Lite (Aleven, Roll)|Hints during tutored problem solving – the effect of fewer hint levels with greater conceptual content (Aleven & Roll)]]
*[[HandwritingEquationSolving | Handwriting in algebra learning (Anthony, Yang & Koedinger)]]
*[[Note-Taking: Restriction and Selection | Note-taking technologies (Bauer & Koedinger)]]
*[[Booth | Knowledge component construction vs. recall (Booth, Siegler, Koedinger & Rittle-Johnson)]]
*[[Visual Representations in Science Learning | Visual Representations in Science Learning (Davenport, Klahr & Koedinger)]]
*[http://www.pitt.edu/~liuying/Node_cotraining.html Co-training of Chinese characters (Liu, Perfetti, Dunlap, Zi, Mitchell)]
*[[Stoichiometry_Study | Studying the Learning Effect of Personalization and Worked Examples in the Solving of Stoichiometry Problems (McLaren, Koedinger & Yaron)]]
*[[REAP_main | The REAP Project: Implicit and explicit instruction on word meanings (Juffs & Eskenazi)]]
*[http://www.pitt.edu/~liuying/Node_talkinghead.html Learning Chinese pronunciation from a “talking head” (Liu, Massaro, Dunlap, Wu, Chen,Chan, Perfetti)] [Was in Fluency]

=== Annotated Bibliography ===
Forthcoming

[[Category:Cluster]]

Coordinative Learning

2007-01-04T16:54:15Z

Adele: /* Abstract */

== The PSLC Coordinative Learning cluster ==

=== Abstract ===
The studies in the Coordinative Learning cluster tend to focus on varying ''a)'' the types of information available to learning or ''b)'' the instructional methods that they employ. In particular, the studies focus on the impact of having learners coordinate two or more types. Given that the student has multiple [[sources]]/methods available, two factors that might impact learning are:

*What is the relationship between the content in the two sources or the content generated by the two methods? Our hypothesis is that the two sources or methods facilitate [[robust learning]] when a [[knowledge component]] is difficult to understand or absent in one and is present or easier to understand in the other.
*When and how does the student coordinate between the two sources or methods? Our hypothesis is that students should be encouraged to compare the two, perhaps by putting them close together in space or time.

At the micro-level, the overall hypothesis is that robust learning occurs when the learning event space has target paths whose [[sense making]] difficulties complement each other (as expressed in the first bullet above) and the students make path choices that take advantage of these [[complementary]] paths (as in the second bullet, above). This hypothesis is just a specialization of the [[Root_node|general PSLC hypothesis]] to this cluster.

=== Glossary ===
[[:Category:Coordinative Learning|Coordinative Learning]] glossary.

*'''[[Co-training]]'''
*'''[[Complementary]]'''
*'''[[Conceptual tasks]]'''
*'''[[Contiguity]]'''
*'''[[Coordination]]'''
*'''[[External representations]]'''
*'''[[Input sources ]]'''
*'''[[Instructional method]]'''
*'''[[Multimedia sources]]'''
*'''[[Procedural tasks]]'''
*'''[[Self-explanation]]'''
*'''[[Self-supervised learning]]'''
*'''[[Sources]]'''
*'''[[Strategies]]'''
*'''[[Unlabeled examples]]'''

=== Research question ===

When and how does coordinating multiple sources of information or lines of reasoning increase robust learning?

=== Independent variables ===

*Content of the sources (e.g., pictures, diagrams, written text)

*Instructional activities designed to engage students in coordination (e.g., conceptual vs. [[procedural]] exercises, contiguous presentation of sources, self-explanation)

=== Dependent variables ===
Measures of normal and robust learning.

=== Hypotheses ===
When students are given sources/methods whose [[sense making]] difficulties are complementary and they are engaged in coordinating the sources/methods, then their learning will be more robust than it would otherwise be.

=== Explanation ===

There are both [[sense making]] and [[foundational skill building]] explanations. From the sense making perspective, if the sources/methods yield complementary content and the student is engaged in coordinating them, then the student is more likely to successfully understand the instruction because if a student fails to understand one of the sources/methods, he can use the second to make sense of the first. From a foundational skill building perspective, attending to both sources/methods simultaneously associates [[features]] from both with the learned knowledge components, thus potentially increasing feature validity and hence robust learning.

=== Descendents ===

*[[Visual-Verbal Learning (Aleven & Butcher Project) | Visual-verbal learning in geometry (Aleven & Butcher)]]
*[[Help_Lite (Aleven, Roll)|Hints during tutored problem solving – the effect of fewer hint levels with greater conceptual content (Aleven & Roll)]]
*[[HandwritingEquationSolving | Handwriting in algebra learning (Anthony, Yang & Koedinger)]]
*[[Note-Taking: Restriction and Selection | Note-taking technologies (Bauer & Koedinger)]]
*[[Booth | Knowledge component construction vs. recall (Booth, Siegler, Koedinger & Rittle-Johnson)]]
*[[Visual Representations in Science Learning | Visual Representations in Science Learning (Davenport, Klahr & Koedinger)]]
*[http://www.pitt.edu/~liuying/Node_cotraining.html Co-training of Chinese characters (Liu, Perfetti, Dunlap, Zi, Mitchell)]
*[[Stoichiometry_Study | Studying the Learning Effect of Personalization and Worked Examples in the Solving of Stoichiometry Problems (McLaren, Koedinger & Yaron)]]
*[[REAP_main | The REAP Project: Implicit and explicit instruction on word meanings (Juffs & Eskenazi)]]
*[http://www.pitt.edu/~liuying/Node_talkinghead.html Learning Chinese pronunciation from a “talking head” (Liu, Massaro, Dunlap, Wu, Chen,Chan, Perfetti)] [Was in Fluency]

=== Annotated Bibliography ===
Forthcoming

[[Category:Cluster]]

Robust learning

2007-01-04T16:10:27Z

Adele:

[[Category:Glossary]]
[[Category:PSLC General]]

We use robust learning to refer to an outcome, that is, a desirable result of instruction. Robust learning is learning the achieves either or both deep conceptual understanding and strong procedural fluency. Sometimes instructional objectives of a course may put more emphasis on one or the other, but often both are desirable. Learning is robust if the acquired knowledge or skill meets at least one of the following three criteria:

• [[long-term retention]]: It is retained for long periods of time, at least for days and even for years.

• [[Transfer]]: It transfers, that is, it can be used in situations that differ significantly from the situations present during instruction.

• [[accelerated future learning]]: It accelerates future learning. That is, when related instruction is presented in the future, this knowledge allows them to learn more quickly and effectively learn from it(from The PSLC Theoretical Framework).

Instruction that achieves robust learning is designed so that the [[learning event space]] has some target paths that would cause an ideal student to acquire [[knowledge components]] that have either or both high [[feature validity]], that is, they are accurate, deep, and general, and high strength, that is, they can be applied quickly and effortlessly.

Normal post-test

2007-01-03T19:03:51Z

Adele:

[[Category:Glossary]]
[[Category:PSLC General]]

Immediately follows instruction. Items/problems/activities are “isomorphic” to those in instruction, have similar form, but may be different content.
(from Toward Tighter Theory-Study Connections: Robust Learning Metrics, Ken Koedinger)

Normal post-test

2007-01-03T19:03:29Z

Adele:

[[Category:Glossary]]
[[Category:PSLC General]]
[[Category:Intelligent Writing Tutor]]
Immediately follows instruction. Items/problems/activities are “isomorphic” to those in instruction, have similar form, but may be different content.
(from Toward Tighter Theory-Study Connections: Robust Learning Metrics, Ken Koedinger)

Normal post-test

2007-01-03T19:01:44Z

Adele:

Immediately follows instruction. Items/problems/activities are “isomorphic” to those in instruction, have similar form, but may be different content.
(from Toward Tighter Theory-Study Connections: Robust Learning Metrics, Ken Koedinger)

Intelligent Writing Tutor

2007-01-03T18:59:47Z

Adele: /* Dependent variables */

== Intelligent Writing Tutor ==
Teruko Mitamura, Ruth Wylie, and Jim Rankin
Project Advisors: Brian MacWhinney and Ken Koedinger

=== Abstract ===
As part of contributing to the PSLC's underlying goal of developing a theory of robust learning, the Intelligent Writing Tutor (IWT) project will explore the issue of [[transfer]] and [[long-term retention]] of acquired knowledge. We will look at positive, negative and null transfer from a student’s native language (L1), and their roles in learning and retaining information. We hope to be able to develop general principles that can then be applied to other domains.

=== Background ===
==== Research Motivation ====

We are interested in looking at the effects of transfer from L1 in language learning and English in particular. This study will contribute to the theory of robust learning by providing experimental data related to the often cited but little researched educational principle of “build on prior knowledge”. We hypothesize that elements of English that correspond to a student's L1 will be easier to learn than those elements that do not correspond (positive transfer). Moreover, elements for which there are no corresponding elements will be harder to learn (negative transfer). Our study will go beyond the simple Contrastive Analysis by examining at a detailed leveled the various [[features]] and their relative validity for a given [[knowledge component]]. For example, instead of looking at simply if articles exist in the native language, we will examine specific instances of article usage (e.g. immediate situation, general knowledge, sporadic reference, etc. (Sand, 2004)).

==== Educational Motivation ====

While error-free article use may not be necessary in order for most communication to occur (e.g. People usually understand what is meant by “Give me a book on the table” even when “Give me the book on the table” is correct), the problem becomes more severe when students submit written work, especially high-level work such as technical and academic reports. Errors in writing at this level may suggest, either consciously or unconsciously, that errors exist in the work itself (Master, 1997). Furthermore, students at this level are often aware of their difficulties with article use and are motivated to learn how to use articles correctly, thus developing a tutoring system that will help them develop better understanding and therefore produce error-free text and speech is likely to be well-received.

=== Research question ===
How is robust learning of English affected by L1 transfer?

=== Independent variables ===
==== Study 1: ====
The independent variable for this study is the student’s first language (L1).
Students will be divided into groups based on the type of determiners present in their L1. Students with L1s do not have an article system (e.g. Chinese, Japanese, Korean) will be placed in one group, while students with L1s that do have an article system (e.g. Spanish, Arabic, French) will be placed in another.

=== Hypothesis ===

==== Study 1: ====
Hypothesis 1: Knowledge components subject to negative transfer will be harder to learn than knowledge components for which there is positive or no transfer

[[Image:Int_writing_tutor_hyp1.gif]]
*Pcno – Probability of correct given no transfer
*Pc- – Probability of correct given negative transfer

Hypothesis 2: Knowledge components subject to negative transfer will exhibit greater rate of decay than knowledge components for which there is positive or no transfer.

[[Image:Int_writing_tutor_hyp2.gif]]
*Pcno – Probability of correct given no transfer
*Pc- – Probability of correct given negative transfer
*PI – Probability of Incorrect

=== Dependent variables ===
This study will utilize a series of post-tests which measure both normal and robust learning, including:

*[[Normal post-test]], immediate: Immediately following instruction, students will complete their first post-test in order to measure the effectiveness of the training itself. These tasks will be a measure of normal learning (near transfer, immediate testing).
*Normal post-test, long-term retention: Additional post-tests will be administered 3, 10, 20, and 35 days after initial instruction. These measures will be similar to the ones students encountered during training but will assess the more robust learning measure of long-term retention.
*Transfer: In addition, we will collect student writing samples in order to determine if the instructional activities succeeded in enabling students to produce text with fewer errors.

=== Explanation ===
This study is part of the Fluency and Refinement cluster. The main hypothesis of this cluster is that the structure of instructional activities and student’s prior knowledge play critical roles in developing robust learning. Since students learning English are already fluent in at least one language, we can utilize this fact to better understand how a student’s prior knowledge affects acquisition of new knowledge components. The learning event space is described as follows:

Start
#Guess
##Entry is correct  exit, with little learning
##Entry is incorrect  Start
#Use the article of one’s first language
##Entry is correct  exit, with possibly mistaken learning
##Entry is incorrect  Start
#Try to apply knowledge of English article grammar
##Entry is correct  Exit, with learning
##Entry is incorrect  Start

The second set of paths (2, 2.1, 2.2) are only available to students whose first language has articles. Thus, this analysis is an instance of the explanation schema adding new paths.

Although this study seems on the surface to be a simple matter of measuring negative transfer, the learning events space analysis suggests that learning is contingent on students’ choices of paths. In particular, if the grammatical system of the first language is quite different from the English system, students may rapidly learn that choice 2 leads only to errors (2.2) so they may stop using their first language as the default solution. In that case, the expected negative transfer may not occur.

The study includes retention and acceleration of future learning measures. This allows testing of the so-called path independence hypothesis (Klahr & Nigam; Nokes & Ohlsson), which is that when students reach a certain level of competence, it doesn’t matter how they got there; their subsequent performance, including both retention and acceleration, will be the same. The PSLC theoretical framework suggests that the path to competence does make a difference, albeit a small one. If students make several errors per learning event, and thus have to cycle through the paths above several times, then when they do eventually produce the correct response, the encoding context is cluttered with features due to the errors and feedback messages. During testing, those features will be absent. Thus, students may be less able to retrieve the appropriate knowledge components. This predicts that students who make multiple errors during training, and this is likely to be the students who have path 2 available to them, are likely to have less robust learning.

=== Further Information ===
Results of the pilot study can be found here. <nowiki>[Need this link still]</nowiki>

Long-term retention

2007-01-03T18:53:40Z

Adele:

[[Category:Glossary]]
[[Category:PSLC General]]
Retention of knowledge over a relatively long period of time. In PSLC studies, a post-test given immediately after the instruction is not a long-term retention test. As a weak heuristic, a long-term retention interval (the time between the end of instruction and the test) should be as least as long as the time of the instruction (the time between the beginning and end of the instructional period of the study). Difference between control and treatment instruction tend to be harder to detect at longer retention intervals but the longer the interval at which a difference is detected the greater the evidence of the treatment leading to long-term retention.

In one of the theories of learning, there are two parts to developing long-term retention: memorization (the transfer of new knowledge from the short term memory to the long term memory) and incorporation (relating new knowledge to exisiting knowledge and assigning it to a proper location in your long term memory ).

Entrenchment

2007-01-03T18:48:15Z

Adele:

[[Category:Glossary]]
[[Category:Refinement and Fluency]]
Entrenchment is a process that leads to increased strengthening of a form-function relation or cue over time. Examples of relevant linguistic forms include phonemes, words, phrases, and constructions. With repeated successful use, a form becomes stronger and can resist competitors. Error decreases, reaction times speed up, and fluency increases. Once entrenched, it is difficult to unlearn a form and difficult to learn new ways of marking the relevant underlying functions. (Brian MacWhinney)

Cue strength

2007-01-03T18:46:06Z

Adele:

[[Category:Glossary]]
[[Category:Refinement and Fluency]]
In order to define cue strength, one first has to define the concept of a cue. This has to be done separately in each content domain. A linguistic cue involves a marking of a linguistic function by a linguistic form. In comprehension, the cue is the form and cues compete for assignment to functions. Markings can be of three types: morphological (affixes and intonations), lexical semantics (animacy, classifiers), and syntactic (word order). Cues are used to mark lingusitic functions, such as case role, attachment, or coreference. For each cue, we can assess its strength by placing it in competition with other cues in experiments designed specifically to measure relative cue strength. Assuming a standard within-subjects ANOVA design, strength is then measured by fitting a maximum likelihood estimation (MLE) model to the data. The notion of cue strength can also be applied to other cognitive domains in a parallel fashion.
(Brian MacWhinney)

Coordination

2007-01-03T18:40:31Z

Adele:

A process for achieving self-supervised learning by integrating information from multiple sources or reasoning methods.

By coordination, we mean the processes that support mapping between relevant visual and verbal information as well as the processes that keep relevant knowledge components active. For example, in geometry a student needs to map between text references to angles and their location in a diagram and will need to maintain the numerical (given or solved) value of that angle to use in problem solving.

(from Learning with Diagrams in Geometry: Strategic Support for Robust Learning by Vincent Aleven & Kirsten Butcher)
[[Category:Glossary]]
[[Category:PSLC General]]
[[Category:Coordinative Learning]]

Coordination

2007-01-03T18:37:59Z

Adele:

A process for achieving self-supervised learning by integrating information from multiple sources or reasoning methods.

(from Learning with Diagrams in Geometry: Strategic Support for Robust Learning by Vincent Aleven & Kirsten Butcher)
[[Category:Glossary]]
[[Category:PSLC General]]
[[Category:Coordinative Learning]]

Learning event space

2007-01-02T20:10:09Z

Adele:

[[Category:Glossary]]
[[Category:PSLC General]]

Robust learning

2007-01-02T20:09:45Z

Adele:

[[Category:Glossary]]
[[Category:PSLC General]]

Robust learning is learning the achieves either or both deep conceptual understanding and strong procedural fluency. Sometimes instructional objectives of a course may put more emphasis on one or the other, but often both are desirable. Learning is robust if the acquired knowledge or skill meets at least one of the following three criteria:

• [[long-term retention]]: It is retained for long periods of time, at least for days and even for years.

• [[Transfer]]: It transfers, that is, it can be used in situations that differ significantly from the situations present during instruction.

• [[accelerated future learning]]: It accelerates future learning. That is, when related instruction is presented in the future, this knowledge allows them to learn more quickly and effectively learn from it(from The PSLC Theoretical Framework).

Instruction that achieves robust learning is designed so that the [[learning event space]] has some target paths that would cause an ideal student to acquire [[knowledge components]] that have either or both high [[feature validity]], that is, they are accurate, deep, and general, and high strength, that is, they can be applied quickly and effortlessly.

Robust learning

2007-01-02T20:09:08Z

Adele:

[[Category:Glossary]]
[[Category:PSLC General]]

Robust learning is learning the achieves either or both deep conceptual understanding and strong procedural fluency. Sometimes instructional objectives of a course may put more emphasis on one or the other, but often both are desirable. Learning is robust if the acquired knowledge or skill meets at least one of the following three criteria:

• [[long-term retention]]: It is retained for long periods of time, at least for days and even for years.

• [[Transfer]]: It transfers, that is, it can be used in situations that differ significantly from the situations present during instruction.

• [[accelerated future learning]]: It accelerates future learning. That is, when related instruction is presented in the future, this knowledge allows them to learn more quickly and effectively learn from it(from The PSLC Theoretical Framework).

Instruction that achieves robust learning is designed so that the [[learning event space]] has some target paths that would cause an ideal student to acquire [[knowledge components]] that have either or both high [[feature validity]], that is, they are accurate, deep, and general, and high [[strength]], that is, they can be applied quickly and effortlessly.

Robustness

2007-01-02T20:08:34Z

Adele:

Robust learning

2007-01-02T20:08:15Z

Adele:

Robust learning is learning the achieves either or both deep conceptual understanding and strong procedural fluency. Sometimes instructional objectives of a course may put more emphasis on one or the other, but often both are desirable. Learning is robust if the acquired knowledge or skill meets at least one of the following three criteria:

• [[long-term retention]]: It is retained for long periods of time, at least for days and even for years.

• [[Transfer]]: It transfers, that is, it can be used in situations that differ significantly from the situations present during instruction.

• [[accelerated future learning]]: It accelerates future learning. That is, when related instruction is presented in the future, this knowledge allows them to learn more quickly and effectively learn from it(from The PSLC Theoretical Framework).

Instruction that achieves robust learning is designed so that the [[learning event space]] has some target paths that would cause an ideal student to acquire [[knowledge components]] that have either or both high [[feature validity]], that is, they are accurate, deep, and general, and high [[strength]], that is, they can be applied quickly and effortlessly.

Root node

2007-01-02T20:08:06Z

Adele: /* Dependent variables */

== PSLC theoretical hierarchy’s Root Node ==

=== Abstract ===
PSLC research is primarily concerned with finding out what instructional environments, methods or activities causes students’ learning to be robust. Although normal learning can be measured with immediate, near-transfer post-tests, we measure [[robustness]] with three addition measures: retention, far [[transfer]] and preparation for learning.

=== Glossary ===

[[:Category:PSLC General|PSLC General Glossary]]

[[:Category:Glossary|Full Glossary]]

=== Research question ===
What instructional activities or methods cause students’ learning to be robust?

=== Dependent variables ===
Measures of basic learning (an immediate, near-transfer post-test) and measures of [[robust learning]] (retention, far-transfer and preparation for future learning)

=== Independent variables ===
Independent variables in PSLC are primarily instructional activities, methods, or treatments. Studies might also include independent variables that measure individual differences, like a language students' first language.

=== Hypotheses ===
Learning will be robust if the instructional activities are designed to include appropriate paths, and the students tend to follow those paths during instruction.

=== Explanation ===
Instructional activities influence the depth and generality of the students’ acquired knowledge components, the knowledge components’ strength and [[feature validity]], and the student’s motivation. These in turn influence the students’ performance on measures of [[robust learning]]. That is, we take a cognitive stance, rather than a radically distributed or situated stance.

At the macro-level, instruction produces robust learning if it increases the frequency of:
*[[sense making]] processes: rederivation, adaptation and self-supervised learning
*and [[foundational skill building]] processes: strengthening, deep feature perception and [[cognitive headroom]].

At the micro-level, instruction produces robust learning if:
*The instruction is designed so that the learning event space has some target paths that would cause an ideal student to acquire knowledge that is deep, general, strong and retrieval-feature-valid.
*Most students follow a target path most of the time. There are many factors outside the easy control of the experimenter or instructor, such as motivation and recall, that affect whether students actually follow the target paths designed into the instruction.

=== Descendents ===
*[[Interactive Communication]].
*[[Coordinative Learning]].
*[[Refinement and Fluency]].

=== Annotated bibliography ===
Forthcoming.

Root node

2007-01-02T20:07:00Z

Adele: /* Explanation */

== PSLC theoretical hierarchy’s Root Node ==

=== Abstract ===
PSLC research is primarily concerned with finding out what instructional environments, methods or activities causes students’ learning to be robust. Although normal learning can be measured with immediate, near-transfer post-tests, we measure [[robustness]] with three addition measures: retention, far [[transfer]] and preparation for learning.

=== Glossary ===

[[:Category:PSLC General|PSLC General Glossary]]

[[:Category:Glossary|Full Glossary]]

=== Research question ===
What instructional activities or methods cause students’ learning to be robust?

=== Dependent variables ===
Measures of basic learning (an immediate, near-transfer post-test) and measures of robust learning (retention, far-transfer and preparation for future learning)

=== Independent variables ===
Independent variables in PSLC are primarily instructional activities, methods, or treatments. Studies might also include independent variables that measure individual differences, like a language students' first language.

=== Hypotheses ===
Learning will be robust if the instructional activities are designed to include appropriate paths, and the students tend to follow those paths during instruction.

=== Explanation ===
Instructional activities influence the depth and generality of the students’ acquired knowledge components, the knowledge components’ strength and [[feature validity]], and the student’s motivation. These in turn influence the students’ performance on measures of [[robust learning]]. That is, we take a cognitive stance, rather than a radically distributed or situated stance.

At the macro-level, instruction produces robust learning if it increases the frequency of:
*[[sense making]] processes: rederivation, adaptation and self-supervised learning
*and [[foundational skill building]] processes: strengthening, deep feature perception and [[cognitive headroom]].

At the micro-level, instruction produces robust learning if:
*The instruction is designed so that the learning event space has some target paths that would cause an ideal student to acquire knowledge that is deep, general, strong and retrieval-feature-valid.
*Most students follow a target path most of the time. There are many factors outside the easy control of the experimenter or instructor, such as motivation and recall, that affect whether students actually follow the target paths designed into the instruction.

=== Descendents ===
*[[Interactive Communication]].
*[[Coordinative Learning]].
*[[Refinement and Fluency]].

=== Annotated bibliography ===
Forthcoming.

Robustness

2007-01-02T20:06:17Z

Adele:

[[Category:Glossary]]
[[Category:PSLC General]]

Cue strength

2007-01-02T19:07:26Z

Adele:

Entrenchment

2007-01-02T19:05:20Z

Adele:

Refinement and Fluency

2007-01-02T15:59:42Z

Adele:

== The PSLC Refinement and Fluency cluster ==

=== Abstract ===
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.

1. task analysis: To design effective instruction, we must analyze learning tasks into their simplest components.

2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.

3. scheduling of practice: The optimal scheduling of practice uses principles of memory [[consolidation]] to maximize robust learning and achieve mastery.

4. [[explicit instruction]]: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.

5. [[implicit instruction]]: On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.

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.

7. [[cue validity]]: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.

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.

9. learning to learn: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.

10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.

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.

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.

===Significance===
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.

=== Glossary ===
[[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.

=== Research question ===
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 teaching events relative to implicit learning opportunities, and the role of feedback.

=== Independent variables ===
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.

=== Dependent variables ===
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.

=== Hypotheses ===
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.

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:

1. scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.

2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems.

3. explicit instruction hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.

4. implicit instruction hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.

5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.

6. cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.

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.

8. learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.

9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.

=== Explanation ===
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.

=== Descendents ===

* [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)

* [[Learning the role of radicals in reading Chinese]] (Liu et al.)

* [[Basic skills training|French dictation training]] (MacWhinney)

*[[French gender cues]] (Presson-MacWhinney)

*[[Chinese pinyin dictation]] (Zhang-MacWhinney)

*[[Japanese fluency]] (Yoshimura-MacWhinney)

* [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)

* [[Optimizing the practice schedule]] (Pavlik-MacWhinney)

* [[Semantic grouping during vocabulary training]] (Tokowicz-Degani)

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Was in Coordinative Learning and in Interactive Communication]

*[[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]

* [[Mental rotations during vocabulary training]] (Tokowicz-Degani)

* [[arithmetical fluency project]] (Fiez)

* [[HandwritingEquationSolving|A multimodal (handwriting) interface for solving equations]] (Anthony, Yang, & Koedinger) [Was in CL]

* [[Fostering fluency in second language learning]] (De Jong, Perfetti)

=== Annotated bibliography ===
Forthcoming

[[Category:Cluster]]

Refinement

2007-01-02T15:08:46Z

Adele:

[[Category:Glossary]]
[[Category:Refinement and Fluency]]
Making modifications to knowledge itself and more importantly, to the conditions under which the knowledge should be applied (from The PSLC Theoretical Framework).

Sense making

2007-01-02T15:06:36Z

Adele:

[[Category:Glossary]]
[[Category:PSLC General]]
These are robust learning processes wherein students try to understand the instruction or engage in higher-level thinking to create knowledge independent of instruction. PSLC research focuses on two types of sense-making processing.

o Coordinative learning: When students go beyond direct instructional feedback to learn on their own by integrating results from multiple input sources, representations, or reasoning strategies, we say they are engaged in coordinative learning. Coordinative learning includes co-training (Blum & Mitchell, 1998), a theoretically sound technique in machine learning for using multiple input sources to perform unsupervised learning from data that does not include correct responses or feedback. Coordinative learning also includes other ways of learning from “multiples” including multimedia, multiple representations and multiple strategies.

o Interactive communication: When two agents takes turns with each other, share initiative during the instruction, and may explore an idea at arbitrary depth, then we say they are engaged in interactive communication. We mean to include natural language dialogues between a student and a peer or a tutor as well as other non-verbal (e.g., computer interface mediated) forms of dialogue (from The PSLC Theoretical Framework).

Sense making refers to the phenomena of making and unmaking of sense.

The way that people choose between multiple possible explanations of sensory and other input as they seek to conform the phenomenological with the real in order to act in such a way as to determine or respond to the world around them.

Robustness

2007-01-02T15:03:25Z

Adele:

[[Category:Glossary]]
[[Category:PSLC General]]

Robust learning is learning the achieves either or both deep conceptual understanding and strong procedural fluency. Sometimes instructional objectives of a course may put more emphasis on one or the other, but often both are desirable. Learning is robust if the acquired knowledge or skill meets at least one of the following three criteria:

• [[long-term retention]]: It is retained for long periods of time, at least for days and even for years.

• [[Transfer]]: It transfers, that is, it can be used in situations that differ significantly from the situations present during instruction.

• [[accelerated future learning]]: It accelerates future learning. That is, when related instruction is presented in the future, this knowledge allows them to learn more quickly and effectively learn from it(from The PSLC Theoretical Framework).

Instruction that achieves robust learning is designed so that the [[learning event space]] has some target paths that would cause an ideal student to acquire [[knowledge components]] that have either or both high [[feature validity]], that is, they are accurate, deep, and general, and high [[strength]], that is, they can be applied quickly and effortlessly.

Cognitive headroom

2006-12-28T18:42:08Z

Adele:

One hypothesized pathway to accelerated future learning and transfer of higher level knowledge components (KCs) is through the development of fluency in lower level foundational skills. Students without such fluency must grapple with the basic lower level KCs while trying to use and acquire higher level KCs. Student with fluency in basic KCs have the more available cognitive capacity, more "headroom", to use and acquire higher level KCs.

For example, Haverty et al. (2000) found that basic number knowledge separated good from poor inductive reasoners and Haverty (1999) followed up with an experimental study showing that basic instruction on number knowledge (e.g., 17 vs. 19 facts) transfered to better higher level reasoning (e.g., discovering the function consistent with a table of x-y pairs).

In the terms of the cognitive load theory elaborated by Sweller and colleagues, lack of basic fluency may produce a kind of "extraneous cognitive load". A PSLC goal is to not simply label learning events as involving "extraneous cognitive load", which is done post-hoc in past research, but to develop predictive models to guide designers a priori in avoiding the design of such learning events. Such models involve knowledge component analysis and, in particularly, when a learning event involves student processing of knowledge components that are either under-learned (thus, no cognitive headroom) or irrelevant and unnecessary to meeting instructional objectives (e.g., keyboard vs. hand written entry of equations in Anthony's PSLC project).

Haverty, L. A., Koedinger, K. R., Klahr, D., & Alibali, M. W. (2000). Solving induction problems in mathematics: Not-so-trivial pursuit. Cognitive Science, 24(2), 249-298.

Haverty, L. A. (1999). The Importance of Basic Number Knowledge to Advanced Mathematical Problem Solving. Doctoral dissertation. Psychology Department, Carnegie Melon University.

[[Category:Glossary]]
[[Category:PSLC General]]

Headroom

2006-12-28T18:41:59Z

Adele:

Root node

2006-12-28T18:41:14Z

Adele: /* Explanation */

== PSLC theoretical hierarchy’s Root Node ==

=== Abstract ===
PSLC research is primarily concerned with finding out what instructional environments, methods or activities causes students’ learning to be robust. Although normal learning can be measured with immediate, near-transfer post-tests, we measure [[robustness]] with three addition measures: retention, far [[transfer]] and preparation for learning.

=== Glossary ===

[[:Category:PSLC General|PSLC General Glossary]]

[[:Category:Glossary|Full Glossary]]

=== Research question ===
What instructional activities or methods cause students’ learning to be robust?

=== Dependent variables ===
Measures of basic learning (an immediate, near-transfer post-test) and measures of robust learning (retention, far-transfer and preparation for future learning)

=== Independent variables ===
Independent variables in PSLC are primarily instructional activities, methods, or treatments. Studies might also include independent variables that measure individual differences, like a language students' first language.

=== Hypotheses ===
Learning will be robust if the instructional activities are designed to include appropriate paths, and the students tend to follow those paths during instruction.

=== Explanation ===
Instructional activities influence the depth and generality of the students’ acquired knowledge components, the knowledge components’ strength and [[feature validity]], and the student’s motivation. These in turn influence the students’ performance on measures of robust learning. That is, we take a cognitive stance, rather than a radically distributed or situated stance.

At the macro-level, instruction produces robust learning if it increases the frequency of:
*[[sense making]] processes: rederivation, adaptation and self-supervised learning
*and [[foundational skill building]] processes: strengthening, deep feature perception and [[cognitive headroom]].

At the micro-level, instruction produces robust learning if:
*The instruction is designed so that the learning event space has some target paths that would cause an ideal student to acquire knowledge that is deep, general, strong and retrieval-feature-valid.
*Most students follow a target path most of the time. There are many factors outside the easy control of the experimenter or instructor, such as motivation and recall, that affect whether students actually follow the target paths designed into the instruction.

=== Descendents ===
*[[Interactive Communication]].
*[[Coordinative Learning]].
*[[Refinement and Fluency]].

=== Annotated bibliography ===
Forthcoming.

Feature validity

2006-12-28T18:37:55Z

Adele:

[[Category:Glossary]]
[[Category:Coordinative Learning]]
Feature validity is a generalization of the standard concept of cue validity. Cues are usually understood to be perceptual or at least rapidly computed (MacWhinney & Bates, 1989). The term “features” includes cues as well as higher level properties, such as those used by experts but not novices (Chi, Glaser & Feltovitch, 1981).

The feature validity of a [[knowledge component]] measures how well the features associated with the mental representation of the knowledge component match the features present during all situations where the component should be recalled. Strength is roughly proportionally to the number of times an encoding of a knowledge component was accessed and how recently it was accessed.

Root node

2006-12-28T18:25:15Z

Adele: /* Explanation */

== PSLC theoretical hierarchy’s Root Node ==

=== Abstract ===
PSLC research is primarily concerned with finding out what instructional environments, methods or activities causes students’ learning to be robust. Although normal learning can be measured with immediate, near-transfer post-tests, we measure [[robustness]] with three addition measures: retention, far [[transfer]] and preparation for learning.

=== Glossary ===

[[:Category:PSLC General|PSLC General Glossary]]

[[:Category:Glossary|Full Glossary]]

=== Research question ===
What instructional activities or methods cause students’ learning to be robust?

=== Dependent variables ===
Measures of basic learning (an immediate, near-transfer post-test) and measures of robust learning (retention, far-transfer and preparation for future learning)

=== Independent variables ===
Independent variables in PSLC are primarily instructional activities, methods, or treatments. Studies might also include independent variables that measure individual differences, like a language students' first language.

=== Hypotheses ===
Learning will be robust if the instructional activities are designed to include appropriate paths, and the students tend to follow those paths during instruction.

=== Explanation ===
Instructional activities influence the depth and generality of the students’ acquired knowledge components, the knowledge components’ strength and [[feature validity]], and the student’s motivation. These in turn influence the students’ performance on measures of robust learning. That is, we take a cognitive stance, rather than a radically distributed or situated stance.

At the macro-level, instruction produces robust learning if it increases the frequency of:
*[[sense making]] processes: rederivation, adaptation and self-supervised learning
*and [[foundational skill building]] processes: strengthening, deep feature perception and cognitive headroom.

At the micro-level, instruction produces robust learning if:
*The instruction is designed so that the learning event space has some target paths that would cause an ideal student to acquire knowledge that is deep, general, strong and retrieval-feature-valid.
*Most students follow a target path most of the time. There are many factors outside the easy control of the experimenter or instructor, such as motivation and recall, that affect whether students actually follow the target paths designed into the instruction.

=== Descendents ===
*[[Interactive Communication]].
*[[Coordinative Learning]].
*[[Refinement and Fluency]].

=== Annotated bibliography ===
Forthcoming.

Learning the role of radicals in reading Chinese

2006-12-28T15:17:59Z

Adele:

'''Node Title''': Semantic Radicals Study

'''Researchers''': Susan Dunlap, Ying Liu, Charles Perfetti, Sue-mei Wu

1. An '''abstract''' that briefly describes the research encompassed by the node

Does providing reliable semantic information help second language learners acquire new words? Two experiments investigated whether adult learners of Chinese benefited from explicit instruction of semantic information when learning new characters. We manipulated whether semantic information was a reliable cue to word meaning and whether predictability was taught explicitly. We measured learning outcomes with translation and semantic judgment tasks.

2. A '''glossary''' that defines terms used elsewhere in this node but not defined in the nodes that are parents, grandparents, etc. of this node

Semantic radical; explicit; implicit; cue reliability

3. The '''research question''' stated as concisely as possible, usually in a single sentence

Does providing reliable semantic information help second language learners acquire new words?

4. A '''background''' and significance section that briefly summarizes prior work on the research question and why it is important to answer it

Previous research has shown that non-native learners of Chinese do not discern the presence of helpful cues in the orthography unless such relationships are taught explicitly (Taft & Chung, 1999). But because semantic cues in Chinese are not always reliable predictors of word meaning (Hanley, 2005; Shu, Chen, Anderson, Wu, & Xuan, 2003), it may actually be more confusing for a beginning learner to be taught these relationships. The aim of this study was to determine how [[reliability]] of cues can affect learning. As in every language, Chinese has rules and exceptions to those rules. The written form of Chinese contains a high percentage of compound characters, which are single, one-syllable words made up of semantic and phonetic radicals. These radicals, or linguistic subcomponents, often provide cues to the character’s meaning and pronunciation. However, a reader cannot rely solely on using this strategy to decode new words in Chinese. Therefore, we wanted to ascertain whether it is helpful to teach the sometimes ambiguous relationship between linguistic subcomponents and whole word definitions.

5 The '''dependent variables''', which are observable and typically measure competence, motivation, interaction, meta-learning, or some other pedagogically desirable outcome

- accuracy and response time on a semantic category judgment task with previouslylearned items (Experiment 1)

- accuracy of translating previously learned Chinese characters into English (Experiment 2)

- accuracy on a multiple-choice translation task with new Characters (Experiments 1 and 2)

6 The '''independent variables''', which are typically include instructional environment, activity or method, and perhaps some student characteristics, such as gender or first language

Training condition was either explicit (information was provided about the semantic radical’s meaning in relation to meaning of the character) or implicit (no additional information was provided). Each semantic radical was either reliable (its meaning was associated with the meaning of the characters) or unreliable (its meaning was unrelated to the meaning of the character in which it appeared).

7 The '''hypothesis''', which is a concise statement of the relationship among the variables that answers the research question

We predict an interaction between reliability and explicitness, such that learners will perform better on items studied in the explicit condition compared to the implicit condition, and this effect will be greater for characters with reliable semantic radicals than characters with unreliable semantic radicals.

8 The '''findings''', which are the results of the study if any are currently available

Preliminary analyses show that providing semantic cues promoted retention of target characters and aided in transferring knowledge to new characters. Reliability of cues had no additional effect on retention or transfer.

9 An '''explanation''', which is short (a paragraph or two) and typically mentions unobservable, hypothetical attributes of the students (e.g., the students’ knowledge or motivation) and cognitive or social processes that affect them

We theorize that learners benefit from being taught the connection between semantic subcomponents of words and the meanings of words, and they adopt this strategy in learning new vocabulary.

10. The '''descendents''', which lists links to descendent nodes of this one, if there are any

None yet.

11. A '''further information''' section that points to documents using hyper links and/or references in APA format. Each indicates briefly the document's relationship to the node (e.g., whether the document is a paper reporting the node in full detail, a proposal describing the motivation and design of the study in more detail, the node for a similar PSLC research study, etc.)

None yet.

Basic skills training

2006-12-28T15:17:10Z

Adele: /* Explanation */

==French dictation training==

==Abstract==

The goal of this project is to improve the ability of students of Elementary French to write down sentences as they hear them.
Mastering this ability involves improvements in vocabulary recognition, auditory range and precision, and control of spelling rules.
Like other studies conducted by MacWhinney and Pavlik, this work emphasizes the role of scheduling in attaining mastery. This practice
is now an in vivo component of French Online.

==Glossary==
*[[optimal spacing interval]]
*[[mastery]]
*[[spelling rules]]
*[[cue reliability]]
*[[cue availability]]
*[[transfer]]

==Research question==
This research is designed to discover the best method of producing robust learning of French dictation. Toward this end, there is an
emphasis on developing materials based on error analysis to configure correct stimulus sequencing. The specific online method examines the role
of sequencing, explicit feedback, and lexical familiarity in promoting dictation skill.

==Background and significance==
Both L1 and L2 classroom instruction in French often uses a system of dictation that seeks to teach clear recognition of words, phrases, and sentences along with proper spelling. In classroom dictatin, the teacher will pronounce a series of phrases or sentences and ask the students to write them out. The sentences are typically designed to provide information regarding orthographic ambiguities such as the contrast between the masculine adjective intelligent, the feminine form intelligente, or the plural form intelligentes. This single classroom activity incorporates four types of learning at once:
#Students must be able to recognize the words between produced by the teacher. For L1 learners, this part of dictation is relatively easy, but L2 learners may find two types of challenges here. First, L2 learners often find that French is difficult to segment into words because of frequent elision patterns and the reduced, monosyllabic nature of high frequency forms.
#Second, word recognition may be difficult in some cases for L2 learners because they have not yet learned the relevant vocabulary.
#Once words are recognized, learners must be able to map them to standard orthographic patterns. For learners with English as L1, the use of diacritics and the proper encoding of vowel patterns is a particular challenge.
#Although French spelling is largely regular, many spelling patterns depend on grammatical [[features]] such as gender, number, and verb conjugation. In order to achieve proper dictation, learners must be sensitive to these features.

After pilot testing in Fall 2005, the FOL (French Online) course now incorporates a Java-based dictation template that logs directly to DataShop. The program gives immediate feedback regarding correctness, but we have not yet provided feedback regarding the technicalities or principles of spelling.

==Dependent variables==
#The dependent variable is percentage correct. Correctness is scored on the word level. There is no penalty for incorrect words. Scoring is done by a Perl program.
#In addition to the overall analysis for percentage correct, we are also closely tracking error types within words. We are interested in specifying closely the spellings rules and auditory patterns that are most difficult for students.

==Independent variables==
#We are using a pretest-posttest design to measure the overall effects of the online training. We compare gain scores from students in the traditional course with no dictation training with gain scores for students in the online course with dictation training.
#We are also tracking the effects of exposure to particular sentences. In each lesson, half of the students study one list and half study another. We then test generalization across lists.

==Hypothesis==
#Our initial hypothesis was that exposure to a particular set of sentence would markedly improve the ability to spell these sentences in comparison with a parallel set of unexposed sentences. In the first round of testing, we found that this was wrong, indicating that the skill of dictation is not pegged to sentence level memories, but rather to the level of the phoneme and word.
#We hypothesize that dictation training in French 1 will markedly improve dictation ability in French 2.
#We hypothesize that the most difficult dictation patterns will be those marked by use of diacritics, unreliable spelling patterns, and difficult auditory sequences.

These predictions derive from the Competition Model (MacWhinney, in press).

==Explanation==
The [[Competition]] Model explanation for these effects emphasizes the role of L1 transfer, cue [[reliability]], cue availability, and lexical learning as determinants of dictation learning. Availability and reliability are measured across the vocabulary. L1 transfer effects are predicted on the basis of a comparative analysis of French and English.

==Descendents==

==Annotated bibliography==
*Bonin, P., Fayol, M., & Pacton, S. (2001). La production verbale écrite: évidences en faveur d'une (relative) autonomie de l'écrit. Psychologie Francaise, 46, 77-88.
*Bonin, P., Fayol, M., & Gombert, J. (1998). An experimental study of lexical access in the writing and naming of isolated words. Inernational Journal of Psychology, 33, 269-286.
*Content, A., Mousty, P., & Radeau, M. (1990). Brulex: Une base de données lexicales informatisée pour le français écrit et parlé. L'Année Psychologique, 90, 551-566.
*MacWhinney, B. (2006). A unified model. In N. Ellis & P. Robinson (Eds.), Handbook of Cognitive Linguistics and Second Language Acquisition. Mahwah, NJ: Lawrence Erlbaum Press.

[[Category:Study]]

French gender cues

2006-12-28T15:16:44Z

Adele: /* Independent variables */

==French gender cues==

==Abstract==

The goal of this project is to improve the ability of students of Elementary French to determine the gender of French nouns.
Like other studies conducted by MacWhinney and Pavlik, this work emphasizes the role of scheduling in attaining mastery.

==Glossary==
*[[optimal spacing interval]]
*mastery
*gender rules
*cue reliability
*cue availability
*lexical effects

==Research question==
This research is designed to discover the best method of producing robust learning of French nominal gender.

==Background and significance==
Tucker, Lambert and Rigault (1977) evaluated the L1 (first language) learning of cues to gender in French. More recently, Holmes and Dejean de la Batie (1999) produced the first study of the acquisition of grammatical gender by L2 learners. Holmes and Segui (2004) have extended the detail of these analyses, but so far only with native speakers.
Carroll (1999) and Lyster (2006) have explored the role of [[cue validity]] and [[availability]] in predicting usage by learners. All of these studies underscore the importance of high validity cues for the general vocabulary. However, these cues are only marginally useful for the highest frequency forms, whose gender must be learned more or less by rote. These analyses are in very close accord with the claims of the [[Competition]] Model (MacWhinney 1978, 2006). Our goal here is to use these findings to guide effective instruction.

==Dependent variables==
The dependent variable is percentage correct gender judgment. Because there are only two genders in French, chance performance is at 50%.

==Independent variables==
#We are using a pretest-posttest design to measure the overall effects of the online training. We compare gain scores from students in the traditional course with no gender training with gain scores for students in the online course with gender training.
#We are also tracking the relative ease of learning particular cues in terms of how [[reliability]] interacts with lexical and cue frequency.

==Hypotheses==
#Learning will be most robust if high reliability cues are taught before low reliability cues or rote training.
#Mastery training with scheduling is more effective than simple repetition.
#Cues that do not interact with similar cues will be easier to learn than those that interact with other cues.

These predictions derive from the Competition Model (MacWhinney, in press).

==Explanation==
The Competition Model explanation for these effects emphasizes the role of cue reliability, cue availability, and lexical learning as determinants of gender cue learning. Availability and reliability are measured across the vocabulary.

==Descendents==

==Annotated bibliography==
*Carroll, S. (1999). Input and SLA: Adults' sensitivity to different sorts of cues to French gender. Language Learning, 49, 37-92.
*Holmes, V. M., & Dejean de la Batie, B. (1999). Assignment of grammatical gender by native speakers and foreign learners of French. Applied Psycholinguistics, 20, 479-506.
*Holmes, V. M., & Segui, J. (2004). Sublexical and lexical influences on gender assignment in French. Journal of Psycholinguistic Research, 33, 425-457.
*Lyster, R. (2006). Predictability in French gender attribution: A corpus analysis. French Language Studies, 16, 69-92.
*MacWhinney, B. (2006). A unified model. In N. Ellis & P. Robinson (Eds.), Handbook of Cognitive Linguistics and Second Language Acquisition. Mahwah, NJ: Lawrence Erlbaum Press.
*Pavlik, P. (2005). Modeling order effects in the learning of information.

Reliability

2006-12-28T15:14:10Z

Adele:

[[Category:Glossary]]
[[Category:Refinement and Fluency]]
Yielding the same or compatible results in different clinical experiments or statistical trials.

Reliability is the consistency of your measurement, or the degree to which an instrument measures the same way each time it is used under the same condition with the same subjects. In short, it is the repeatability of your 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])

Reliability

2006-12-28T15:08:58Z

Adele:

Reliability

2006-12-28T15:08:34Z

Adele:

Reliability

2006-12-28T15:07:20Z

Adele: