Refinement and Fluency
- 1 The PSLC Refinement and Fluency cluster
The PSLC Refinement and Fluency cluster
The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, refinement, and fluent control of critical knowledge components. The research of the cluster addresses a series of core propositions, including but not limited to the following.
1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components.
2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.
4. explicit instruction: Explicit instruction, i.e. instruction that either directly asserts information ("facts") or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.
5. implicit instruction: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns.
6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.
7. cue validity: In both explicit and implicit instruction, the validity of a cue for a knowledge component affects the learning of that knowledge component. (Cue validity is related to feature validity.)
8. focusing: Instruction that directs (focuses) the learner's attention to valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.
9. learning to learn: The acquisition of skills and strategies that can generalize across learning tasks can promote new learning. Examples may be deep analysis, help-seeking, use of advance organizers, and, most generally, meta-cognitive strategies.
10. transfer: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while inhibiting 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.
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.
Refinement and Fluency glossary.
The overall research question is how can instruction optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain? In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the cognitive demands of knowledge components, the scheduling of practice, the timing and extent of explicit instructional events relative to implicit learning opportunities, and the role of feedback.
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.
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.
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 analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:
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. (This hypothesis is similar or perhaps the same as Coordinative Learning hypothesis or co-training more specifically whereby "divergent coding systems" here may be the same as "multiple input sources" in co-training.)
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. See also feature validity.
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.
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.
A. Explicit vs Implicit. These projects typically compare a more explict form of instruction with a more implict form
- Learning the role of radicals in reading Chinese (Liu et al.)
- French dictation training (MacWhinney)
- Providing optimal support for robust learning of syntactic constructions in ESL (Levin, Frishkoff, De Jong, Pavlik)
B. Explicit attention manipulations studies typically vary features available to learner
- Chinese pinyin dictation (Zhang-MacWhinney)
- Learning a tonal language: Chinese (Wang, Perfetti, Liu) [Also Coordinative learning]
- Learning French gender cues with prototypes (Presson, MacWhinney)
C. Explicit instruction: Practice and Scheduling Typical studies control practice events and provide feedback
- Optimizing the practice schedule (Pavlik et al.)
- French grammatical gender cue learning (Presson, MacWhinney)
- Japanese fluency (Yoshimura-MacWhinney)
- Fostering fluency in second language learning (De Jong, Perfetti)
- Using learning curves to optimize problem assignment (Cen & Koedinger)
A. Background knowledge These projects directly study effects of learners' background knowledge
- First language effects on second language grammar acquisition (Mitamura-Wylie)* Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren) [Also in Interactive Communication]
- The Impact of Native Writing Systems on 2nd Language Reading (Einikis, Ben-Yehudah, Fiez)
B. Availability of knowledge during learning
- Using syntactic priming to increase robust learning (De Jong, Perfetti, DeKeyser)
- What is difficult about composite problems? (Kao, Roll)
- Arithmetical fluency project (Fiez)
- A word-experience model of Chinese character learning (Reichle, Perfetti, & Liu)
These projects also include some addressing issues of learner control
- Mental rotations during vocabulary training (Tokowicz-Degani)
- Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving (completed) [Also in Coordinative Learning]
- Note-taking Project Page (Bauer & Koedinger) [Also in Coordinative Learning]
- Handwriting Algebra Tutor (Anthony, Yang & Koedinger)
- In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input (in progress) [Also in Coordinative Learning]
- Development of a Novel Writing System (Greene, Durisko, Ciuca, Fiez)