Refinement and Fluency

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

Refinement and Fluency glossary.

Research question

The research pursued in this cluster tests the empirical adequacy of the propositions (see Abstract) that are derived from its overall hypothesis. The overall research question is to how instruction can optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain. In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the demands placed on learners of specific knowledge components, the scheduling of practice, the timing and extent of explicit teaching events relative to implicit learning opportunities, and the role of feedback.

Independent variables

Alternative structures of instructional events based on alternative analyses of task demands, relevant knowledge components, and learner background. Assessing the learner’s background is essentially part of the learning task analysis.

Dependent variables

The dependent variables in these studies are typically percentage correct and time to mastery of a structure at a certain level.

Hypotheses

The general hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task. The specific hypotheses are:

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. in vivo evaluation: The work in this cluster is targeted toward the in vivo evaluation of instruction in basic skills.
4. scheduling of practice: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.
5. resonance: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems.
6. explicit instruction: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.
7. implicit instruction: On the other hand, implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.
8. immediacy of feedback: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.
9. cue validity: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components.
10. focusing: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.
11. learning to learn: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.
12. transfer: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.

Explanation

Attention to features of the task domain as a knowledge component is processed leads to associating those features with the knowledge component. If the features are valid, then forming or strengthening such associations facilitates retrieval during subsequent assessment or instruction, and thus leads to more robust learning. However, the robustness is also dependent on the scheduling of learning events that promotes the long term retentiono of the associations.

Descendents

• Using syntactic priming to increase robust learning (de Jong, Perfetti, DeKeyser)

• Learning the role of radicals in reading Chinese (Liu et al.)

• French dictation training (MacWhinney)

• French gender cues (Presson-MacWhinney)

• Chinese pinyin dictation (Zhang-MacWhinney)

• Japanese fluency (Yoshimura-MacWhinney)

• First language effects on second language grammar acquisition (Mitamura-Wylie)

• Optimizing the practice schedule (Pavlik-MacWhinney)

• Semantic grouping during vocabulary training (Tokowicz-Degani)

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

• What is difficult about composite problems? (Kao, Roll)

• Mental rotations during vocabulary training (Tokowicz-Degani)

• arithmetical fluency project (Fiez)

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

Annotated bibliography

Forthcoming