Optimizing the practice schedule

Optimizing the practice schedule

Abstract

This project plan extends dissertation work of Pavlik. In this initial work, a model-based algorithm was described to maximize the rate of learning for simple facts using flashcard like practice by determining the best schedule of presentation for a set of facts. The goal of this project plan is to develop this initial work to allow this tutor with optimized scheduling to handle more complex information and different types of learning in more natural settings (like LearnLabs). Specifically, this project plan describes extensions to the theory in two main areas.

1.  Specification of a theory of refined encoding
  a.  Generalization practice (multimodal and bidirectional training)
  b.  Discrimination practice (detailed error remediation)
2.  Specification of a theory of co-training
  a.  Effect of declarative memory chunk sequence during learning
  b.  Effect of declarative memory chunks on production rule learning

These theoretical directions are intended to enhance the optimization tutor by greatly extending its capabilities.

A secondary goal of the project is to link the optimization algorithm used in this project with the larger CTAT project. In this linkage the optimization algorithm would be integrated onto the current CTAT system as a curriculum management system that could select or generate problems according to the algorithm, but using CTAT interfaces. This integration will make it easier for people to use the optimal learning system and therefore increase its impact and usefulness.

Glossary

Optimal Spacing Expanding Spacing Wide Spacing Narrow Spacing Activation

Research question

How can analyses of task and learner’s knowledge lead to a structuring of instructional events that lead to robust learning?

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

Measures of normal and robust learning.

Hypothesis

Robust learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task.

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.

Descendents

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

• Basic skills training (MacWhinney)

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

• Optimizing the practice schedule (Pavlik)

• Semantic grouping during vocabulary training (Tokowicz)

• Mental rotations during vocabulary training (Tokowicz)

Annotated bibliography

Forthcoming