Optimizing the practice schedule

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Revision as of 20:31, 14 September 2006 by PhilPavlik (talk | contribs) (Background and significance)
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Optimizing the practice schedule project


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.


Research question

How can the optimal sequence of learning be computed?

Background and significance

Since the early 60's researchers in learning theory have been describing models of practice which attempt to capture the effect of practice on performance at a later time. These models are applicable to describing many types of learning situations, but are easier to apply where information to be learned can be broken up into small chunks that can be learned independently. For instance, Atkinson (1972) applied a Markov model of learning to schedule drill of German vocabulary.

More recently there has been a renewed emphasis on repeated practice. For instance, the National Council of Teachers of Mathematics new report WSJ article emphasizes the importance of this type of learning for simple math skills.

Dependent variables

Measures of normal and robust learning.

Independent variables

Alternative structures of instructional schedule for knowledge component training based on the predictions of an ACT-R based cognitive model. Further independent variables include how the material is presented for each learning event and the assumptions of the model used to presents schedule the learning. The assumptions of the model include alternative analyses of task demands, the structure of relevant knowledge components, and learner background.


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



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.


Annotated bibliography