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Learning theory and knowledge structures in computer-aided instruction.
F R Jelovsek1, V A Catanzarite, R D Price
1University of Arkansas for Medical Sciences.
M.D. Computing : Computers in Medical Practice
|March 1, 1990
Summary
Developing effective computer-aided instruction (CAI) requires understanding how students learn. Explicit goals, timely hints, and structured knowledge presentation enhance learning efficiency in CAI systems.
Area of Science:
- Artificial Intelligence in Education
- Cognitive Science
- Human-Computer Interaction
Background:
- Computer-aided instruction (CAI) development lacks a unified learning theory.
- Understanding knowledge acquisition and storage is crucial for effective CAI.
Purpose of the Study:
- To infer integral learning activities from intelligent tutoring systems.
- To identify key principles for enhancing learning efficiency in CAI.
Main Methods:
- Reviewing learning theories associated with CAI.
- Analyzing knowledge classifications (rule-based, declarative, causal).
- Inferring generic learning activities (aggregation, clustering, storage).
Main Results:
- Explicit task goals accelerate learning.
- Timely hints support student problem-solving strategies.
- Teaching general rules before exceptions improves knowledge acquisition.
Conclusions:
- Common themes across knowledge classifications can inform better CAI design.
- Integrating inferred learning activities can lead to more effective CAI systems.