Accession Number : ADA117365
Title : The Recognition of Instructional Strategies in the Modelling of Student Acquisition of Problem-Solving Skills.
Descriptive Note : Final rept. 1 Sep 79-31 Aug 81,
Corporate Author : RUTGERS - THE STATE UNIV NEW BRUNSWICK NJ LAB FOR COMPUTER SCIENCE RESEARCH
Personal Author(s) : Smith,Robert ; Walker,Phyllis ; Spool,Peter
Report Date : MAY 1982
Pagination or Media Count : 182
Abstract : Most scientific and technical instruction relies heavily on the use of examples and exercises to teach domain-relevant problem-solving skills. Little is known, however, about the relationship between the structure of such curricula and the learning abilities and strategies students must have for effective learning. The research reported here has explored how learning occurs when a student is presented structured sequences of exercises in a teaching situation. The approach has been to develop a computer simulation model of a prototypical student learning how to prove theorems in elementary logic. The simulation is based on an actual computer-assisted instruction (CAI) curriculum of exercises used to teach human students at the university level. The focus in the simulation has been to obtain good performances, in terms of acquiring problem-solving skills, by relying as much as possible on a model of how the student interprets the sequence of exercises in the curriculum. We have identified a small number of principles used in creating such sequences, and have demonstrated algorithms for recognizing the sequences in order to drive the acquisition by the simulated student. The work has implications for the design of computer-based learning systems and models. In addition, the work provides a framework for understanding the incremental development of problem-solving skills in students.
Descriptors : *Computer aided instruction, *Computerized simulation, *Problem solving, *Skills, *Students, Artificial intelligence, Test methods, Mathematical models, Learning, Information theory, Logic, Reasoning, Performance(Human), Algorithms, Planning, Strategy
Subject Categories : Humanities and History
Distribution Statement : APPROVED FOR PUBLIC RELEASE