Development of a Model for Adaptive Training via Computer-Assisted Instruction Utilizing Regression Analysis Techniques.

Abstract

The intent of the study was to investigate the efficacy of a dynamic decision model for an ongoing learning situation presented via computer-assisted instruction (CAI). In addition to correctness on learning frames within a concept, criterion frames testing the concepts, and correctness of end of unit quiz questions, the study was concerned with variables such as latencies for each of the above measures, and the subject's confidence of his response on the criterion and quiz questions. The first step involved the investigation, through correlational analysis, of the relationship of these variables with performance on the final examination for the two hour course on concepts of Boolean algebra. Based on the results of correlation and regression analysis with the final exam as the dependent measure, the relevant variables were incorporated into the decision model. The adaptive decision model was shown to be effective in identifying those trainees who needed remedial instruction. However, the use of the adaptive model did not significantly improve performance on a course presenting basic concepts of Boolean algebra when compared with a group of trainees that did not have the benefit of the adaptive model. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1970
Accession Number
AD0725466

Entities

People

  • Arthur D. King
  • Duncan N. Hansen
  • Leroy C. Rivers
  • Walter Dick

Organizations

  • Florida State University

Tags

DTIC Thesaurus Topics

  • Adaptive Training
  • Boolean Algebra
  • Computers
  • Computing-Related Activities
  • Data Science
  • Information Science
  • Instructions
  • Interdisciplinary Science
  • Learning
  • Mathematical Analysis
  • Mathematics
  • Regression Analysis
  • Statistical Analysis
  • Statistics
  • Trainees
  • Training

Fields of Study

  • Education

Readers

  • STEM Education
  • Software Engineering.
  • Statistical inference.