CEC Theses and Dissertations

Title

Casual Inferences of Achievement By Adult Learners as Determined By Learning Style Difference in Hypertext-Based Computer-Assisted Instruction

Date of Award

1997

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Graduate School of Computer and Information Sciences

Advisor

Marlyn Kemper Littman

Committee Member

Steven R. Terrell

Committee Member

Getrude W. Abramson

Abstract

This study examined learning styles and achievement scores with hypertext-based computer-assisted instruction (CAI) as it pertains to the adult learner. The learning styles investigated in this study were used as independent variables in an a priori manner to determine the causal inference between learning style and achievement. Correlations among additional variables, such as attitude toward CAI, experience with computers, and experience with CAI software variables, were examined in an a posteriori manner. The experiment consisted of one independent variable, learning style, which is a subject independent variable consisting of two levels- the iconic mode and the reading mode-as delineated by the Canfield Learning Styles Inventory. The dependent variable is the achievement gain determined by the difference between the pretest and posttest scores.

This dissertation inquiry was designed to add to the body of knowledge pertaining to the efficacy of CAI for the adult learner. The focus of this study was to determine the need for the integration of adult learning styles into the design of CAI for adult learners. The propagation of CAI in adult education necessitates an investigation of adopting learning styles into the design of CAI to facilitate the acquisition of skills. The investigation addressed several issues regarding the design and deployment of CAI in adult educational settings. First, the adoption of learning styles into the design of CAI answered particular educational concerns about andragogy. Second, the investigation addressed the needs of instructional technologists for content development.

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