Date of Award

Fall 2013

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Information Systems - (Ph.D.)

Department

Information Systems

First Advisor

Fadi P. Deek

Second Advisor

Katia Passerini

Third Advisor

Julian M. Scher

Fourth Advisor

James A. McHugh

Fifth Advisor

David Klappholz

Abstract

While empirical research efforts are sufficient to provide evidence of the role of most constructs in the Social Cognitive Career Theory (SCCT), this dissertation shifts the research focus and finds serious shortcomings in defining the construct of computer technology learning experiences design.

The purpose of this dissertation is to investigate whether, and to what extent, the proposed SCCT-enhanced framework can increase self-efficacy and interest of pre-college and college students in computer-based technology through the newly proposed “Learning Experiences” construct; in particular, whether it can reduce the gender gaps.

As a result of a comprehensive literature review, the dissertation connects learning, instructional design and career development theories in a holistic fashion identifying and synthesizing gaps with corresponding interventions concerning learning experiences. Subsequently, the study carries out an evolutionary re-design of SCCT in multiple iterations with the incorporation of theoretical findings until a revised SCCT framework is proposed utilizing interventions used in best practices. Accordingly, eight hypotheses are formulated to answer all research questions.

A multi-phase experiment of four rounds is designed to study the impact of the revised “learning experiences” on self-efficacy, outcome expectations and technology interest. The data collection process is cumulative in nature with numerous refinements that leads to a scale which is confidently replicated for future research and theory evolution with few refinements.

Next, an extensive statistical analysis is conducted to test all hypotheses. All hypothesized relationships between SCCT constructs and technology interest are substantiated, proving the effectiveness of the refined learning model. It is concluded that the redefined “learning experiences” construct has three key dimensions with social integration as the most powerful predictor. It is also inferred that, while the new combined interventions appear to be more powerful predictors of pre-college and college student interest in computer technology than variables derived from SCCT traditional sources, using the new model has a limited impact on reducing the gender gap; it can be attributed to a time-factor in experimental design.

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