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Assessing Student Behavior in Computer Science Education with an fsQCA Approach

Assessing Student Behavior in Computer Science Education with an fsQCA Approach This study uses complexity theory to understand the causal patterns of factors that stimulate students intention to continue studies in computer science (CS). To this end, it identifies gains and barriers as essential factors in CS education, including motivation and learning performance, and proposes a conceptual model along with research propositions. To test its propositions, the study employs fuzzy-set qualitative comparative analysis on a data sample from 344 students. Findings indicate eight configurations of cognitive and noncognitive gains, barriers, motivation for studies, and learning performance that explain high intention to continue studies in CS. This research study contributes to the literature by (1) offering new insights into the relationships among the predictors of CS students intention to continue their studies and (2) advancing the theoretical foundation of how students gains, barriers, motivation, and learning performance combine to better explain high intentions to continue CS studies. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Computing Education (TOCE) Association for Computing Machinery

Assessing Student Behavior in Computer Science Education with an fsQCA Approach

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 ACM
ISSN
1946-6226
eISSN
1946-6226
DOI
10.1145/3036399
Publisher site
See Article on Publisher Site

Abstract

This study uses complexity theory to understand the causal patterns of factors that stimulate students intention to continue studies in computer science (CS). To this end, it identifies gains and barriers as essential factors in CS education, including motivation and learning performance, and proposes a conceptual model along with research propositions. To test its propositions, the study employs fuzzy-set qualitative comparative analysis on a data sample from 344 students. Findings indicate eight configurations of cognitive and noncognitive gains, barriers, motivation for studies, and learning performance that explain high intention to continue studies in CS. This research study contributes to the literature by (1) offering new insights into the relationships among the predictors of CS students intention to continue their studies and (2) advancing the theoretical foundation of how students gains, barriers, motivation, and learning performance combine to better explain high intentions to continue CS studies.

Journal

ACM Transactions on Computing Education (TOCE)Association for Computing Machinery

Published: May 23, 2017

Keywords: Higher education

References