Principal Component Clustered Factors for Determining Study Performance in Computer Programming Class

Journal article

Authors/Editors

Strategic Research Themes

Creative and Learning Society (Strategic Research Themes)

Publication Details

Author list: Ninrutsirikun, Unhawa; Imai, Hideyuki; Watanapa, Bunthit; Arpnikanondt, Chonlameth;

Publisher: Springer

Publication year: 2020

Journal: Wireless Personal Communications (0929-6212)

Volume number: 115

Issue number: 4

Start page: 2897

End page: 2916

Number of pages: 20

ISSN: 0929-6212

eISSN: 1572-834X

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079487561&doi=10.1007%2fs11277-020-07194-5&partnerID=40&md5=c0e9ab19d5a600c76e9cdd2de264ab09

Languages: English-Great Britain (EN-GB)

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Abstract

Studying computer programming requires not only an understanding of theories and concepts, but also coding pragmatism. Success in studying or conducting such a course is definitely a challenge. This paper proposes a model that transforms students’ attributes (including the cognitive and non-cognitive abilities, and traditional lagging measures of academic background) into a set of principal components (PCs). As opposed to traditional approaches, the proposed model optimally extracts the orthogonal PCs to form a basis for determining the studying performance of students in terms of declarative knowledge and procedural proficiency (or skill). The obtained relationship model yields two contributive values (1) an optimal set of determinants, in the form of students’ clusters, to determine study performance and (2) the fully preserved interpretability of the original attributes of students in each PC. The experiment was conducted using 115 complete datasets of IT major students who enrolled the Introduction to Computer Programming course. The Best Subset Selection and LASSO algorithms were deployed to find the optimal set of features. The effectiveness of the model was validated by multiple linear regression to predict the performance in terms of knowledge and skills with an accuracy of 76.52%, and 70.44%, respectively. Insights into the interpretability of student clusters are provided. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

Achievement in computer programming, Best subset selection, Feature extraction, LASSO, Optimal features, Principal components