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.
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The authors would like to express their sincere gratitude to all volunteers, lecturers, and staff at the School of Information Technology (SIT), King Mongkut’s University of Technology, Thonburi (KMUTT) for assisting in data collection process. The main author would like to express her grateful appreciation and thanks to Petchra Pra Jom Klao Ph.D. Research Scholarship for granting a full Ph.D. scholarship. She also wants to thank the School of Information Technology (SIT) for all support.
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Ninrutsirikun, U., Imai, H., Watanapa, B. et al. Principal Component Clustered Factors for Determining Study Performance in Computer Programming Class. Wireless Pers Commun (2020). https://doi.org/10.1007/s11277-020-07194-5
- Achievement in computer programming
- Best subset selection
- Feature extraction
- Optimal features
- Principal components