Using students’ cognitive, affective, and demographic characteristics to predict their understanding of computational thinking concepts

A machine learning-based approach

Bibliographic Data

ID21631267
AuthorsSiu Cheung Kong (0000-0002-8691-3016, Education University of Hong Kong, corresponding author), Wei Shen (0000-0002-3567-2726, Education University of Hong Kong)
Year2024
Volume32
Issue10
Pages7647-7660
Publication date2024-11-25
Open AccessNo
TypeARTICLE
VenueInteractive Learning Environments (JOURNAL)
Journal identifiersISSN: 1049-4820 • E-ISSN: 1744-5191
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/10494820.2024.2331148
OpenAlexW4392951402
LanguageEN
Citations received1
References cited32

Logistic regression models have traditionally been used to identify the factors contributing to students’ conceptual understanding. With the advancement of the machine learning-based research approach, there are reports that some machine learning algorithms outperform logistic regression models in terms of prediction. In this study, we collected cognitive, affective, and demographic data from 4,142 primary students as features to predict their understanding of computational thinking (CT) concepts. We identified and used five popularly used machine learning models. All five machine learning models outperformed the logistic regression model, with the extreme gradient boosting (XGBoost) model achieving the highest predictive accuracy. We used these features and the K-means algorithm of the unsupervised clustering technique to identify four optimal clusters of students. By comparing the representative students from each of the four clusters, selected by the t-distributed stochastic neighbour embedding algorithm, we found that each cluster had its characteristics. For example, one cluster consisted of students who outperformed students in the other three clusters in mathematics, scored highest in prior experience in programming, and used computers and the Internet most frequently. By identifying the characteristics of these clusters, pedagogical design, and resource support can be proposed to support students’ learning of CT concepts

Cognition · Cognitive psychology · Cognitive science · Computational Thinking · Educational technology · Mathematics education · Computer Science · Genetics, Bioinformatics, and Biomedical Research · Online Learning and Analytics · Psychology · Teaching and Learning Programming · Artificial Intelligence

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