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Analysis of Machine Learning Classification Approaches for Predicting Students’ Programming Aptitude

Author

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  • Ali Çetinkaya

    (Department of Computer Engineering, Konya Technical University, Konya 42250, Türkiye)

  • Ömer Kaan Baykan

    (Department of Computer Engineering, Konya Technical University, Konya 42250, Türkiye)

  • Havva Kırgız

    (Konya Science Center, Konya 42100, Türkiye)

Abstract

With the increasing prevalence and significance of computer programming, a crucial challenge that lies ahead of teachers and parents is to identify students adept at computer programming and direct them to relevant programming fields. As most studies on students’ coding abilities focus on elementary, high school, and university students in developed countries, we aimed to determine the coding abilities of middle school students in Turkey. We first administered a three-part spatial test to 600 secondary school students, of whom 400 completed the survey and the 20-level Classic Maze course on Code.org. We then employed four machine learning (ML) algorithms, namely, support vector machine (SVM), decision tree, k-nearest neighbor, and quadratic discriminant to classify the coding abilities of these students using spatial test and Code.org platform data. SVM yielded the most accurate results and can thus be considered a suitable ML technique to determine the coding abilities of participants. This article promotes quality education and coding skills for workforce development and sustainable industrialization, aligned with the United Nations Sustainable Development Goals.

Suggested Citation

  • Ali Çetinkaya & Ömer Kaan Baykan & Havva Kırgız, 2023. "Analysis of Machine Learning Classification Approaches for Predicting Students’ Programming Aptitude," Sustainability, MDPI, vol. 15(17), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12917-:d:1226175
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    References listed on IDEAS

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    1. Wala Bagunaid & Naveen Chilamkurti & Prakash Veeraraghavan, 2022. "AISAR: Artificial Intelligence-Based Student Assessment and Recommendation System for E-Learning in Big Data," Sustainability, MDPI, vol. 14(17), pages 1-22, August.
    2. Silvia Bacci & Bruno Bertaccini, 2022. "A Mixture Hidden Markov Model to Mine Students’ University Curricula," Data, MDPI, vol. 7(2), pages 1-19, February.
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