IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i17p12917-d1226175.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/17/12917/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/17/12917/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Schneider, Kerstin & Berens, Johannes & Oster, Simon & Burghoff, Julian, 2018. "Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181544, Verein für Socialpolitik / German Economic Association.
    2. 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.
    3. Silvia Bacci & Bruno Bertaccini, 2022. "A Mixture Hidden Markov Model to Mine Students’ University Curricula," Data, MDPI, vol. 7(2), pages 1-19, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dursun Delen & Behrooz Davazdahemami & Elham Rasouli Dezfouli, 2024. "Predicting and Mitigating Freshmen Student Attrition: A Local-Explainable Machine Learning Framework," Information Systems Frontiers, Springer, vol. 26(2), pages 641-662, April.
    2. Dunhong Yao & Xian Zhang & Yiwen Liu, 2022. "Teaching Reform in C Programming Course from the Perspective of Sustainable Development: Construction and 9-Year Practice of “Three Classrooms–Four Integrations–Five Combinations” Teaching Model," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
    3. Isphording, Ingo E. & Raabe, Tobias, 2019. "Early Identification of College Dropouts Using Machine-Learning: Conceptual Considerations and an Empirical Example," IZA Research Reports 89, Institute of Labor Economics (IZA).
    4. Bianca-Raluca Cibu & Camelia Delcea & Adrian Domenteanu, 2024. "Determinants In Modelling Early School Dropout," Cactus - The tourism journal for research, education, culture and soul, Bucharest University of Economic Studies, vol. 6(2), pages 6-21.
    5. Behr Andreas & Giese Marco & Teguim K Herve D. & Theune Katja, 2020. "Early Prediction of University Dropouts – A Random Forest Approach," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 240(6), pages 743-789, December.
    6. Contini, Dalit & Salza, Guido, 2020. "Too few university graduates. Inclusiveness and effectiveness of the Italian higher education system," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12917-:d:1226175. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.