Academic Bibliography

 Search 200 years of publications by Ghent University researchers.
Advanced search
1 file | 1.01 MB
Add to list
  1. Search results
  2. Reproducing predictive learning analy...

Reproducing predictive learning analytics in CS1 : toward generalizable and explainable models for enhancing student retention

Denis Zhidkikh, Ville Heilala, Charlotte Van Petegem (UGent) , Peter Dawyndt (UGent) , Miitta Järvinen, Sami Viitanen, Bram De Wever (UGent) , Bart Mesuere (UGent) , Vesa Lappalainen, Lauri Kettunen, et al.
(2024) JOURNAL OF LEARNING ANALYTICS. 11(1).
Author
Organization
Project
Abstract
Predictive learning analytics has been widely explored in educational research to improve student retention and academic success in an introductory programming course in computer science (CS1). General-purpose and interpretable dropout predictions still pose a challenge. Our study aims to reproduce and extend the data analysis of a privacy-first student pass–fail prediction approach proposed by Van Petegem and colleagues (2022) in a different CS1 course. Using student submission and self-report data, we investigated the reproducibility of the original approach, the effect of adding self-reports to the model, and the interpretability of the model features. The results showed that the original approach for student dropout prediction could be successfully reproduced in a different course context and that adding self-report data to the prediction model improved accuracy for the first four weeks. We also identified relevant features associated with dropout in the CS1 course, such as timely submission of tasks and iterative problem solving. When analyzing student behaviour, submission data and self-report data were found to complement each other. The results highlight the importance of transparency and generalizability in learning analytics and the need for future research to identify other factors beyond self-reported aptitude measures and student behaviour that can enhance dropout prediction.
Keywords
Computer Science Applications, Education, Predictive learning analytics, CS1, retention, privacy, self-report data, trace data

Downloads

  • Download
    7979-Article Text-40423-1-10-20240124.pdf
    • full text (Published version)
    • |
    • open access
    • |
    • PDF
    • |
    • 1.01 MB

Citation

Please use this url to cite or link to this publication:

MLA
Zhidkikh, Denis, et al. “Reproducing Predictive Learning Analytics in CS1 : Toward Generalizable and Explainable Models for Enhancing Student Retention.” JOURNAL OF LEARNING ANALYTICS, vol. 11, no. 1, 2024, doi:10.18608/jla.2024.7979.
APA
Zhidkikh, D., Heilala, V., Van Petegem, C., Dawyndt, P., Järvinen, M., Viitanen, S., … Hämäläinen, R. (2024). Reproducing predictive learning analytics in CS1 : toward generalizable and explainable models for enhancing student retention. JOURNAL OF LEARNING ANALYTICS, 11(1). https://doi.org/10.18608/jla.2024.7979
Chicago author-date
Zhidkikh, Denis, Ville Heilala, Charlotte Van Petegem, Peter Dawyndt, Miitta Järvinen, Sami Viitanen, Bram De Wever, et al. 2024. “Reproducing Predictive Learning Analytics in CS1 : Toward Generalizable and Explainable Models for Enhancing Student Retention.” JOURNAL OF LEARNING ANALYTICS 11 (1). https://doi.org/10.18608/jla.2024.7979.
Chicago author-date (all authors)
Zhidkikh, Denis, Ville Heilala, Charlotte Van Petegem, Peter Dawyndt, Miitta Järvinen, Sami Viitanen, Bram De Wever, Bart Mesuere, Vesa Lappalainen, Lauri Kettunen, and Raija Hämäläinen. 2024. “Reproducing Predictive Learning Analytics in CS1 : Toward Generalizable and Explainable Models for Enhancing Student Retention.” JOURNAL OF LEARNING ANALYTICS 11 (1). doi:10.18608/jla.2024.7979.
Vancouver
1.
Zhidkikh D, Heilala V, Van Petegem C, Dawyndt P, Järvinen M, Viitanen S, et al. Reproducing predictive learning analytics in CS1 : toward generalizable and explainable models for enhancing student retention. JOURNAL OF LEARNING ANALYTICS. 2024;11(1).
IEEE
[1]
D. Zhidkikh et al., “Reproducing predictive learning analytics in CS1 : toward generalizable and explainable models for enhancing student retention,” JOURNAL OF LEARNING ANALYTICS, vol. 11, no. 1, 2024.
@article{01HMZYBPT26V8ZRQEGH5AH2P9J,
  abstract     = {{Predictive learning analytics has been widely explored in educational research to improve student retention and academic success in an introductory programming course in computer science (CS1). General-purpose and interpretable dropout predictions still pose a challenge. Our study aims to reproduce and extend the data analysis of a privacy-first student pass–fail prediction approach proposed by Van Petegem and colleagues (2022) in a different CS1 course. Using student submission and self-report data, we investigated the reproducibility of the original approach, the effect of adding self-reports to the model, and the interpretability of the model features. The results showed that the original approach for student dropout prediction could be successfully reproduced in a different course context and that adding self-report data to the prediction model improved accuracy for the first four weeks. We also identified relevant features associated with dropout in the CS1 course, such as timely submission of tasks and iterative problem solving. When analyzing student behaviour, submission data and self-report data were found to complement each other. The results highlight the importance of transparency and generalizability in learning analytics and the need for future research to identify other factors beyond self-reported aptitude measures and student behaviour that can enhance dropout prediction.}},
  articleno    = {{7979}},
  author       = {{Zhidkikh, Denis and Heilala, Ville and Van Petegem, Charlotte and Dawyndt, Peter and Järvinen, Miitta and Viitanen, Sami and De Wever, Bram and Mesuere, Bart and Lappalainen, Vesa and Kettunen, Lauri and Hämäläinen, Raija}},
  issn         = {{1929-7750}},
  journal      = {{JOURNAL OF LEARNING ANALYTICS}},
  keywords     = {{Computer Science Applications,Education,Predictive learning analytics,CS1,retention,privacy,self-report data,trace data}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{21}},
  title        = {{Reproducing predictive learning analytics in CS1 : toward generalizable and explainable models for enhancing student retention}},
  url          = {{http://doi.org/10.18608/jla.2024.7979}},
  volume       = {{11}},
  year         = {{2024}},
}
Altmetric
View in Altmetric