A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Reproducing Predictive Learning Analytics in CS1 (2024)


Zhidkikh, D., Heilala, V., Van Petegem, C., Dawyndt, P., Järvinen, M., Viitanen, S., De Wever, B., Mesuere, B., Lappalainen, V., Kettunen, L., & Hämäläinen, R. (2024). Reproducing Predictive Learning Analytics in CS1. Journal of Learning Analytics, Early Access. https://doi.org/10.18608/jla.2024.7979


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajatZhidkikh, Denis; Heilala, Ville; Van Petegem, Charlotte; Dawyndt, Peter; Järvinen, Miitta; Viitanen, Sami; De Wever, Bram; Mesuere, Bart; Lappalainen, Vesa; Kettunen, Lauri; et al.

Lehti tai sarjaJournal of Learning Analytics

eISSN1929-7750

Julkaisuvuosi2024

Ilmestymispäivä24.01.2024

VolyymiEarly Access

KustantajaUniversity of Technology Sydney

JulkaisumaaAustralia

Julkaisun kielienglanti

DOIhttps://doi.org/10.18608/jla.2024.7979

Julkaisun avoin saatavuusAvoimesti saatavilla

Julkaisukanavan avoin saatavuusOsittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX)https://jyx.jyu.fi/handle/123456789/93216


Tiivistelmä

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.


YSO-asiasanatopiskelijatopinnotoppiminenopintojen keskeyttäminen

Vapaat asiasanatpredictive learning analytics; CS1; retention; privacy; self-reported data; trace data; research paper


Liittyvät organisaatiot


Hankkeet, joissa julkaisu on tehty


OKM-raportointiKyllä

Raportointivuosi2024

Alustava JUFO-taso2

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Viimeisin päivitys 2024-13-05 klo 18:05