Identify Students at Risk Based on Behavioural Patterns in Continuous Assessment

Zongxi LI, Haoran XIE, Fu Lee WANG*, Weiming WANG, Man-Kong CHOW

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

1 Citation (Scopus)

Abstract

Students' success is the ultimate goal of any institution around the world. Early detection of at-risk students can facilitate the instructor or tutor to provide timely support to those at risk of failing the course. In a traditional face-to-face classroom, students can monitor learning patterns in routine interactions. However, teachers in the online classroom have limited information, compared with the face-to-face classroom, to detect students in trouble due to the lack of instance interactions between teachers and students. Particularly, such a problem has become worse than ever since 2020, as online teaching and learning are ubiquitous in the Post-COVID19 Era. In this work, we aim to predict if the student obtains a low course grade based on their behavioral patterns in continuous assessments, which are easy-to-retrieve attributes and available in most e-learning systems. We leverage the ratio of assessment grade to the time spent on the assessment as a useful feature in the machine-learning prediction framework. Experiments on real-world datasets indicate that such a ratio can improve the accuracy of detecting at-risk students.

Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350398144
DOIs
Publication statusPublished - 28 Dec 2022
Event9th IEEE International Conference on Behavioural and Social Computing, BESC 2022 - Matsuyama, Japan
Duration: 29 Oct 202231 Oct 2022

Publication series

NameProceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022

Conference

Conference9th IEEE International Conference on Behavioural and Social Computing, BESC 2022
Country/TerritoryJapan
CityMatsuyama
Period29/10/2231/10/22

Bibliographical note

Funding Information:
The research has been supported by Lam Woo Research Fund (LWP20019) and the Faculty Research Grants (DB22B4) of Lingnan University, Hong Kong.

Publisher Copyright:
© 2022 IEEE.

Keywords

  • academic performance prediction
  • at-risk students detection
  • e-learning
  • education data mining
  • machine learning

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