Identify Students at Risk Based on Behavioural Patterns in Continuous Assessment
2022 9th International Conference on Behavioural and Social Computing (BESC)(2022)
摘要
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.
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关键词
academic performance prediction,education data mining,at-risk students detection,machine learning,e-learning
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