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- W4313225752 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." @default.
- W4313225752 created "2023-01-06" @default.
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- W4313225752 date "2022-10-29" @default.
- W4313225752 modified "2023-09-27" @default.
- W4313225752 title "Identify Students at Risk Based on Behavioural Patterns in Continuous Assessment" @default.
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- W4313225752 doi "https://doi.org/10.1109/besc57393.2022.9995635" @default.
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