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6. Mining Assignment Submission Time to Detect At-Risk Students with Peer Information

Author

Wang, Yuancheng, Luo, Nanyu, and Zhou, Jianjun

Abstract

Doing assignments is a very important part of learning. Students' assignment submission time provides valuable information on study attitudes and habits which strongly correlate with academic performance. However, the number of assignments and their submission deadlines vary among university courses, making it hard to use assignment submission time as a feature to predict academic performance. In this paper, we propose a new method called Relative Assignment Submission Time (RAST) which uses the assignment submission information of peer students to improve the correlation with course grades. Experiments on real-life data of 20 courses show that RAST has a high correlation with students' academic performance. We also build a machine learning model using RAST as a feature to detect students who would suffer from poor grades. Our method outperforms the traditional method by up to 61% on f1-score. We believe that our proposed method can help other studies on assignment submission time to improve the prediction accuracy on academic performance and detecting at-risk students. [For the full proceedings, see ED623995.]

Published
2022

13. Assessing Attendance by Peer Information

Author

Deng, Pan, Zhou, Jianjun, Lyu, Jing, and Zhao, Zitong

Abstract

Attendance rate is an important indicator of students' study motivation, behavior and Psychological status; however, the heterogeneous nature of student attendance rates due to the course registration difference or the online/offline difference in a blended learning environment makes it challenging to compare attendance rates. In this paper, we propose a novel method called Relative Attendance Index (RAI) to measure attendance rates, which reflects students' efforts on attending courses. While traditional attendance focuses on the record of a single person or course, relative attendance emphasizes peer attendance information of relevant individuals or courses, making the comparisons of attendance more justified. Experimental results on real-life data show that RAI can indeed better reflect student engagement. [For the full proceedings, see ED615472.]

Published
2021

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