Understanding Student Learning Behavior and Predicting Their Performance
Understanding Student Learning Behavior and Predicting Their Performance
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DOI：
10.4018/978-1-5225-9031-6.ch001
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
M. Wasif;Hajra Waheed;Naif R. Aljohani;Saeed-Ul Hassan
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作者：
M. Wasif;Hajra Waheed;Naif R. Aljohani;Saeed-Ul Hassan
尽管采用在线教育平台的人数有所增加，但学生保留率仍然是一项具有挑战性的任务，许多学生在这些课程中的表现幅度很低。本章旨在使用公开的开放大学学习分析数据集，根据学生的学习行为，基于他们的日志数据历史，预测学生的表现。为了模拟这个问题，逻辑回归（LR）被用作基线技术。此外，还部署了随机森林（RF），具有多个激活函数的多层感知器和高斯朴素贝叶斯。结果表明，RF优于基线LR和其他模型，准确率为89%，精确率为89%，召回率为88%，F1分数为88%。最后，作者的结论是，使用上述模型，学生的“风险”可以识别，可以通过一个警报机制进行管理，以提高学生的成功率，及时干预。
Despite the increase in the adoption of online educational platforms, student retention is still a challenging task with a number of students having low performance margins during these courses. This chapter intends to predict student performance based on their learning behavior on the basis of their logging data history, using the publicly available Open University Learning Analytics Dataset. To model this problem, logistic regression (LR) is used as a baseline technique. Additionally, random forest (RF), multiple layered perceptron with multiple activation functions, and Gaussian Naïve Bayes are also deployed. The results demonstrate that RF outperforms the baseline LR and other models with 89% accuracy, 89% precision, 88% recall, and 88% F1-score. Finally, the authors conclude that using the above-mentioned models, students “at-risk” can be identified which can be managed by an alert mechanism to improve student success rate by making timely interventions.