An Artificial Neural Network Based Early Prediction of Failure-Prone Students in Blended Learning Course
An Artificial Neural Network Based Early Prediction of Failure-Prone Students in Blended Learning Course
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DOI：
10.3991/ijet.v14i19.10366
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Otgontsetseg Sukhbaatar;T. Usagawa;Lodoiravsal Choimaa
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文献类型：
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作者：
Otgontsetseg Sukhbaatar;T. Usagawa;Lodoiravsal Choimaa
等级高等教育绩效评估的目标之一是降低学生的不合格率。要识别和减少不及格学生的数量，必须对学生在课堂上的学习活动和行为进行持续监测；然而，监测大量学生是一项极其困难的任务。基于网络的学习系统在学术机构中的普及揭示了通过这些系统评估学生活动的可能性。在本文中，我们提出了一种早期预测方案，以识别在混合学习课程中失败的学生的风险。在一个学习管理系统中，我们使用神经网络对从学生在线学习活动中提取的预测变量集进行处理。这些实验基于1110名学生的数据，这些学生参加了一门必修的二年级课程。结果表明，基于神经网络的方法可以实现对可能不及格的学生的早期识别；25%的不及格学生在第一次提交测验后被正确识别。期中考试后，65%的不及格学生被正确预测。
One of the objectives of the performance measurement of grade-based higher education is to reduce the failure rate of students. To identify and reduce the number of failing students, the learning activities and behaviors of students in the classroom must be continuously monitored; however, monitoring a large number of students is an extremely difficult task. A penetration of web-based learning systems in academic institutions revealed the possibility of evaluating student activities via these systems. In this paper, we propose an early prediction scheme to identify students at risk of failing in a blended learning course. We employ a neural network on the set of prediction variables extracted from the online learning activities of students in a learning management system. The experiments were based on data from 1110 student who attended a compulsory, sophomore-level course. The results indicate that a neural-network-based approach can achieve early identification of students that are likely to fail; 25% of the failing students were correctly identified after the first quiz submission. After the mid-term examination, 65% of the failing students were correctly predicted.