Prediction of Students’ Performance in e-Learning Environment using Data Mining/ Machine Learning Techniques

Brijesh K. Verma, Nidhi Srivastava, H. Singh
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引用次数: 6

Abstract

The COVID-19 pandemic has drastically changed the way od of learning. During this pandemic the learning has shifted from offline to online. student’s performance prediction based on their relevant information has emerged new area for educational institutions for improving teaching learning process, changes in course curriculum. Machine leaning technology can be helpful in predicting the performance of student and accordingly the institutions can make required changes in in their lecture delivery and curriculum. This paper utilized some machine learning methodologies to predict the students’ performance. Educational data of open University(OU) is analysed Based on parameters that are demographic, engagement and performance. In the experimental analysis. In the experimental analysis, the k-NN approach performed best in some cases and ANN performed best in other cases among all compared algorithms on OU dataset.
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利用数据挖掘/机器学习技术预测学生在电子学习环境中的表现
2019冠状病毒病大流行彻底改变了学习方式。在这次大流行期间,学习已从线下转移到线上。基于其相关信息的学生成绩预测已成为教育机构改进教学过程、改变课程设置的新领域。机器学习技术可以帮助预测学生的表现,因此机构可以在他们的讲座和课程中做出必要的改变。本文利用一些机器学习方法来预测学生的表现。开放大学的教育数据是根据人口统计、参与度和绩效等参数进行分析的。在实验分析中。在实验分析中,在OU数据集的所有比较算法中,k-NN方法在某些情况下表现最好,而ANN方法在其他情况下表现最好。
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