Enrichment of Student Performance model using Collaborative Methods

G. Mehta, Mukesh Kumar, Alok Kumar Agrawal, Shivani Gautam
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Abstract

These days, educational data mining is a new research area that is being used for data exploration in educational settings for the prediction of student’s performance. In online educational system, the behavioral features of learners play an important role for judging the interaction between students and the Learning Management System. Here, in this article, new features known as behavioral features of students is used and performance is evaluated using classifiers such as Support Vector Machine, K-Nearest Neighbor and Decision Tree. Moreover, in order to enhance the classifier’s performance, the collaborative methods such as Bagging, Boosting and Random Forest are used. Accuracy of 87.4% was archived when the collaborative techniques were applied to the classifiers for improving the performance in academics.
利用协作方法丰富学生绩效模型
如今,教育数据挖掘是一个新的研究领域,它被用于教育环境中的数据探索,以预测学生的表现。在在线教育系统中,学习者的行为特征对判断学生与学习管理系统之间的互动起着重要的作用。在这里,在这篇文章中,我们使用了被称为学生行为特征的新特征,并使用支持向量机、k近邻和决策树等分类器来评估学生的表现。此外,为了提高分类器的性能,还采用了Bagging、Boosting和Random Forest等协同方法。将协作技术应用于分类器以提高学术性能,准确率达到87.4%。
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