Predicting Secondary School Students' Performance Utilizing a Semi-supervised Learning Approach
Predicting Secondary School Students' Performance Utilizing a Semi-supervised Learning Approach
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DOI：
10.1177/0735633117752614
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发表时间：
2019-04-01
影响因子：
4.8
通讯作者：
Pintelas, Panagiotis
中科院分区：
文献类型：
--
作者：
Livieris, Ioannis E.;Drakopoulou, Konstantina;Pintelas, Panagiotis
教育数据挖掘是一个新兴的研究领域，由于其能够监测学生的学习成绩和预测未来的发展，在过去的十年中得到了普及。许多机器学习技术，特别是监督学习算法已被应用于开发准确的模型来预测学生的特征，从而诱导他们的行为和表现。在这项工作中，我们检查和评估的有效性的两个包装方法的半监督学习算法预测学生的表现在期末考试。我们初步的数值实验表明，半监督方法的优点是，可以显着提高分类精度，利用一些标记和许多未标记的数据开发可靠的预测模型。
Educational data mining constitutes a recent research field which gained popularity over the last decade because of its ability to monitor students' academic performance and predict future progression. Numerous machine learning techniques and especially supervised learning algorithms have been applied to develop accurate models to predict student's characteristics which induce their behavior and performance. In this work, we examine and evaluate the effectiveness of two wrapper methods for semisupervised learning algorithms for predicting the students' performance in the final examinations. Our preliminary numerical experiments indicate that the advantage of semisupervised methods is that the classification accuracy can be significantly improved by utilizing a few labeled and many unlabeled data for developing reliable prediction models.