Today, blended learning is widely carried out in many colleges. Different online learning platforms have accumulated a large number of fine granularity records of students’ learning behavior, which provides us with an excellent opportunity to analyze students’ learning behavior. In this paper, based on the behavior log data in four consecutive years of blended learning in a college’s programming course, we propose a novel multiclassification frame to predict students’ learning outcomes. First, the data obtained from diverse platforms, i.e., MOOC, Cnblogs, Programming Teaching Assistant (PTA) system, and Rain Classroom, are integrated and preprocessed. Second, a novel error-correcting output codes (ECOC) multiclassification framework, based on genetic algorithm (GA) and ternary bitwise calculator, is designed to effectively predict the grade levels of students by optimizing the code-matrix, feature subset, and binary classifiers of ECOC. Experimental results show that the proposed algorithm in this paper significantly outperforms other alternatives in predicting students’ grades. In addition, the performance of the algorithm can be further improved by adding the grades of prerequisite courses.

中文翻译：

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使用多源数据进行混合学习中的成绩预测

如今，混合式学习在许多大学中广泛开展。不同的在线学习平台积累了大量关于学生学习行为的细粒度记录，这为我们分析学生的学习行为提供了绝佳的机会。在本文中，基于某大学编程课程连续四年混合学习的行为日志数据，我们提出了一种新颖的多分类框架来预测学生的学习成果。首先，对从MOOC、Cnblogs、编程助教（PTA）系统、雨课堂等不同平台获取的数据进行整合和预处理。其次，一种基于遗传算法（GA）和三元按位计算器的新型纠错输出码（ECOC）多分类框架，旨在通过优化ECOC的代码矩阵、特征子集和二元分类器来有效预测学生的年级水平。实验结果表明，本文提出的算法在预测学生成绩方面明显优于其他算法。此外，还可以通过增加先修课程的成绩来进一步提高算法的性能。