Abstract
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.
Highlights
In recent years, online education platforms have increased with the rapid development of Internet technology; and the mixed teaching mode combining offline face-to-face teaching and online learning has emerged
We propose a novel error-correcting output codes (ECOC) multiclassification framework to predict students’ grades
The results of the grade-1 dataset are optimized by 1.97%, 4.42%, and 1.23%; and the grade-2 dataset’s results are improved by about 3.17%, 3.17%, and 7.66%, respectively, based on other algorithms
Summary
Online education platforms have increased with the rapid development of Internet technology; and the mixed teaching mode combining offline face-to-face teaching and online learning has emerged. Under the mixed teaching mode, students self-regulate the learning process, and teachers supervise students’ learning progress as well as efficiency in real time. Integrating online evaluation with offline evaluation, the students’ learning autonomy increases while learning efficiency is guaranteed. At the same time, combining with the analysis of students’ online learning behavior trajectory and offline classroom performance, we can infer the students’ phased learning situation to adjust the teaching pace in time and further improve the teaching quality. Erefore, it is worth studying how to transform students’ learning data such as their online learning behavior track and offline classroom performance into their phased learning situation and take corresponding measures to adjust and guide their personalized learning to achieve better teaching effect.
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