National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan 430079, China.
Sensors (Basel). 2021 Oct 5;21(19):6629. doi: 10.3390/s21196629.
In recent years, massive open online courses (MOOCs) have received widespread attention owing to their flexibility and free access, which has attracted millions of online learners to participate in courses. With the wide application of MOOCs in educational institutions, a large amount of learners' log data exist in the MOOCs platform, and this lays a solid data foundation for exploring learners' online learning behaviors. Using data mining techniques to process these log data and then analyze the relationship between learner behavior and academic performance has become a hot topic of research. Firstly, this paper summarizes the commonly used predictive models in the relevant research fields. Based on the behavior log data of learners participating in 12 courses in MOOCs, an entropy-based indicator quantifying behavior change trends is proposed, which explores the relationships between behavior change trends and learners' academic performance. Next, we build a set of behavioral features, which further analyze the relationships between behaviors and academic performance. The results demonstrate that entropy has a certain correlation with the corresponding behavior, which can effectively represent the change trends of behavior. Finally, to verify the effectiveness and importance of the predictive features, we choose four benchmark models to predict learners' academic performance and compare them with the previous relevant research results. The results show that the proposed feature selection-based model can effectively identify the key features and obtain good prediction performance. Furthermore, our prediction results are better than the related studies in the performance prediction based on the same Xuetang MOOC platform, which demonstrates that the combination of the selected learner-related features (behavioral features + behavior entropy) can lead to a much better prediction performance.
近年来，大规模在线开放课程（MOOC）因其灵活性和免费访问而受到广泛关注，吸引了数百万在线学习者参与课程。随着 MOOC 在教育机构中的广泛应用，MOOC 平台中存在大量学习者的日志数据，这为探索学习者在线学习行为奠定了坚实的数据基础。使用数据挖掘技术处理这些日志数据，然后分析学习者行为与学业成绩之间的关系，已成为研究的热点。首先，本文总结了相关研究领域中常用的预测模型。基于学习者参与 MOOC 中 12 门课程的行为日志数据，提出了一种基于熵的量化行为变化趋势的指标，探讨了行为变化趋势与学习者学业成绩之间的关系。然后，我们构建了一组行为特征，进一步分析了行为与学业成绩之间的关系。结果表明，熵与相应的行为具有一定的相关性，能够有效表示行为的变化趋势。最后，为了验证预测特征的有效性和重要性，我们选择了四个基准模型来预测学习者的学业成绩，并与之前的相关研究结果进行了比较。结果表明，基于特征选择的模型可以有效地识别关键特征并获得良好的预测性能。此外，我们的预测结果优于基于相同学堂在线 MOOC 平台的相关研究在学业成绩预测方面的结果，表明所选学习者相关特征（行为特征+行为熵）的组合可以带来更好的预测性能。
