Zhiyao Niu
ABSTRACT
With the booming of massive open online courses (MOOCs) in recent years, high drop-out rate and low completion rate remain major impediments. Analyzing the behavior of learners and exploring the reasons of dropout of course are of great significance for evaluating the teaching quality and improving the teaching effect. As educational data mining and machine learning rise, it is possible to explore the causes of high dropout rate and low completion rate on MOOCs. Here, we conducted analysis on data from five multi-disciplinary courses on icourse 163 platform and extracted more than 50 features for evaluating causes of withdrawing on MOOCs. A cause exploration method based on gradient boosting decision tree was implemented to extract the essential features that influence the learner's performance with the importance of each feature calculated. To get the best performed classification model, Cross-Validation was used in modeling and Grid Search was employed in parameter optimization. According to the importance of each feature, different subsets of features were selected and tested in turn. The results indicated nine features being the main factors affecting dropout. It could be expected to provide a guideline for a basis for evidence-based improvement of online education.
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