Investigating Learners' Behaviors and Implementing Intervention in a SPOC

This Work-In-Progress paper is in the Innovative Practice category. In the MOOC-related research field, many researchers analyzed students' learning behavior based on the logging data to predict students' performance and improve the course design. Nowadays, Small Private Online Courses (SPOC) are favored in college education, especially in computing education. This hybrid teaching model allows courses to be conducted through Internet, which enables teachers and students to access the course anytime, anywhere. Besides, multimedia resources, including images, videos, and audio could be contained in course materials to strengthen the expressiveness of SPOC. On the other hand, the online learning management system (LMS) collects all the students' interactions with it. But how could we extract meaningful information from them? And how could we improve the learning outcomes of a SPOC? In this study, we analyzed LMS data from a sophomore Computer Structure course. We applied several data mining techniques and conducted an intervention using several visualization techniques. Features were selected according to Spearman's rank correlation coefficient with grades. The correlation coefficient of these selected features ranged from 0.42 to 0.84. Course data were further processed to predict students' performance. The predicted grade was processed in the form of heatmaps to illustrate students' learning behavior. Besides, we further designed an overall view for teachers' perspective, which contains data of all the students in each heatmap. The predicting models were evaluated by ROC-AVC values. Several hyperparameters were tuned in order to pursue better predict performance. The best ROC-AVC value could reach 97.44%.