Predicting Course Grades Through Comprehensive Modeling of Students’ Learning Behavioral Patterns - Danial Hooshyar; Yeongwook Yang (Complexity)
While modeling students’ learning behavior or preferences has been found to be a crucial indicator for their course achievement, very few studies have considered it in predicting the achievement of students in online courses. This study aims to model students’ online learning behavior and accordingly predict their course achievement. First, feature vectors are developed using their aggregated action logs during a course. Second, some of these feature vectors are quantified into three numeric values that are used to model students’ learning behavior, namely, accessing learning resources (content access), engaging with peers (engagement), and taking assessment tests (assessment). Both students’ feature vectors and behavior models constitute a comprehensive student’s learning behavioral pattern which is later used for the prediction of their course achievement. Lastly, using a multiple-criteria decision-making method (i.e., TOPSIS), the best classification methods were identified for courses with different sizes. Our findings revealed that the proposed generalizable approach could successfully predict students’ achievement in courses with different numbers of students and features, showing the stability of the approach. Decision tree and AdaBoost classification methods appeared to outperform other existing methods on different datasets. Moreover, our results provide evidence that it is feasible to predict students’ course achievement with high accuracy through modeling their learning behavior during online courses.