Abstract
While modelling students’ learning behavior or preferences has been found as a crucial indicator for their course achievement, very few studies have considered it in predicting 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 model constitute a comprehensive students’ learning behavioral pattern which is later used for 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 a high accuracy through modelling their learning behavior during online courses.
Highlights
Higher education establishments play a key role in today’s world and contribute immensely to people’s lives by training highly skilled students
Contributions of this work include the following: (1) to provide educators with opportunities to identify students that are not performing well; (2) to take into account comprehensive students’ learning behavioral pattern in prediction of their course grade by developing students learning behavior model for each student in a course; (3) to develop an easy to implement and interpret student modelling approach that is generalizable to different online courses; and (4) to employ a multiple criteria decision-making method for automatically evaluating various classification methods to find the most suitable one for various datasets in hand
For each course, we extracted different types of action data, such as number of times course resource viewed, course modules viewed, course materials downloaded, feedback viewed, feedback received, forum discussion viewed, quizzes answered, discussion created in forum, book chapters viewed, book list viewed, assignment submitted, assignment viewed, discussion viewed in forum, post created in forum, comments viewed, posts updated in forum, and of posts, assignment grade, quiz grades, and final grade
Summary
Higher education establishments play a key role in today’s world and contribute immensely to people’s lives by training highly skilled students. While these institutes successfully graduate thousands of students across the world, they face the issue of student’s failure in courses, leading to delay in their graduation or their dropout. Erefore, predicting students’ grades or achievement in courses could be one way to identify students at risk of failure (e.g., [9, 10]). Such predictions would enable teachers to provide students with timely feedback or additional support. It can be argued that equipping online educational systems (such as learning management systems, LMSs) with automated approaches to predict students’ performance or achievement in a course is crucial
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