Using Machine Learning to Explore the Relation Between Student Engagement and Student Performance

Abstract

Engagement in learning activities is an important factor that affects student performance in education. According to research, student engagement involves the degree of passion, interest and attention that they exhibit in their educational environment. In the traditional learning system, educators encourage students to engage in their learning activities through various teaching strategies such as making them pay attention, take notes, ask questions and participate actively in the learning processes. Sometimes, educators call on a specific student to answer a question as a means of encouraging the student to participate in learning processes. Nowadays, engagement strategies for learning are changing, especially with the use of technology-enhanced learning systems (TELS) in education. As a result, improving the engagement level of students in online learning environments remains an open research question that needs to be explored. This research is part of a preliminary study on discovering ways of increasing student engagement in an online learning system through data-driven interventions. Student engagement in this research is determined using objective data (activity logs of a specific undergraduate course in a TELS). Activity log is unbiased data and a reflection of students' actual learning behaviours (uncontrolled). In this study, we mined the log of students' learning activities from a TELS used for an undergraduate course to explore differences between students' learning behaviours as they relate to their engagement level and academic performance (measured in terms of final grade points in a course). We employed supervised (Random Forest) and unsupervised (Clustering) machine learning approaches in exploring the relations. The approaches identified an interesting pattern on student engagement and show that engagement and assessment scores are good predictors of student academic performance. Assessment scores are measured with results of quizzes and assignments performed by the students in the TELS, while academic performance is measured with the final grade of the student in the course. The implications of our findings are discussed.