Integrating Learning Analytics and Collaborative Learning for Improving Student's Academic Performance

Big data analytics has shown tremendous success in several fields such as businesses, agriculture, health, and meteorology, and education is no exception. Concerning its role in education, it is used to boost students' learning process by predicting their performance in advance and adapting the relevant instructional design strategies. This study primarily intends to develop a system that can predict students' performance and help teachers to timely introduce corrective interventions to uplift the performance of low-performing students. As a secondary part of this research, it also explores the potential of collaborative learning as an intervention to act in combination with the prediction system to improve the performance of students. To support such changes, a visualization system is also developed to track and monitor the performance of students, groups, and overall class to help teachers in the regrouping of students concerning their performance. Several well-known machine learning models are applied to predict students performance. Results suggest that experimental groups performed better after treatment than before treatment. The students who took part in each class activity, prepared and submitted their tasks perform much better than other students. Overall, the study found that collaborative learning methods play a significant role to enhance the learning capability of the students.