Early prediction of student engagement in virtual learning environments using machine learning techniques

Learning analytics aims at helping the students to attain their learning goals. The predictions in learning analytics are made to enhance the effectiveness of educational interferences. This study predicts student engagement at an early phase of a Virtual Learning Environment (VLE) course by analyzing data collected from consecutive years. The prediction model is developed using machine learning techniques applied to a subset of Open University Learning Analytics Dataset, provided by Open University (OU), Britain. The investigated data belongs to 7,775 students who attended social science courses for consecutive assessment years. The experiments are conducted with a reduced feature set to predict whether the students are highly or lowly engaged in the courses. The attributes indicating students' interaction with the VLE, their scores, and final results are the most contributing variables for the predictive analysis. Based on these variables, a reduced feature vector is constructed. The baseline used in the study is the linear regression model. The model's best results showed 95% accurate, 95% precise, and 98% relevant results with the Random Forest classification algorithm. Early prediction's relevant features are a subset of click activities, which provided a functional interface between the students and the VLE.