Towards the Use of teacher-chosen features to predict students’ performance

Educational Data Mining regroups education and computer science. It offers many benefits to the education sector. One of the principal tasks of it is predicting students’ outcomes and despite several studies exploring the subject using different algorithms, different optimization tools, and different features. The teachers’ perspective concerning the features they found essential to detect underperforming students is neglected. Therefore, this paper's aim is to profit from teachers’ expertise in dealing with students and noticing students with difficulties in the course to predict students’ performance. Five machine learning algorithms are applied and compared to the features the teachers found important in the first experiment, and in a second one, they will be applied to all the dataset features. This study will use the Open University Learning Analytics Dataset.