In this paper, we present an extended evaluation of a course recommender system designed to support students who struggle in the first semesters of their studies and are at risk of dropping out. The system, which was developed in earlier work using a student-centered design, is based on the explainable k-nearest neighbor algorithm and recommends a…

This study investigated the use of automated essay scoring (AES) to identify at-risk students enrolled in a first-year university writing course. An application of AES, the "Criterion"[R] Online Writing Evaluation Service was evaluated through a methodology focusing on construct modelling, response processes, disaggregation, extrapolation,…

Earlier studies have suggested that higher education institutions could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk students and allow for more timely pedagogical interventions. This paper confirms and extends this proposition by providing data from an international…