[hal-04974355] A Recommender System of Postgraduate Courses Based on Soft Skills: A Psychometric-inspired Approach

Higher Education is constantly pushing to include soft skills in their curricula. An alternative could be to personalise their curricula so that students could better develop their soft skills. However, there are not many studies that investigate how to personalise the students’ curricula based on their soft skills, especially if we consider multiple soft skills that need development. The aim of the article is to propose a recommender system framework based on soft skills in order to bridge the soft skills gap between the expected proficiency by employers and the actual proficiency of graduates. The approach is illustrated using real data from three cohorts of students that graduated in the years 2021, 2022 and 2023 at a French Higher Education Institution. We use a psychometric modelling approach to predict the soft skills proficiency of students within a genetic algorithm framework. We define three fitness functions and two aggregation methods, with which we can quantify the relevance of a set of courses across 10 different soft skills (e.g., Problem Solving, Leadership). The results show the recommendations to have, on average, a higher fitness than the actual courses taken by the students during the program. Moreover, there is significant evidence that the recommendations would allow the students to satisfy more of the soft skill targets compared to the courses the students actually took.