Applying Predictive Analytics in Elective Course Recommender System While Preserving Student Course Preferences

Abstract

In higher education scenarios, elective courses sought to provide a deeper insight of the trending advancements in the field of specialization for undergraduate students. Choice of elective subjects during the pre-final or final year of the undergraduates play a crucial role as they help in shaping their career or area of specialization for future research. However, there exist numerous gaps and concerns that arise due to mismatch of the elective course pre-requisites and the student’s possessed skills-set which result in degraded student academic performance as well as quality of education. This research study focuses on filling in these gaps by efficiently predicting the marks in different elective subjects for the current cohort of students, beforehand, as well as side by side preserving their explicit subject preferences.