Student Performance Prediction Model Based on Course Description and Student Similarity

Choosing courses at the beginning of each semester is a complex decision that affects students’ future careers and academic performance, especially when given the freedom to choose. Among other factors, the expected grade at the end of the semester and/or the expected ability to successfully complete a course plays an important role in course selection. This paper introduces a prototype for predicting student performance using state-of-the-art natural language processing techniques. The prototype, designed to assist students in course selection, uses historical course enrollment data and current course descriptions to predict possible grades and warn students of possible negative performance. A large language model, BERT, was used to analyse text and create course description embeddings. For this purpose, descriptions of courses a student has attended were considered and formed the basis for the student knowledge profile. In addition, student performance profiles are created by examining grades from the historical enrolment data. This two-pronged analysis is used to identify patterns that lead to negative study results. Although the idea of creating knowledge profiles based on course descriptions is promising, the evaluation showed room for improvement in terms of accuracy and recall.