Tetik, Ş.K.Toprak, E.Kumova Metin, S.Uymaz, H.A.2025-12-302025-12-30202597898975867129789897585333978989758614997898975871609789897587696https://doi.org/10.5220/0013682800004000https://hdl.handle.net/20.500.14365/8475Institute for Systems and Technologies of Information, Control and Communication (INSTICC)This study proposes a framework to support undergraduate students in course selection by identifying implicit prerequisites and predicting performance in elective courses. Unlike traditional prerequisite rules that rely solely on curriculum design, our approach integrates students’ academic history and course-level semantic information. We define two core tasks: (T1) identifying practical prerequisites that significantly impact success in a target course, and (T2) predicting student success in elective courses based on academic profiles. For T1, we analyze prior course performance and learning outcomes using SHAP (SHapley Additive exPlanations) to determine the most influential courses. For T2, we build student representations using course descriptions and learning outcomes, then apply embedding models (Sentence-BERT, Doc2Vec, Universal Sentence Encoder) combined with classification algorithms to predict course success. Experiments demonstrate that embedding-based models, especially those using Sentence-BERT, can effectively predict course outcomes. The results suggest that incorporating semantic representations enhances curriculum design, course advisement, and prerequisite refinement. © © 2025 by SCITEPRESS – Science and Technology Publications, Lda.eninfo:eu-repo/semantics/openAccessCourse PrerequisitesEmbedding ModelsMachine LearningSHAPSoftware Engineering EducationConference Object10.5220/00136828000040002-s2.0-105022513970