Redefining Prerequisites Through Text Embeddings: Identifying Practical Course Dependencies

Şükrü Kaan Tetik, Emirhan Toprak, Senem Kumova Metin, Hande Aka Uymaz

2025

Abstract

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.

Paper Citation

in Harvard Style

Tetik Ş., Toprak E., Kumova Metin S. and Aka Uymaz H. (2025). Redefining Prerequisites Through Text Embeddings: Identifying Practical Course Dependencies. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN , SciTePress, pages 49-59. DOI: 10.5220/0013682800004000

in Bibtex Style

@conference{kdir25,
author={Şükrü Kaan Tetik and Emirhan Toprak and Senem Kumova Metin and Hande Aka Uymaz},
title={Redefining Prerequisites Through Text Embeddings: Identifying Practical Course Dependencies},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2025},
pages={49-59},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013682800004000},
isbn={},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Redefining Prerequisites Through Text Embeddings: Identifying Practical Course Dependencies
SN -
AU - Tetik Ş.
AU - Toprak E.
AU - Kumova Metin S.
AU - Aka Uymaz H.
PY - 2025
SP - 49
EP - 59
DO - 10.5220/0013682800004000
PB - SciTePress