Improving embeddings representations for comparing higher education curricula: A use case in computing

Murrugarra-Llerena, Jeffri, Alva Manchego, Fernando ORCID: https://orcid.org/0000-0001-6218-8377 and Murrugarra-LLerena, Nils 2022. Improving embeddings representations for comparing higher education curricula: A use case in computing. Presented at: 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 7-11 December 2022. Published in: Goldberg, Yoav, Kozareva, Zornitsa and Zhang, Yue eds. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 11299–11307. 10.18653/v1/2022.emnlp-main.776

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Abstract

We propose an approach for comparing curricula of study programs in higher education. Pre-trained word embeddings are fine-tuned in a study program classification task, where each curriculum is represented by the names and content of its courses. By combining metric learning with a novel course-guided attention mechanism, our method obtains more accurate curriculum representations than strong baselines. Experiments on a new dataset with curricula of computing programs demonstrate the intuitive power of our approach via attention weights, topic modeling, and embeddings visualizations. We also present a use case comparing computing curricula from USA and Latin America to showcase the capabilities of our improved embeddings representations.

Item Type:	Conference or Workshop Item - published (Paper)
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	Association for Computational Linguistics
Last Modified:	28 May 2026 08:41
URI:	https://orca.cardiff.ac.uk/id/eprint/161898

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