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

Murrugarra-Llerena, Jeffri, Alva Manchego, Fernando 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, 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

Full text not available from this repository.

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 (Paper)
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Association for Computational Linguistics
Last Modified:	03 Oct 2023 14:45
URI:	https://orca.cardiff.ac.uk/id/eprint/161898

Actions (repository staff only)

Edit Item