Improving Embeddings Representations for Comparing Higher Education Curricula: A Use Case in Computing

Jeffri Murrugarra-Llerena, Fernando Alva-Manchego, Nils Murrugarra-LLerena


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.
Anthology ID:
2022.emnlp-main.776
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11299–11307
Language:
URL:
https://aclanthology.org/2022.emnlp-main.776
DOI:
10.18653/v1/2022.emnlp-main.776
Bibkey:
Cite (ACL):
Jeffri Murrugarra-Llerena, Fernando Alva-Manchego, and Nils Murrugarra-LLerena. 2022. Improving Embeddings Representations for Comparing Higher Education Curricula: A Use Case in Computing. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11299–11307, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Improving Embeddings Representations for Comparing Higher Education Curricula: A Use Case in Computing (Murrugarra-Llerena et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/remove-xml-comments/2022.emnlp-main.776.pdf