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
- 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)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2022.emnlp-main.776.pdf