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.
BibTeX
@inproceedings{murrugarra-llerena-etal-2022-improving,
title = "Improving Embeddings Representations for Comparing Higher Education Curricula: A Use Case in Computing",
author = "Murrugarra-Llerena, Jeffri and
Alva-Manchego, Fernando and
Murrugarra-LLerena, Nils",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.776/",
doi = "10.18653/v1/2022.emnlp-main.776",
pages = "11299--11307"
}