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        <title>Improving Embeddings Representations for Comparing Higher Education Curricula: A Use Case in Computing</title>
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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.</abstract>
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%0 Conference Proceedings
%T Improving Embeddings Representations for Comparing Higher Education Curricula: A Use Case in Computing
%A Murrugarra-Llerena, Jeffri
%A Alva-Manchego, Fernando
%A Murrugarra-LLerena, Nils
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F murrugarra-llerena-etal-2022-improving
%X 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.
%R 10.18653/v1/2022.emnlp-main.776
%U https://aclanthology.org/2022.emnlp-main.776
%U https://doi.org/10.18653/v1/2022.emnlp-main.776
%P 11299-11307

Markdown (Informal)

[Improving Embeddings Representations for Comparing Higher Education Curricula: A Use Case in Computing](https://aclanthology.org/2022.emnlp-main.776) (Murrugarra-Llerena et al., EMNLP 2022)

• Improving Embeddings Representations for Comparing Higher Education Curricula: A Use Case in Computing (Murrugarra-Llerena et al., EMNLP 2022)

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.