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

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.

Fernando Alva-Manchego

Lecturer

My research interests include text adaptation, evaluation of natural language generation, and NLP for education.