A Research Framework for Understanding Education-Occupation Alignment with NLP Techniques

Renzhe Yu, Subhro Das, Sairam Gurajada, Kush Varshney, Hari Raghavan, Carlos Lastra-Anadon


Abstract
Understanding the gaps between job requirements and university curricula is crucial for improving student success and institutional effectiveness in higher education. In this context, natural language processing (NLP) can be leveraged to generate granular insights into where the gaps are and how they change. This paper proposes a three-dimensional research framework that combines NLP techniques with economic and educational research to quantify the alignment between course syllabi and job postings. We elaborate on key technical details of the framework and further discuss its potential positive impacts on practice, including unveiling the inequalities in and long-term consequences of education-occupation alignment to inform policymakers, and fostering information systems to support students, institutions and employers in the school-to-work pipeline.
Anthology ID:
2021.nlp4posimpact-1.11
Volume:
Proceedings of the 1st Workshop on NLP for Positive Impact
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | NLP4PosImpact
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
100–106
Language:
URL:
https://aclanthology.org/2021.nlp4posimpact-1.11
DOI:
10.18653/v1/2021.nlp4posimpact-1.11
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.nlp4posimpact-1.11.pdf