@inproceedings{yu-etal-2021-research,
title = "A Research Framework for Understanding Education-Occupation Alignment with {NLP} Techniques",
author = "Yu, Renzhe and
Das, Subhro and
Gurajada, Sairam and
Varshney, Kush and
Raghavan, Hari and
Lastra-Anadon, Carlos",
editor = "Field, Anjalie and
Prabhumoye, Shrimai and
Sap, Maarten and
Jin, Zhijing and
Zhao, Jieyu and
Brockett, Chris",
booktitle = "Proceedings of the 1st Workshop on NLP for Positive Impact",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4posimpact-1.11",
doi = "10.18653/v1/2021.nlp4posimpact-1.11",
pages = "100--106",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Research Framework for Understanding Education-Occupation Alignment with NLP Techniques
%A Yu, Renzhe
%A Das, Subhro
%A Gurajada, Sairam
%A Varshney, Kush
%A Raghavan, Hari
%A Lastra-Anadon, Carlos
%Y Field, Anjalie
%Y Prabhumoye, Shrimai
%Y Sap, Maarten
%Y Jin, Zhijing
%Y Zhao, Jieyu
%Y Brockett, Chris
%S Proceedings of the 1st Workshop on NLP for Positive Impact
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F yu-etal-2021-research
%X 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.
%R 10.18653/v1/2021.nlp4posimpact-1.11
%U https://aclanthology.org/2021.nlp4posimpact-1.11
%U https://doi.org/10.18653/v1/2021.nlp4posimpact-1.11
%P 100-106
Markdown (Informal)
[A Research Framework for Understanding Education-Occupation Alignment with NLP Techniques](https://aclanthology.org/2021.nlp4posimpact-1.11) (Yu et al., NLP4PI 2021)
ACL