A Research Framework for Understanding Education-Occupation Alignment with NLP Techniques (2021)

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.

## introduction one important role of higher education is to prepare students for the workforce, but not all college graduates benefit equally from their degrees: more than 40% of recent college graduates are either unemployed or work in jobs not requiring a degree @xcite . on the other side of the equation, 45% employers worldwide report having difficulty "finding the right skills or talent" @xcite . are there significant gaps between what higher education offers and what employers look for? what are the sources of these gaps? addressing these questions can bring substantial policy implications and positive societal impacts, such as mitigating inequalities in labor market outcomes across student groups and major areas. given that college education is delivered predominantly through structured coursework @xcite , we assume that curricular content and its correspondence with employers' demand may be an important driver of differences in student outcomes in the labor market. nonetheless, there has been little consensus on the definition of this correspondence and the understanding of how it contributes to the observed gaps @xcite . one challenge behind this void is the lack of data that can capture the dynamics of labor market demands and the details of curricular content on a large scale. with the recent availability of digitized records of course content and job requirements as well as advances in computational methods, granular and scalable analysis of the correspondence between the two becomes possible @xcite . in this paper, we present a novel research framework to measure the alignment between curricular content and labor market demands. we leverage neural network-based language models and other nlp techniques to learn representations of relevant documents. based on these representations and theoretical insights, we incorporate three lenses through which to measure the alignment: skill overlap (economic), instructional design features (educational), and semantic text similarity (technical). this framework represents the first comprehensive and scalable approach for connecting the content of education and workforce, which was either treated as a black box or investigated on a small scale in prior research @xcite . moreover, the computational capacity of this framework can empower system-wide policy research as well as local practices regarding curricular alignment and workforce preparation, thereby bringing positive societal impacts. for example, university stakeholders can track the downstream consequences of ill-aligned curricula especially for students from marginalized groups. in section 2, we briefly summarize prior research related to the technical and substantive aspects of our work. section 3 details our threedimensional framework that measures educationoccupation alignment. section 4 envisions the societal benefits of our framework through assisting downstream policy research and field practice of different stakeholders in the school-workforce pipeline. finally, we conclude with a summary and next steps in section 5. ## summary we propose a research framework for measuring the alignment between curricular content and job requirements by leveraging nlp techniques. based on neural representations of curricular and job documents, our framework includes three dimensions for quantifying education-occupation alignment: 1) amount of specialized knowledge and skills shared by the two types of documents, 2) quantity of instructional design features associated with general social and cognitive competencies, and 3) overall semantic text similarity between the two corpora. we discuss how the framework can help researchers answer education and economic policy questions, and empower stakeholders in practice to make more informed decisions around recruitment, course development, major/course choice, etc. we focus on sketching the high-level picture of the proposed framework through examples, while leaving plenty of space for technical details and future work by ourselves and others. given the importance of education-occupation alignment especially in the post-pandemic era, and the widely available yet underutilized corpora data of curricular and job content, we call for more cross-disciplinary collaborations on the topic to contribute to healthier education-occupation dynamics of the future.