UniSkill: A Dataset for Matching University Curricula to Professional Competencies

TL;DR

This paper introduces UniSkill, a new dataset linking university courses to professional skills, and demonstrates that language models can effectively match courses with skills, aiding skill extraction and recommendation systems.

Contribution

The paper provides the first publicly available dataset for course-skill matching and establishes baseline performance using BERT models for this task.

Findings

BERT achieves 87% F1-score on course-skill matching.

The dataset includes manually annotated and synthetic data based on ESCO taxonomy.

Course and skill matching is a feasible and effective task.

Abstract

Skill extraction and recommendation systems have been studied from recruiter, applicant, and education perspectives. While AI applications in job advertisements have received broad attention, deficiencies in the instructed skills side remain a challenge. In this work, we address the scarcity of publicly available datasets by releasing both manually annotated and synthetic datasets of skills from the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy and university course pairs and publishing corresponding annotation guidelines. Specifically, we match graduate-level university courses with skills from the Systems Analysts and Management and Organization Analyst ESCO occupation groups at two granularities: course title with a skill, and course sentence with a skill. We train language models on this dataset to serve as a baseline for retrieval and recommendation systems for course-to-skill and skill-to-course matching. We evaluate the models on a portion of the annotated data. Our BERT model achieves 87% F1-score, showing that course and skill matching is a feasible task.