Tracking K-12 and Higher Education Job Postings Through Web-Scraped Longitudinal Data

Teacher shortages and workforce trends in education are critical policy and research concerns. This study presents a robust data collection pipeline that systematically web-scrapes job postings for K-12 and higher education job postings across multiple sources. While the methodology could theoretically be adapted to other job categories, the pipeline is specifically implemented for educational job postings due to platform-specific structures and scraping constraints. Using R, we extract, clean, and archive job postings weekly, compiling them into a longitudinal master dataset that tracks trends in teacher openings over time. Our approach enables monthly trend analysis, providing insights into hiring patterns, subject-area demands, and geographic disparities. By making this dataset available, we contribute both a reproducible methodological pipeline for scraping, cleaning, and standardizing K-12 and higher education job postings, and a validated longitudinal dataset for research and workforce policy applications. This data descriptor details the methodology, data structure, and potential applications for researchers and policymakers monitoring education sector employment trends.