Data Extraction Matrix of 71 Studies on Unsupervised Learning and Educational Trajectories: A Systematic Literature Review Copiar título

This dataset contains a structured extraction matrix of 71 scientific studies included in a systematic literature review on the application of unsupervised machine learning techniques to educational data and student trajectory analysis. The matrix comprises 36 fields covering bibliographic information, educational contexts, data sources, temporal representations, preprocessing procedures, unsupervised learning algorithms, validation methods, identified patterns, and educational implications. The studies were selected following a PRISMA 2020-based review process, including searches in Scopus, Web of Science, IEEE Xplore, and SpringerLink, complemented by additional literature searches. This dataset aims to support methodological comparison, reproducibility, and future research on educational trajectories, student progression, academic performance, and dropout prevention through educational data analytics.