Research Summary: Development of a data mining methodology for an analytical system supporting students’ professional development

A study published in Frontiers in Education (International) proposes a data mining methodology and analytical system architecture aimed at supporting students' professional development. This initiative addresses the challenge faced by higher education institutions in adapting to the digital economy by leveraging Educational Data Mining (EDM), Learning Analytics (LA), and Large Language Models (LLMs) to analyze educational data and labor market demands. The system integrates various data sources, including academic records, digital portfolios, and employer requirements from platforms like HeadHunter and Enbek.

Why it matters

This development is strategically important as it addresses the growing gap between academic preparation and dynamic labor market needs, particularly in technical disciplines. By leveraging advanced data analytics, institutions can proactively guide individuals towards relevant skills and career paths, enhancing workforce readiness and economic competitiveness.

Key insights

• The digital economy's rapid evolution necessitates intellectual support for students' professional development.
• Higher education institutions face the challenge of analyzing educational data and labor market demands to guide students.
• A methodology for intelligent analysis of educational data and an analytical system architecture are proposed.
• The approach utilizes Educational Data Mining (EDM), Learning Analytics (LA), and Large Language Models (LLMs).
• Data integration includes educational programs, academic data, digital portfolios, and employer requirements from job platforms.

Source

Frontiers in Education — https://www.frontiersin.org/articles/10.3389/feduc.2026.1909777