ACADEMIC ANALYTICS AS A TOOL FOR MONITORING THE QUALITY OF PROFESSIONAL EDUCATION IN HIGHER EDUCATION INSTITUTIONS: INTERNATIONAL EXPERIENCE
Abstract
The article is devoted to the analysis of academic analytics as a tool for monitoring and improving the quality of vocational education in higher education institutions. The purpose of the article is to provide a theoretical and applied substantiation of academic analytics as a tool for monitoring the quality of vocational education in higher education institutions, taking into account international experience in its implementation and the adaptation of best practices to the national context. Methods. The study is based on the analysis of domestic and foreign scientific and methodological literature, a comparative analysis of academic analytics models in higher education institutions of the EU, the USA, and other countries, a systemic approach to the design of monitoring mechanisms, as well as the synthesis of results from empirical studies on the application of learning analytics and educational data mining in vocational training. Results. Key models and tools of academic analytics were analyzed, and typical indicators for assessing the quality of vocational training were identified, including learning outcomes, the formation of professional competencies, the effectiveness of practice-oriented components, and graduate employment indicators. The advantages of international approaches were revealed, such as data integration among stakeholders, the use of predictive models for early risk detection, transparent reporting systems, employer involvement in evaluation processes, and scalable solutions for inter-institutional cooperation. Typical challenges were also outlined, including personal data protection, ensuring system interoperability, and the need for human resources and digital literacy among academic staff. Conclusions. Academic analytics is an effective tool for monitoring the quality of vocational education, enabling the improvement of educational program effectiveness and the management of higher education institutions. It is recommended to develop inter-institutional data-sharing platforms, implement ethical standards for educational data processing (in compliance with ethical norms), ensure system interoperability, provide training for data analysis specialists, and strengthen partnerships with employers, which will enhance program adaptability and graduate competitiveness.
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