The evaluation of academic staff activity remains a challenging task for higher education institutions because current assessment procedures are often fragmented, labor-intensive, and dependent on manually verified data. In practice, this leads to inconsistent scoring, limited transparency, and delayed managerial decisions. This paper addresses the problem of building a reliable and repeatable rating assessment mechanism for academic staff within a virtual educational environment. The proposed solution combines academic, methodological, organizational, and research-oriented indicators and supports the use of scientometric data to improve the objectivity of assessment. The developed module enables automated collection, verification, aggregation, and reporting of rating data, which reduces manual workload and improves the comparability of results across staff members and structural units. The obtained results demonstrate that the proposed approach increases the transparency and consistency of academic performance evaluation, while reducing manual data processing efforts. In addition, the system enables multi-level analytical assessment, supporting decision-making at the departmental, faculty, and institutional levels. This contributes to improved governance of academic processes and provides a scalable framework for data-driven management in higher education institutions.