Data analytics in inclusive education: indicators, metrics and evidence-based monitoring of educational quality
Abstract
Inclusive education has become one of the key priorities of modern educational systems, aiming to ensure equal learning opportunities for all students regardless of their abilities or learning needs. In such environments, effective monitoring and evaluation of the educational process require systematic data collection, measurable indicators, and analytical approaches. This study examines the role of data, indicators, metrics, and analytics in improving the effectiveness of inclusive education systems. The research explores how educational data can be transformed into meaningful analytical insights that support evidence-based decision-making in teaching and educational management. Using a conceptual analytical framework, the study identifies key metrics related to student participation, academic progress, and social adaptation in inclusive classrooms. In addition, a statistical model based on regression and structural equation modeling (SEM) is proposed to analyze the relationships between data indicators and educational outcomes. The results indicate that systematic use of data analytics enables educators to identify learning barriers, monitor individual development, and improve pedagogical strategies. Furthermore, the integration of indicators and metrics into inclusive education management contributes to transparency, accountability, and continuous improvement of educational quality. The findings suggest that data-driven approaches play a critical role in transforming inclusive education from a policy principle into an effective educational practice.
Keywords: inclusive education, educational data analytics, indicators, metrics, learning analytics, evidence-based education, educational monitoring
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