Data Driven Teaching and Real Time Decision Making in Education Management

In the wake of advancements in data-driven education, it is increasingly debated whether real-time decision-making in educational management can achieve improved student learning outcomes and institutional efficiency through the integration of data analytics tools and pedagogical strategies in the same manner as traditional data-informed policies did. Based on these facts, the aim of this paper is to analyze, evaluate, and rank the impact of data-driven teaching methods on education management effectiveness and its potential impact in the field of academic administration and policy development. The authors construct a hierarchical analytical model using the Analytic Hierarchy Process (AHP) and regression analysis estimation for describing the relationship between the Data-Driven Teaching Index and organizational, technological, and pedagogical factors. In our study, we identified key determinants related to student engagement, policy frameworks, organizational culture, and human capital development. Evidence suggests that technological infrastructure is likely to affect student performance, but it is questionable whether the adoption of subsequent institutional decision-making movements (e.g. towards fully automated learning systems) will equally benefit from existing educational frameworks. The paper finishes with strategic recommendations and policy implications for educational institutions and policymakers. Data analytics integration as well as technological advancements change the rules of educational governance and the requirements of teaching methodologies, administrative leadership, and digital literacy development of educators and students. Future studies can empirically test the proposed linkages to reveal institutional, technological, and policy-specific interdependencies between processes of data-driven teaching adoption and real-time decision-making efficiency.