DATA-DRIVEN PEDAGOGICAL INNOVATION: ANALYSIS OF EMERGING STRATEGIES IN HYBRID EDUCATIONAL ENVIRONMENTS

Abstract

The expansion of hybrid educational environments after the pandemic has intensified the availability of learning data, which can guide more accurate pedagogical decisions. This article presents a theoretical-documentary review of recent literature (2021–2025) on data-driven pedagogical innovation in hybrid and blended contexts. Empirical studies and systematic reviews on learning analytics, educational data mining, adaptive platforms and artificial intelligence are analyzed, as well as works focused on the ethics of data use and data literacy among teachers. The results are organized into four axes of emerging strategies: (1) use of learning analytics for formative feedback and early warning, (2) personalization through adaptive platforms and AI systems, (3) hybrid instructional redesign supported by data and (4) ethical data governance and development of teacher data literacy. Evidence indicates that the pedagogical integration of learning analytics and AI improves monitoring capacity, personalization, and student engagement, as long as it is accompanied by robust ethical frameworks and specific teacher training. It is concluded that data-based pedagogical innovation in hybrid environments requires articulating, in a balanced way, technological infrastructure, student-centered pedagogical models, and institutional policies that guarantee the protection and responsible use of data.