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     2026:7/1

Journal of Frontiers in Multidisciplinary Research

ISSN: 3050-9718 (Print) | 3050-9726 (Online) | Impact Factor: 8.10 | Open Access

Data-Driven Teaching: Using Student Progress Data to Personalize Learning in Special Education Classrooms

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Abstract

This study critically examines the transformative role of data-informed teaching practices in enhancing learning personalization within special education environments. It explores how the systematic application of student progress data can optimize pedagogical decisions, improve instructional precision, and support inclusive learning outcomes. Employing a conceptual review approach, the paper synthesizes empirical research, theoretical models, and global case studies to illuminate how educators can effectively integrate analytics, artificial intelligence, and technological tools into classroom practice. The methodology involved a structured analysis of peer-reviewed literature, focusing on the intersections of data-driven pedagogy, educational technology, and special needs instruction across diverse educational contexts.
Findings reveal that the strategic use of data enables teachers to identify learning gaps, predict student progress, and design adaptive interventions tailored to individual learning trajectories. However, the study also underscores significant barriers, including inadequate technological infrastructure, insufficient teacher data literacy, and ethical dilemmas related to privacy and algorithmic bias. The discussion highlights the necessity of developing comprehensive data governance frameworks that safeguard confidentiality while promoting equitable and transparent data use. Furthermore, the study affirms that sustainable implementation requires not only technological readiness but also institutional leadership, continuous professional development, and a culture of collaboration among educators and policymakers.
In conclusion, the research advocates for a holistic strategy that integrates ethical data practices, robust digital infrastructure, and capacity-building initiatives to realize the full potential of data-driven instruction in special education. By embracing data as both a diagnostic and transformative tool, educational systems can cultivate adaptive, evidence-based teaching environments that advance equity, accountability, and individualized learning excellence.
 

How to Cite This Article

Thomas Jerome Yeboah, Mforchive Abdoulaye Bobga, Kenneth Boakye, Chuks Sunday Ogbona (2022). Data-Driven Teaching: Using Student Progress Data to Personalize Learning in Special Education Classrooms . Journal of Frontiers in Multidisciplinary Research (JFMR), 3(2), 225-240. DOI: https://doi.org/10.54660/.JFMR.2022.3.2.225-240

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