The Impact of Technology-Driven Adaptive Learning on Mathematical Conceptual Mastery in Primary Education

Abstract

This study examines the impact of technology-driven adaptive learning on elementary students’ mastery of mathematical concepts. Using a quantitative quasi-experimental design, sixth-grade students were assigned to experimental and control groups. The experimental group received four weeks of instruction via Quipper School Premium, an AI-based adaptive platform, while the control group engaged in traditional learning. A 25-item conceptual understanding test was administered, and data were analyzed through validity, reliability, normality, and homogeneity tests, followed by an Independent Samples t-test. Results revealed a significant improvement in the experimental group’s post-test scores (t = 6.85; p < 0.001), indicating that adaptive learning enhances conceptual mastery through personalized pacing and real-time feedback. Implications include targeted teacher training in adaptive analytics, integration of adaptive modules into the Merdeka Curriculum, equitable access to devices, and secure data governance. Findings highlight the potential of AI-powered personalized learning to strengthen foundational mathematics, particularly in developing country contexts.

Keywords: adaptive strategy, educational technology, concept understanding, basic mathematics