Data-Based Assessment for Improving Learning Quality

Abstract

This study aims to analyze the implementation of data-driven assessments to improve learning quality, identify factors influencing their effectiveness, and evaluate their impact on pedagogical practices and student academic achievement. The study used a mixed methods approach involving 450 teachers from 75 schools. The findings indicate that integrating learning analytics into pedagogical practices improves student academic achievement by 35% and teachers' learning differentiation by 68%. Data literacy and school leadership support are significant predictors of implementation success (β = 0.43 and β = 0.38, p < 0.001). Data-driven formative feedback was shown to be 3.5 times more specific and reduced feedback time from 4.2 days to 0.3 days. Personalizing learning through adaptive systems increased students' intrinsic motivation by 42%. Challenges include technological infrastructure gaps (45%), limited teacher data literacy (52%), and privacy concerns (38%). The study emphasizes the importance of continuous professional development and a robust governance framework for sustainable educational transformation