Development Of Statistics Instruction Based On Authentic Data To Improve Students’ Statistical Reasoning And Data Literacy

Abstract

This study aims to develop and evaluate the effectiveness of an authentic data-based statistics learning model in improving students’ statistical reasoning and data literacy. A quasi-experimental research design with a one-group pretest-posttest approach was used, involving a number of students from relevant degree programs. The research instruments included tests of statistical reasoning and data literacy—which had been validated for validity and reliability—as well as observations and focus group discussions. The results of the paired t-test analysis showed a significant increase in both variables following the implementation of the learning model. The mean statistical reasoning score increased from 65.7 (pretest) to 85.2 (posttest) with p < 0.001 and Cohen’s d of 1.25 (large effect). Meanwhile, data literacy scores increased from 62.3 (pre-test) to 82.5 (post-test), with p < 0.001 and Cohen’s d of 1.18 (large effect). Further qualitative analysis confirmed that the experience of working with authentic—and often complex—data fostered students’ intrinsic motivation and critical thinking skills, making the material more relevant and engaging. This study expands the framework of authentic learning by positioning direct interaction with real data as a key mechanism for internalizing statistical concepts and developing 21st-century competencies. Pedagogical implications suggest a shift in the statistics curriculum toward hands-on experiences with authentic data and the reinforcement of data literacy as an essential competency.