Developing a Digital Learning Environment Based on Computational Thinking–Data-Driven Inquiry to Enhance Informatics Students’ Statistical Problem-Solving and Data Literacy

Abstract

This study developed and evaluated a digital learning environment integrating Computational Thinking and Data-Driven Inquiry for statistics learning among Informatics students. The study employed a Research and Development approach using the ADDIE model and a quasi-experimental nonequivalent pretest–posttest control group design. The participants comprised 52 fourth-semester Informatics students, with 25 students in the experimental group and 27 in the control group. Product feasibility was assessed by three experts, while effectiveness was examined through statistical problem-solving and data literacy tests. Expert validation yielded an overall feasibility score of 96.44%, indicating that the product was highly feasible in terms of content, instructional design, and technology. Descriptively, the experimental group showed higher posttest scores in statistical problem solving and data literacy. ANCOVA results indicated that the group differences were not statistically significant for statistical problem solving, F(1, 49) = 4.004, p = .051, or data literacy, F(1, 49) = 2.329, p = .133. These findings suggest that the developed learning environment is feasible and pedagogically promising, although further studies with larger samples and longer interventions are needed to establish its effectiveness.