A Data-Driven Approach to Improving Scientific Reasoning: The Development of the Data-Based Learning Cycle Model

This study aims to develop a Data-Based Learning Cycle (DaBeLCy) instructional model that is valid, practical, and effective in enhancing undergraduate students’ scientific reasoning in Natural Science (IPA) learning. The DaBeLCy model is an extension of the 5E Learning Cycle model, incorporating an explicit focus on data literacy through five instructional phases: Data Perception, Questioning and Hypothesis Generation, Data Generation and Explanation, Data Elaboration, and Reflection and Evaluation. The development process involved literature studies, needs analysis, model formulation, expert validation, and small- and large-scale trials. Validation results showed that the model and its supporting tools achieved content and construct validity scores above 3.7 with high reliability (α > .83), indicating that the model is suitable for implementation. The trials revealed high implementation fidelity (>83%) and active student engagement (73%–95%). Students’ scientific reasoning showed significant improvement, with N-Gain values in the moderate category (.56–.65) across different test classes. Student responses toward the model were highly positive, with agreement levels nearing 100%. The DaBeLCy model has proven to be valid, practical, and effective (feasible) for developing students’ scientific reasoning and is thus recommended as an innovative instructional model for science education at the university level.