Division strategy of learning time formulation in blended learning based on prior knowledge on the ability to apply and analyze statistics courses - Azis, Yunia Mulyani; Yunus, Dede Ropik (Edelweiss Applied Science and Technology, 2025)
This study investigates the impact of various division strategies of learning time (DSLT) in a blended learning environment on students’ ability to apply and analyze statistical concepts, considering their prior knowledge (PK) levels. A quasi-experimental 3 × 3 factorial design was employed, involving 125 students grouped based on high, medium, and low prior knowledge. Each group received one of three DSLT treatments, which combined online learning and face-to-face instruction in ratios of 40:60, 60:40, and 70:30. Data were collected via pre-tests and post-tests measuring application and analysis competencies in linear regression. Results from two-way MANOVA revealed significant main and interaction effects between DSLT and PK. The DSLT with a ratio of 70:30 was most effective for students with high PK, while the 60:40 ratio worked best for students with medium PK. Both 40:60 and 60:40 strategies showed similar effectiveness for students with low PK. The findings suggest that aligning instructional time distribution with students’ prior knowledge enhances learning outcomes in statistical education.