Division strategy of learning time formulation in blended learning based on prior knowledge on the ability to apply and analyze statistics courses

Yunia Mulyani Azis () and Dede Ropik Yunus ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 9, 465-474

Abstract: 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.

Keywords: Blended learning; DSLT; Prior knowledge. (search for similar items in EconPapers)
Date: 2025
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