Discovering the Learning Gradient of Students’ Preferences for Learning Environment

Abstract

The aim of this study was to examine the effects of online learning self-regulation on learning outcomes during the COVID-19 pandemic lockdown among university college students. Quantitative k-means cluster analysis was used to examine the relationship among students in three different clusters based on their preferences toward online learning. The results indicated that online learning self-regulation had a significant positive effect on learning outcomes due to the shift to online learning. Thus, we identified a “learning gradient” among students, showing that cluster 1 students (preferences for 100% online) have the most positive preferences toward online teaching and the highest degree of self-regulation and learning outcome, cluster 2 students (moderate preferences for both physical and online teaching) are mixed (both positive and negative experiences) and moderate self-regulation and learning outcomes while cluster 3 students (preferences for physical classroom teaching) have the most negative preferences and the lowest self-regulation and learning outcome. The results from this study show that students’ self-regulated learning strategies during online teaching environments are important for their learning outcomes and that cluster 1 and 2 students especially profited from the more flexible online learning environment with organized and deep learning approaches. Cluster 3 students need more support from their educators to foster their self-regulation skills to enhance their learning outcomes in online teaching environments.

Keywords

• online teaching
• learning approaches
• self-regulation
• meta cognitive
• cluster analysis
• learning gradient