Background
In recent years, the education sector has witnessed a significant shift towards online and blended learning, further accelerated by global events like the COVID-19 pandemic. Understanding what influences learners' preferences for these modes is vital for educational institutions adapting to changing pedagogical needs. This study focuses on exploring the characteristics that shape medical students' choices between online-only and blended learning.
Objectives
This research aims to investigate learner traits impacting their selection of learning environments, specifically emphasizing the preference for online only or blended learning.
Methods
We conducted a cross-sectional survey among medical students at the end of the 2020/2021 academic year's first semester. The survey covered demographics, learning environment preferences, the Scale of University Students' Readiness for E-Learning, and the Motivation and Learning Strategies Scale. We employed statistical analyses, including t-tests, Eta coefficient tests (η) for correlation, and Eta squared tests (η²) for effect size.
Results
Out of 665 respondents, the majority (63.2 %) favored blended learning. Interestingly, students preferring online-only learning scored significantly higher in e-learning readiness, motivation, and cognitive learning strategies. A strong correlation and substantial effect size linked the preferred environment to e-learning readiness, especially motivation for e-learning (η = 0.834, η² = 0.695), and overall motivation for learning. However, the influence of cognitive learning strategies showed weaker correlation and a smaller effect size.
Discussion
This study highlights the pivotal role of e-learning readiness, particularly motivation for e-learning and general motivation for learning, in shaping students' preferences for learning environments. In contrast, cognitive learning strategies exert less impact on these preferences. To prepare students for evolving educational landscapes, prioritizing e-learning skills and motivation enhancement is recommended, especially given the potential for future disruptions to traditional learning methods..