The differential impact of interaction, environment and learner cognitive factors on online learning outcomes: Insights from a European transition country

This article explores the multifaceted role played by interaction, cognitive, and environmental factors on various online learning outcomes in the context of Albania, a European transition country. More specifically, it scrutinises the differentiated impact of interaction (with content, peers and instructor), ease of use, attitudes, technical, and time constraints on three online learning outcomes, namely satisfaction, perceived learning, and quality of courses delivered online, aiming to achieve a nuanced understanding that can inform practitioners’ decision-making. Drawing from a sample of 1698 graduate and undergraduate University students, hierarchical regression was employed to test the proposed models. Our findings for the pooled sample corroborate previous research results on the pivotal role of interaction in determining learning outcomes, albeit with some differences. While student-instructor and student-content interaction positively affect satisfaction, perceived learning and quality across models, the hypothesised effect of student-student interaction on satisfaction is not supported. Further, one of the cognitive factors analysed - ease of use - has no significant impact on learning outcomes. In contrast, attitude toward online learning is a major determinant affecting all outcomes. Environmental factors, such as technical constraints, negatively affect all three outcomes, while time constraints lower students’ satisfaction. Finally, our results show differences among bachelor and master students. Interaction with the instructor is a critical determinant for all learning outcomes in the case of bachelor students, while interaction with peers is key to perceived learning for master students. This study contributes to the research stream focused on the differentiated impact on learning outcomes by introducing two environmental factors in the existing models. The implications for higher education administrators and policymakers are further discussed.