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COVID-19 pandemic triggered distance education in higher education. Decisions such as isolation, social distancing and quarantine made by countries unexpectedly and suddenly forced face-to-face education to change to distance education within days. All academics around the world had to move online overnight. All the educational and academic activities in higher education (courses, exams, meetings, etc.) had to be conducted online in a few days. Based on these changes, this study aimed to analyze the relationships among student, faculty (adaptations of faculty members to distance education) and institutional (distance learning capacities of the universities) variables that affected satisfaction of the students related to distance education in higher education institutions in Turkey during COVID-19 pandemic using hierarchical linear modeling (HLM). The study group included 14,962 students and 3631 academics from 30 universities. The results showed that universities with higher distance education capacities got higher satisfaction scores. HLM analysis showed that 43% of the variation in satisfaction scores resulted from universities. The second HLM analysis showed that 44% of the overall satisfaction score variance of the students could be explained by the factors of university features (Level 2: distance education capacity and acceptance and use of distance education systems of faculty members). Thus, it was determined that 44% of the university factor calculated as 43% in Model 1 (which is calculated within students’ general satisfaction scores) resulted from the distance education capacity and the acceptance and use of distance education systems of faculty members. The findings of this study provide insights to improve distance education by stakeholders of higher education institutions.

international journal of educational technology in higher education

Multi-level analyses of distance education capacity, faculty members’ adaptation, and indicators of student satisfaction in higher education during COVID-19 pandemic