THE DETERMINANTS OF ADULT STUDENT PERSISTENCE IN ONLINE COURSES IN HIGHER EDUCATION: THE UTAUT PERSPECTIVE

Over the past two decades, the number of online courses has grown considerably in higher education in North America. This growth is mainly due to the participation of adult students to this delivery mode. Indeed, among students who register in online courses, nearly 92% are adult students (Yoo and Huang, 2013). The growth of online courses is due to several other factors. First they meet the demands of adult students for flexible course schedules. Also, they give better access to higher education; these adult students would not attend face-to-face courses, because of family and/or work responsibilities, not to mention their distance from higher education institutions. Finally, they significantly decrease their educational costs. Indeed, these adult students would not need to travel any more to attend face-to-face sessions while benefiting from direct or indirect contact with the instructor and with other students (Wang & Hsu, 2008). That said, several studies reported that persistence rates in online courses are very low. They range between 30% and 50% according to Kranzow (2013). By persistence, we refer to completing a course despite adverse obstacles or circumstances. Thus, with the exponential increase of the number of online courses in higher education, persistence in these courses is of great concern.
The determinants of student persistence in online courses in higher education have been studied by some authors and have been defined in some models. However, none of them has put a special emphasis on technological determinants. And yet, technology is very important in implementing and delivering instruction in online courses. For this purpose, this paper focuses on the technological determinants of adult student persistence in online courses in higher education. The model used to identify these determinants is the one of Venkatesh et al. (2003) known as the Unified Theory of Acceptance and Use of Technology (UTAUT) model, enriched with other determinants from other models. The UTAUT model was selected because it is designed exclusively with regard to the use of technology and it incorporates a wide variety of constructs originating from the main theoretical models of technology acceptance.
Participants in this study were adult students enrolled in several online courses in a Master of education program at a faculty of education in Canada, offering higher education in French and English. A total of 204 students responded to an online questionnaire on a voluntary basis. This questionnaire was made of 67 items and required 20 minutes to be completed. It was put online during the final three weeks of the winter semester where the study took place.
The structural model was tested using Smart-PLS software. The structural model was also examined according to the number of working hours and credits accumulated in the program. The results indicated different patterns of strength and significant relationships between groups and with the overall model, suggesting that working hours and credits accumulated played a moderating role. The coefficient of determination R2 (a number that indicates how well data fit the statistical model) ranged from 37% to 61%, which means that the enriched UTAUT model was able to explain up to 61% of the variability of persistence in online courses. The discussion focused on the most important factors to consider by administrators and faculties in higher education when they come to implement online courses.