Modeling student success in online learning environments

Online learning has shown constant growth in higher education over the past two decades. However, its success has been marginal, given low levels of graduation, variable academic achievement, and student dissatisfaction. These issues have resulted in a continually growing gap between students enrolling and those who successfully complete online learning courses. Research suggests that potential risks to student success can be detected and prevented through the analysis of interactions within online learning environments, and it has identified a large number of factors that influences success; however, it has been unable to provide a clear path to understand the process that leads to student success. One possible reason for this gap in the field is the need to develop a theoretically supported and coherent framework to systematize interaction of influencing factors, which would enable combined analysis of the complex interactions affecting student success in online learning environments. The present study creates and tests a theoretical model of student success in online learning environments. This work draws on known influencing factors of online learning and presents a model, underpinned by distance education theories, that organizes relationships and complex interactions within online learning. A case study methodology is used to test the model using existing data from a distance-learning university. Academic records and online learning data are combined with student’s perception questionnaire data to create a more holistic view of online learning. The sample consisted of 2862 participants from 416 online courses in a distance-learning university. The records included 79 factors of student success. Structural Equation Modelling (SEM) is used to analyze data and test the model. Findings suggest that the model is valid within the analyzed sample, so that student success in online learning environments can be understood as the outcome of a complex process of interaction between Student, Teaching and Context-related factors, which, simultaneously, are produced by the interaction of sub-factors. Key implications for practice relate to design of online learning and student support structures. Advances in research include how factors of student success are identified in specific contexts and a validated framework for studying online student success.