Predicting Learning Outcomes in Distance Learning Universities: Perspectives from an Integrated Model

The progressive spread of online academic courses is a result of the flexible and customisable nature of the related learning process, while some studies on students’ achievement in distance learning universities have underlined retention as a priority issue for future research. Despite the number of studies that have investigated specific variables related to online learning, there are no systemic reference models that consider specific online environmental variables, IT competence and outcomes together. This paper offers an integrated model to test the contribution of different variables in predicting student performance in online academic courses, building on the literature on the digital learning environment and achievement. The model, based on the initial Biggs’ 3P learning model, aims to evaluate technical competency and the ability to self-manage as personal variables; furthermore, it proposes the analysis of a set of perceptions related to course design. Through the proposed model, a student’s background, personal variables, perception of the physical learning environment and perception of the course design can be utilized as predictors of student performance. Future research should investigate the applicability of the model in academic distance learning contexts

Modeling change in learning strategies throughout higher education : a multi-indicator latent growth perspective

The change in learning strategies during higher education is an important topic of research in the Student Approaches to Learning field. Although the studies on this topic are increasingly longitudinal, analyses have continued to rely primarily on traditional statistical methods. The present research is innovative in the way it uses a multi-indicator latent growth analysis in order to more accurately estimate the general and differential development in learning strategy scales. Moreover, the predictive strength of the latent growth models are estimated. The sample consists of one cohort of Flemish University College students, 245 of whom participated in the three measurement waves by filling out the processing and regulation strategies scales of the Inventory of Learning Styles – Short Versions. Independent-samples t-tests revealed that the longitudinal group is a non-random subset of students starting University College. For each scale, a multi-indicator latent growth model is estimated using Mplus 6.1. Results suggest that, on average, during higher education, students persisting in their studies in a non-delayed manner seem to shift towards high-quality learning and away from undirected and surface-oriented learning. Moreover, students from the longitudinal group are found to vary in their initial levels, while, unexpectedly, not in their change over time. Although the growth models fit the data well, significant residual variances in the latent factors remain