A framework for digital media literacies for teaching and learning in higher education

Across a broad range of subjects in higher education institutions, students are required to complete assessment tasks that involve the production of digital artefacts. Examples include podcasts, digital stories, animations, video and blended media. To produce effective digital artefacts, one must be digitally literate. This requires a certain set of technical, audio-visual, behavioural, critical and social skills. In this article, the authors propose a framework that can be used to develop digital media literacies and train students in digital media creation. The framework considers three interdependent domains: conceptual, functional and audio-visual. A series of examples will be provided to illustrate the importance and interdependent nature of these domains. Implications of the framework on student training are discussed

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