12 research outputs found
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 studentsâ 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 Biggsâ 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 studentâs 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