This presentation demonstrates how an interactive computer simulation is being used to model the dynamic complexity of supporting mainstream adoption of elearning innovations in universities. These are innovations that originate in faculties and have proven to be effective in teaching and learning. Previous studies of technology adoption in universities relied on case studies and surveys to identify individual and institutional actors and causal factors in this dynamic and complex process. This is the first study to connect the main actors and factors to reveal critical relationships within university systems that enable and inhibit technology adoption in higher education teaching practice.
This study examines the adoption of digital technologies in higher education teaching practice, commonly known as elearning, and investigates what needs to change in universities to support the wider adoption of faculty-originated elearning innovations. These are innovations originated by education technology enthusiasts and visionaries in universities who apply new ways of using digital technologies in their own teaching practice. Yet, even when these elearning innovations are evaluated as beneficial in teaching and learning, very few gain wider adoption within the mainstream of university teaching.
How to achieve mainstream adoption of these innovations is widely acknowledged as a complex problem. Firstly it involves four university system stakeholders, (1) individual innovators and (2) adopters in higher education teaching roles and the institutional roles of (3) management and (4) central support services, who are the actors in this process. Secondly, a wide range of causal factors have been identified in case studies and surveys that enable and inhibit the sustainable diffusion of elearning innovations. Understanding the complex dynamic relationships between these university system actors and these causal factors is the focus for this doctoral study.
To investigate this dynamic complexity, this study uses a computer simulation to model the critical relationships between university actors, associated causal factors and levels of influence in the technology adoption process. In the computer modelling process, the first-hand experiences of different university actors are applied in interviews to connect and explore enabling and inhibiting factors and levels of influence across the four stakeholder groups. The resulting computer model provides a view of a whole university system that reveals the critical relationships between these stakeholders. The modelling process, demonstrated in this presentation, extends the findings from previous case studies and surveys to reveal and rethink the critical relationships between university system stakeholders in a complex and changing environment.