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Interpretive Case-Based Modelling: Getting Beyod the Tipping Point in Unravelling the Complexity of Innovation Adoption in Higher Education Teaching Practice

Mrs Irena White, Professor Lindsey Conner

Flinders University

Getting beyond the tipping point in innovation adoption has remained an elusive challenge for educators for several decades. The quest for mainstreaming innovations with a proven capacity for transforming teaching, learning and critical inquiry, is particularly challenging when innovations originate bottom-up in higher education teaching practice. This is in contrast to management-directed top-down implementations, such as, Learning Management Systems and MOOCs which, it has been argued, are stifling the development and diffusion of bottom-up teaching innovations. Further challenges emerge from traditional relationships embedded within academic silos in increasingly complex education systems that are faced with constant financial pressures and changing student expectations. Case studies and survey methods in research studies have generated lists of critical success factors and identified institutional actors that play a key role in both development and mainstreaming of innovations in teaching practice within the context of this complex environment. Interpretive case-based modelling offers a new method that connects the factors and actors to unravel the complexity of innovation adoption in higher education teaching practice.

The conceptual framework for this new method addresses a gap in conducting educational research which has a long tradition of applying qualitative and quantitative methods for investigating and analysing the implementation of new practices in education systems. While these traditional methods have led to valuable conclusions from research studies, they have not captured the dynamic, unpredictable and non- linear complexities that are characteristics of wicked problems found in educational systems and practices. In addressing this gap, interpretive case-based modelling weaves together primary and secondary case study data by utilising an agent-based modelling computer simulation within an interpretive research design to re-imagine and extend prior methods used in research. The new method is applied in-situ during interviews to populate models with real cases and explore ideal scenarios in real-time. The new method was developed and successfully applied during a PhD study of 15 case studies of e-learning innovation adoption in Australian and New Zealand universities.

The findings suggest there is strong potential for applying interpretive case-based modelling across other fields of educational research and extending the interview techniques in this method to focus groups.