Using the Behaviour Change Wheel to design and test a Learning Analytics adoption strategy at a regional Australian university

Using the Behaviour Change Wheel to design and test a Learning Analytics adoption strategy at a regional Australian university

Learning Analytics (LA) is an emerging and important field in Higher Education, concerned with using data about students and their learning environments to optimise students’ learning experiences. However, to date, many academic staff are not engaging with LA to inform and enhance their teaching practice and course design. This study applied the Behaviour Change Wheel (BCW) to design an implementation plan to enhance individual academics’ knowledge of LA, and their competence and confidence in the use of this knowledge, enabling them to understand and enhance students' learning experiences. A set of transferable design principles were developed based upon analysis of the LA implementation plan and its impacts, allowing for adaptation and adoption in broader contexts.

The BCW is an approach developed in the health disciplines, based on 19 frameworks of behaviour change. It is an aid for designing interventions that effect behavioural change, based on the capabilities, opportunities and motivations of the individuals involved. Professional learning and support for academics in the field of Learning Analytics is currently an under researched area. This study contributes significant insights into how academic behaviour change in the use of Learning Analytics can be effectively supported through professional learning.

The study employed a Design-Based Research (DBR) approach and was conducted in the context of a regional Australian university. Four consecutive phases were included in the study: an analysis of a practical problem (low levels of use of learning analytics by academic staff), the development of a solution to the problem (a BCW implementation plan for learning analytics adoption), the iterative trialling and evaluation of this design, and, finally, reflection, to produce design principles for an implementation plan that could be more widely adapted and adopted. Data collection methods included surveys, interview data, and logs of staff usage of the learning management system and associated learning analytics tools and reports.

Survey results were analysed using descriptive statistical techniques and usage data through simple counts and comparisons. Interview data were coded and analysed using deductive and inductive thematic analysis.

The study has resulted in transferable research outputs including a conceptual framework for adoption of LA, the I Framework, and development of a deep understanding of the barriers, enablers, and motivators for LA implementation. The resulting LA implementation plan was comprised of a mixture of individual consultations and group discussions with associated support resources for facilitators and participants that could be adapted and adopted at other institutions. Participants in this study self-reported that their involvement increased their awareness and use of LA; and commented that the benefits of involvement were the combination of individual support, opportunities to discuss with other staff interested in using learning analytics and the resources made available to them. Findings suggest that incorporating these elements into a long-term implementation plan is likely to result in increased uptake and staff capabilities in the use of learning analytics.