The engagement of university teachers with predictive learning analytics

Predictive learning analytics (PLA) is an educational innovation that has the potential to enhance the teaching practice and facilitate student learning and success. Yet, the degree of PLA adoption across educational institutions remains limited, while teachers who make use of PLA do not engage with it in a systematic manner. Informed by the Unified Theory of Acceptance and Use of Technology (UTAUT), we conducted eleven in-depth interviews with university teachers and examined their engagement patterns with PLA for the duration of a 37-week undergraduate course. We aimed to identify (a) factors that explain the degree of using PLA in the teaching practice and (b) the impact of an intervention - sending email reminders to teachers - on facilitating systematic engagement with PLA. Findings suggested that, amongst the factors facilitating engagement with PLA were performance expectancy, effort expectancy, and social influence. Amongst the factors inhibiting engagement with PLA were performance expectancy and facilitated conditions that were related to training and a lack of understanding of predictive data. Implications for the adoption and use of PLA in higher education are discussed.