  In the context of higher education's evolving dynamics post-COVID-19, this
paper assesses the impact of new pedagogical incentives implemented in a
first-year undergraduate computing module at University College London. We
employ a mixed methods approach, combining learning analytics with qualitative
data, to evaluate the effectiveness of these incentives on increasing student
engagement.
  A longitudinal overview of resource interactions is mapped through Bayesian
network analysis of Moodle activity logs from 204 students. This analysis
identifies early resource engagement as a predictive indicator of continued
engagement while also suggesting that the new incentives disproportionately
benefit highly engaged students. Focus group discussions complement this
analysis, providing insights into student perceptions of the pedagogical
changes and the module design. These qualitative findings underscore the
challenge of sustaining engagement through the new incentives and highlight the
importance of communication in blended learning environments.
  Our paper introduces an interpretable and actionable model for student
engagement, which integrates objective, data-driven analysis with students'
perspectives. This model provides educators with a tool to evaluate and improve
instructional strategies. By demonstrating the effectiveness of our mixed
methods approach in capturing the intricacies of student behaviour in digital
learning environments, we underscore the model's potential to improve online
pedagogical practices across diverse educational settings.
