Development of a maturity model for blended education: A delphi study

In order to embed blended learning environments in a strategic and sustainable manner, a multi-actor, multidimensional approach is necessary. This paper reports the results of a 3-round Delphi study involving 28 experts which focuses on the refinement and validation of a layered maturity model that assesses key aspects of blended practices in higher education. The study examines the wording of the proposed assumptions, dimensions and indicators, whether they bear validity and if there are others that are not accounted for. We present the findings of each round, the confirmed maturity model and a series of recommendations for its future usage. As such, it is helpful for lecturers, program coordinators, support services or institutional leaders to decide upon follow-up actions and to achieve up-scaled blended programs and courses in higher education institutions

Development of a maturity model for blended education: A delphi study

In order to embed blended learning environments in a strategic and sustainable manner, a multi-actor, multidimensional approach is necessary. This paper reports the results of a 3-round Delphi study involving 28 experts which focuses on the refinement and validation of a layered maturity model that assesses key aspects of blended practices in higher education. The study examines the wording of the proposed assumptions, dimensions and indicators, whether they bear validity and if there are others that are not accounted for. We present the findings of each round, the confirmed maturity model and a series of recommendations for its future usage. As such, it is helpful for lecturers, program coordinators, support services or institutional leaders to decide upon follow-up actions and to achieve up-scaled blended programs and courses in higher education institutions.Teaching & Learning ServicesExtension Schoo

Explainable Learning Analytics: assessing the stability of student success prediction models by means of explainable AI

Beyond managing student dropout, higher education stakeholders need decision support to consistently influence the student learning process to keep students motivated, engaged, and successful. At the course level, the combination of predictive analytics and self-regulation theory can help instructors determine the best study advice and allow learners to better self-regulate and determine how they want to learn. The best performing techniques are often black-box models that favor performance over interpretability and are heavily influenced by course contexts. In this study, we argue that explainable AI has the potential not only to uncover the reasons behind model decisions, but also to reveal their stability across contexts, effectively bridging the gap between predictive and explanatory learning analytics (LA). In contributing to decision support systems research, this study (1) leverages traditional techniques, such as concept drift and performance drift, to investigate the stability of student success prediction models over time; (2) uses Shapley Additive explanations in a novel way to explore the stability of extracted feature importance rankings generated for these models; (3) generates new insights that emerge from stable features across cohorts, enabling teachers to determine study advice. We believe this study makes a strong contribution to education research at large and expands the field of LA by augmenting the interpretability and explainability of prediction algorithms and ensuring their applicability in changing contexts.</p