The Dashboard That Loved Me: Designing adaptive learning analytics for self-regulated learning

Learning dashboards are learning analytics (LA) tools built to make learners aware of their learning performance and behaviour and supporting self-reflection. However, most of the existing dashboards follow a “one size fits all” philosophy disregarding individual differences between learners, e.g., differences that stem from diverse cultural backgrounds, different motivations for learning or different levels of self-regulated learning (SRL) skills.

In this thesis, we challenge the assumption that impactful learning analytics should be limited to making learners aware of their learning, but rather should encourage and support learners in taking action and changing their learning behaviour. We thus take a learner-centred approach and explore what information learners choose to receive on learning analytics dashboards and how this choice relates to their learning motivation and their SRL skills. We also investigate how dashboard designs support learners in making sense of the displayed information and how learner goals and level of SRL skills influence what learners find relevant on such interfaces.

The large-scale experiments conducted with both higher education students and with MOOC learners bring empirical evidence as to how aligning the design of learning analytics dashboards with the learners’ intentions, learning motivation and the level of self-regulated learning skills influences the uptake and impact of such tools. The outcomes of these studies are synthesised in eleven recommendations for learning analytics dashboard design grouped according to the phase of the dashboard life-cycle to which they apply: (i) methodological aspects to be considered before designing dashboards, (ii) design requirements to be considered during the design phase and (iii) support offered to learners after the dashboard has been rolled out.