Learning Analytics, Probabilistic Graphical Models, Bayesian Networks, Probability, Metacognition, Self-Reflection, Personalised Feedback, Data Literacy, Ethics
Given appropriate tools and starter data, learners can gather data themselves to build personalised feedback models. Learners are often seen as passive objects of learning analytics (LA) initiatives and the opaque, black-box nature of many of LA systems reinforces this tendency. This research explores ways to involve students as active participants in shaping LA to their needs and encouraging them to be critical, data literate users of their own learning data. We have developed proof-of-concept, interactive, white-box probabilistic graphical models which students can interact with visually, and which are designed to give a sense of agency and an interconnected understanding of their learning activities. We are using qualitative methods to try to learn what other nodes would be impactful for students’ understanding and that would prompt and support metacognitive and self-reflection activities. These models are being developed further, tested and validated. Finally in future phases, we will conduct quantitative and qualitative analyses of their impact on student learning and learners.