From data to personal user models for life‐long, life‐wide learners

As technology has become ubiquitous in learning contexts, there has been an explosion in the amount of learning data. This creates opportunities to draw on the decades of learner modelling research from Artificial Intelligence in Education and more recent research on Personal Informatics. We use these bodies of research to introduce a conceptual model for a Personal User Model for Life-long, Life-wide Learners (PUMLs). We use this to define a core set of system competency questions. A successful PUML and its interface must enable a learner to answer these by scrutinising their PUML, aided by its scaffolding interfaces. We aim to give learners both control over their own learning data and the means to harness that data for the important metacognitive processes of self-monitoring, reflection and planning. We conclude with a set of design guidelines for creating PUMLs. Our core contribution is a way to think about the design and evaluation of learning data and applications so that they give learner control and agency beyond simple data access and algorithmic transparency. Practitioner Notes What is already known about this topic There is decades of Artificial Intelligence in Education (AIED) research on learner modelling, personalisation and Open Learner Models (OLMs). There is a growing body of work on Personal Informatics. What this paper adds Drawing on the above research, we present a conceptual model showing how learning applications and data repositories relate to a Personal User Model for Life-long, Life-wide Learners (PUMLs). A set of competency questions to inform design and evaluation of PUMLs. Guidelines for designing interfaces that enable learners to scrutinise and control their learning data and models. Implications for practice and/or policy As universities create institutional repositories of learning data, our work takes a complementary, learner-centred perspective of learning data, applications and repositories. PUMLs offer a mechanism to support student's meta-cognitive processes. PUMLs go beyond simplistic views of data access and transparency of algorithmic processes-empowering learners to scrutinise their long-term data and its use.