Author:

Keywords:

learning analytics dashboards, open learner models, Science & Technology, Social Sciences, Technology, Computer Science, Theory & Methods, Education, Scientific Disciplines, Computer Science, Education & Educational Research, Learning analytics dashboards, open student models, literature review, COGNITIVE TUTORS, INDICATORS, KNOWLEDGE

Abstract:

This paper aims to link Learning Analytics Dashboards (LADs) to the corpus of research on Open Learner Models (OLMs), as both have similar goals. We conducted a systematic review of work on OLMs and compare this with LADs for learners in terms of (i) data use and modelling, (ii) key publication venues, (iii) authors and articles, (iv) key themes, and (v) system evaluation. We highlight the similarities and differences between the research on LADs and OLMs. Our key contribution is a bridge between these two areas as a foundation for building upon the strengths of each. We report the following key results from the review: in reports of new OLMs, almost 60% are based on a single type of data; 30-40% use behavioural metrics, support input from the user, or have complex models; and just 6% involve multiple applications. Key associated themes include intelligent tutoring systems, learning analytics, and self-regulated learning. Notably, compared with LADs, OLM research is more likely to be interactive (81% of papers compared with 31% for LADs), report evaluations (76% versus 59%), use assessment data (100% versus 37%), provide a comparison standard for students (52% versus 38%), but less likely to use behavioural metrics (33% against 75% for LADs). In OLM work, there was a heightened focus on learner control and access to their own data.