Open learner models and learning analytics dashboards

TLDR

Learning Analytics Dashboards and Open Learner Models share similar goals, and this paper seeks to connect the two research areas. The study aims to bridge LADs and OLMs, providing a foundation to leverage each area’s strengths. The authors performed a systematic review of OLM literature and compared it with a prior LAD review across data use, modelling, venues, authors, themes, and evaluation. The review revealed that most OLMs rely on a single data type (≈60 %), use behavioral metrics (33 %), allow user input (39 %), employ complex models (37 %), and rarely involve multiple applications (6 %); OLM research is more interactive, better evaluated, and more often uses assessment data than LADs, while focusing more on learner control and data access.

Abstract

This paper aims to link student facing 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 literature on OLMs and compared the results with a previously conducted review of 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; 33% use behavioral metrics; 39% support input from the user; 37% 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 behavioral metrics, or resource use data (33% against 75% for LADs). In OLM work, there was a heightened focus on learner control and access to their own data.