• Robert Bodily
  • Judy Kay
  • Vincent Aleven
  • Ioana Jivet
  • Dan Davis
  • Franceska Xhakaj
  • Katrien Verbert

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.

Original languageEnglish
Title of host publicationLAK'18 Proceedings of the 8th International Conference on Learning Analytics and Knowledge
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Print)978-1-4503-6400-3
Publication statusPublished - 2018
EventLAK 2018 : The 8th International Conference on Learning Analytics and Knowledge - Sydney, Australia
Duration: 7 Mar 20189 Mar 2018


ConferenceLAK 2018

    Research areas

  • Learning analytics dashboards, Literature review, Open learner models, Open student models

ID: 47159639