Open learner models and learning analytics dashboards: a systematic review

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

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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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.
Original languageEnglish
Title of host publicationLAK '18
Subtitle of host publicationProceedings of the 8th International Conference on Learning Analytics and Knowledge
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages41-50
Number of pages10
ISBN (Print)9781450364003
DOIs
Publication statusPublished - 7 Mar 2018
EventInternational Conference on Learning Analytics and Knowledge - Sydney, Australia
Duration: 7 Mar 20189 Mar 2018
https://latte-analytics.sydney.edu.au/

Conference

ConferenceInternational Conference on Learning Analytics and Knowledge
CountryAustralia
CitySydney
Period7/03/189/03/18
Internet address

Fingerprint

Students
Intelligent systems

Keywords

  • Learning analytics dashboards
  • literature review
  • open learner models
  • open student models

Cite this

Bodily, R., Kay, J., Aleven, V., Jivet, I., Davis, D., Xhakaj, F., & Verbert, K. (2018). Open learner models and learning analytics dashboards: a systematic review. In LAK '18: Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 41-50). New York, NY: Association for Computing Machinery (ACM). https://doi.org/10.1145/3170358.3170409
Bodily, Robert ; Kay, Judy ; Aleven, Vincent ; Jivet, Ioana ; Davis, Dan ; Xhakaj, Franceska ; Verbert, Katrien. / Open learner models and learning analytics dashboards : a systematic review. LAK '18: Proceedings of the 8th International Conference on Learning Analytics and Knowledge . New York, NY : Association for Computing Machinery (ACM), 2018. pp. 41-50
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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.",
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Bodily, R, Kay, J, Aleven, V, Jivet, I, Davis, D, Xhakaj, F & Verbert, K 2018, Open learner models and learning analytics dashboards: a systematic review. in LAK '18: Proceedings of the 8th International Conference on Learning Analytics and Knowledge . Association for Computing Machinery (ACM), New York, NY, pp. 41-50, International Conference on Learning Analytics and Knowledge, Sydney, Australia, 7/03/18. https://doi.org/10.1145/3170358.3170409

Open learner models and learning analytics dashboards : a systematic review. / Bodily, Robert; Kay, Judy; Aleven, Vincent; Jivet, Ioana; Davis, Dan; Xhakaj, Franceska; Verbert, Katrien.

LAK '18: Proceedings of the 8th International Conference on Learning Analytics and Knowledge . New York, NY : Association for Computing Machinery (ACM), 2018. p. 41-50.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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Bodily R, Kay J, Aleven V, Jivet I, Davis D, Xhakaj F et al. Open learner models and learning analytics dashboards: a systematic review. In LAK '18: Proceedings of the 8th International Conference on Learning Analytics and Knowledge . New York, NY: Association for Computing Machinery (ACM). 2018. p. 41-50 https://doi.org/10.1145/3170358.3170409