Complementing educational recommender systems with open learner models

Solmaz Abdi, Hassan Khosravi, Shazia Sadiq, Dragan Gasevic

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

5 Citations (Scopus)

Abstract

Educational recommender systems (ERSs) aim to adaptively recommend a broad range of personalised resources and activities to students that will most meet their learning needs. Commonly, ERSs operate as a "black box" and give students no insight into the rationale of their choice. Recent contributions from the learning analytics and educational data mining communities have emphasised the importance of transparent, understandable and open learner models (OLMs) that provide insight and enhance learners' understanding of interactions with learning environments. In this paper, we aim to investigate the impact of complementing ERSs with transparent and understandable OLMs that provide justification for their recommendations. We conduct a randomised control trial experiment using an ERS with two interfaces ("Non-Complemented Interface" and "Complemented Interface") to determine the effect of our approach on student engagement and their perception of the effectiveness of the ERS. Overall, our results suggest that complementing an ERS with an OLM can have a positive effect on student engagement and their perception about the effectiveness of the system despite potentially making the system harder to navigate. In some cases, complementing an ERS with an OLM has the negative consequence of decreasing engagement, understandability and sense of fairness.

Original languageEnglish
Title of host publicationLAK 2020 Conference Proceedings
EditorsVitomir Kovanović, Maren Scheffel, Niels Pinkwart, Katrien Verbert
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages360-365
Number of pages6
ISBN (Electronic)9781450377126
DOIs
Publication statusPublished - 2020
EventInternational Conference on Learning Analytics and Knowledge 2020 - Frankfurt, Germany
Duration: 23 Mar 202027 Mar 2020
Conference number: 10th
https://lak20.solaresearch.org

Conference

ConferenceInternational Conference on Learning Analytics and Knowledge 2020
Abbreviated titleLAK 2020
CountryGermany
CityFrankfurt
Period23/03/2027/03/20
Internet address

Keywords

  • Educational Recommender Systems
  • Open Learner Models
  • User models

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