research-article

Fine-Grained Open Learner Models: Complexity Versus Support

Authors:

Julio Guerra-Hollstein,

Jordan Barria-Pineda,

Christian D. Schunn,

Peter BrusilovskyAuthors Info & Claims

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

Pages 41 - 49

https://doi.org/10.1145/3079628.3079682

Published: 09 July 2017 Publication History

Abstract

Open Learner Models (OLM) show the learner model to users to assist their self-regulated learning by, for example, helping prompt reflection, facilitating planning and supporting navigation. OLMs can show different levels of detail of the underlying learner model, and can also structure the information differently. As a result, a trade-off may exist between the potential for better support for learning and the complexity of the information shown. This paper investigates students' perceptions about whether offering more and richer information in an OLM will result in more effective support for their self-regulated learning. In a first study, questionnaire responses relating to designs for six visualisations of varying complexity led to the implementation of three variations on one of the designs. A second controlled study involved students interacting with these variations. The study revealed that the most useful variation for searching for suitable learning material was a visualisation combining a basic coloured grid, an extended bar chart-like visualisation indicating related concepts, and a learning gauge.

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Cited By

Hendrawan RBrusilovsky PLekshmi Narayanan ABarria-Pineda JBoratto LGena CMarras MGermanakos PPopescus E(2024)Explanations in Open User Models for Personalized Information ExplorationAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665188(256-263)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665188
You YTsai CLi YMa FHeron CGui X(2023)Beyond Self-diagnosis: How a Chatbot-based Symptom Checker Should RespondACM Transactions on Computer-Human Interaction10.1145/358995930:4(1-44)Online publication date: 31-Mar-2023
https://dl.acm.org/doi/10.1145/3589959
Ilić MMikić VKopanja LVesin B(2023)Intelligent techniques in e-learning: a literature reviewArtificial Intelligence Review10.1007/s10462-023-10508-156:12(14907-14953)Online publication date: 14-Jun-2023
https://doi.org/10.1007/s10462-023-10508-1
Show More Cited By

Index Terms

Fine-Grained Open Learner Models: Complexity Versus Support
1. Applied computing
  1. Education
    1. Interactive learning environments
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies
  2. Visualization

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Published In

cover image ACM Conferences

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

July 2017

420 pages

ISBN:9781450346351

DOI:10.1145/3079628

General Chairs:
Maria Bielikova
Slovak University of Technology in Bratislava, Slovakia
,
Eelco Herder
L3S Research Center, Germany and Radboud Universiteit Nijmegen, The Netherlands
,
Program Chairs:
Federica Cena
University of Torino, Italy
,
Michel Desmarais
Polytechnique University, Montreal, Canada

Copyright © 2017 ACM.

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Published: 09 July 2017

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UMAP '17

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UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization

July 9 - 12, 2017

Bratislava, Slovakia

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UMAP '17 Paper Acceptance Rate 29 of 80 submissions, 36%;

Overall Acceptance Rate 162 of 633 submissions, 26%

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33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

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Cited By

Hendrawan RBrusilovsky PLekshmi Narayanan ABarria-Pineda JBoratto LGena CMarras MGermanakos PPopescus E(2024)Explanations in Open User Models for Personalized Information ExplorationAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665188(256-263)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665188
You YTsai CLi YMa FHeron CGui X(2023)Beyond Self-diagnosis: How a Chatbot-based Symptom Checker Should RespondACM Transactions on Computer-Human Interaction10.1145/358995930:4(1-44)Online publication date: 31-Mar-2023
https://dl.acm.org/doi/10.1145/3589959
Ilić MMikić VKopanja LVesin B(2023)Intelligent techniques in e-learning: a literature reviewArtificial Intelligence Review10.1007/s10462-023-10508-156:12(14907-14953)Online publication date: 14-Jun-2023
https://doi.org/10.1007/s10462-023-10508-1
Akhuseyinoglu KHardt RBarria-Pineda JBrusilovsky PPollari-Malmi KSirkiä TMalmi LBecker BQuille KLaakso MBarendsen ESimon (2022)A Study of Worked Examples for SQL ProgrammingProceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 110.1145/3502718.3524813(82-88)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1145/3502718.3524813
Musto CNarducci FPolignano MDe Gemmis MLops PSemeraro G(2021) MyrrorBot: A Digital Assistant Based on Holistic User Models for Personalized Access to Online ServicesACM Transactions on Information Systems10.1145/344767939:4(1-34)Online publication date: 16-Aug-2021
https://dl.acm.org/doi/10.1145/3447679
Tsai CYou YGui XKou YCarroll JKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Exploring and Promoting Diagnostic Transparency and Explainability in Online Symptom CheckersProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445101(1-17)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445101
Marquès JCalvet LArguedas MDaradoumis TMor E(2021)Using a Notification, Recommendation and Monitoring System to Improve Interaction in an Automated Assessment Tool: An Analysis of Students’ PerceptionsInternational Journal of Human–Computer Interaction10.1080/10447318.2021.1938400(1-20)Online publication date: 17-Jun-2021
https://doi.org/10.1080/10447318.2021.1938400
Albó LBarria-Pineda JBrusilovsky PHernández-Leo D(2021)Knowledge-Based Design Analytics for Authoring Courses with Smart Learning ContentInternational Journal of Artificial Intelligence in Education10.1007/s40593-021-00253-332:1(4-27)Online publication date: 21-May-2021
https://doi.org/10.1007/s40593-021-00253-3
Kay J(2021)Scrutability, Control and Learner Models: Foundations for Learner-Centred Design in AIEDArtificial Intelligence in Education10.1007/978-3-030-78270-2_1(3-8)Online publication date: 12-Jun-2021
https://doi.org/10.1007/978-3-030-78270-2_1
Rahdari BBrusilovsky PBabichenko DSalter ABarber JGadiraju U(2020)Personalizing Information Exploration with an Open User ModelProceedings of the 31st ACM Conference on Hypertext and Social Media10.1145/3372923.3404797(167-176)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.1145/3372923.3404797
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