research-article

Should We Give Learners Control Over Item Difficulty?

Authors:

Jan Papoušek,

Radek PelánekAuthors Info & Claims

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

Pages 299 - 303

https://doi.org/10.1145/3099023.3099080

Published: 09 July 2017 Publication History

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Abstract

Personalized educational systems are able to provide learners questions of specified difficulty. Since learners differ, the appropriate level of difficulty may vary and it may be impossible to find an universal setting. We implemented a version of an adaptive educational system for geography practice that allows learners to adjust difficulty of questions. We evaluated this feature using a randomized control experiment. The overall results show only a small effect of the adjustment. A more detailed analysis, however, shows that for some groups of learners the effect can be important, although not necessarily advantageous. The collected data from the experiment provide insight into how to tune question difficulty automatically.

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Cited By

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Kandemir EVie JSanchez-Ayte APalombi ORamus F(2024)Adaptation of the Multi-Concept Multivariate Elo Rating System to Medical Students' Training DataProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636858(123-133)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636858
Knoop‐van Campen Cvan der Graaf JHorvers AKooi RDijkstra RMolenaar I(2024)Enacting control with student dashboards: The role of motivationJournal of Computer Assisted Learning10.1111/jcal.1293640:3(1137-1153)Online publication date: 14-Jan-2024
https://doi.org/10.1111/jcal.12936
Yan HLin FKinshuk (2024)An AI-Learner Shared Control Model Design for Adaptive PracticingGenerative Intelligence and Intelligent Tutoring Systems10.1007/978-3-031-63028-6_21(272-280)Online publication date: 1-Jun-2024
https://doi.org/10.1007/978-3-031-63028-6_21
Show More Cited By

Index Terms

Should We Give Learners Control Over Item Difficulty?
1. Applied computing
  1. Education
    1. Interactive learning environments

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Published In

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

July 2017

456 pages

ISBN:9781450350679

DOI:10.1145/3099023

Editors:
Marko Tkalcic
Free University of Bolzano
,
Dhaval Thakker
University of Leeds
,
Panagiotis Germanakos
SAP SE & University of Cyprus
,
Kalina Yacef
University of Sydney
,
Cecile Paris
CSIRO ICT Centre
,
Olga Santos
Spanish National University for Distance Education
,
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:
Michel Desmarais
Polytechnique University, Montreal, Canada
,
Federica Cena
University of Torino, Italy

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

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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Overall Acceptance Rate 162 of 633 submissions, 26%

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UMAP '25

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33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

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Cited By

View all

Kandemir EVie JSanchez-Ayte APalombi ORamus F(2024)Adaptation of the Multi-Concept Multivariate Elo Rating System to Medical Students' Training DataProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636858(123-133)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636858
Knoop‐van Campen Cvan der Graaf JHorvers AKooi RDijkstra RMolenaar I(2024)Enacting control with student dashboards: The role of motivationJournal of Computer Assisted Learning10.1111/jcal.1293640:3(1137-1153)Online publication date: 14-Jan-2024
https://doi.org/10.1111/jcal.12936
Yan HLin FKinshuk (2024)An AI-Learner Shared Control Model Design for Adaptive PracticingGenerative Intelligence and Intelligent Tutoring Systems10.1007/978-3-031-63028-6_21(272-280)Online publication date: 1-Jun-2024
https://doi.org/10.1007/978-3-031-63028-6_21
Kosch TWelsch RChuang LSchmidt A(2023)The Placebo Effect of Artificial Intelligence in Human–Computer InteractionACM Transactions on Computer-Human Interaction10.1145/352922529:6(1-32)Online publication date: 11-Jan-2023
https://dl.acm.org/doi/10.1145/3529225
Brusilovsky P(2023)AI in Education, Learner Control, and Human-AI CollaborationInternational Journal of Artificial Intelligence in Education10.1007/s40593-023-00356-z34:1(122-135)Online publication date: 21-Aug-2023
https://doi.org/10.1007/s40593-023-00356-z
Sayed WNoeman AAbdellatif AAbdelrazek MBadawy MHamed AEl-Tantawy S(2022)AI-based adaptive personalized content presentation and exercises navigation for an effective and engaging E-learning platformMultimedia Tools and Applications10.1007/s11042-022-13076-882:3(3303-3333)Online publication date: 29-Jun-2022
https://doi.org/10.1007/s11042-022-13076-8
Pelanek R(2020)A Classification Framework for Practice Exercises in Adaptive Learning SystemsIEEE Transactions on Learning Technologies10.1109/TLT.2020.302705013:4(734-747)Online publication date: 1-Oct-2020
https://doi.org/10.1109/TLT.2020.3027050

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