research-article

Comparing Peer Recommendation Strategies in a MOOC

Authors:

Fran�ois Bouchet,

Hugues Labarthe,

R�mi BacheletAuthors Info & Claims

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

Pages 129 - 134

https://doi.org/10.1145/3099023.3099036

Published: 09 July 2017 Publication History

Abstract

Lack of social relationship has been shown to be an important contribution factor for attrition in Massive Open Online Courses (MOOCs). Helping students to connect with other students is therefore a promising solution to alleviate this phenomenon. Following up on our previous research showing that embedding a peer recommender in a MOOC had a positive impact on students' engagement in the MOOC, we compare in this paper the impact of three different peer recommenders: one based on socio-demographic criteria, one based on current progress made in the MOOC, and the last one providing random recommendations. We report our results and analysis (N = 2025 students), suggesting that the socio-demographic-based recommender had a slightly better impact than the random one.

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Cited By

Shou ZChen YWen HLiu JMo JZhang H(2024)Research on Joint Recommendation Algorithm for Knowledge Concepts and Learning Partners Based on Improved Multi-Gate Mixture-of-ExpertsElectronics10.3390/electronics1307127213:7(1272)Online publication date: 29-Mar-2024
https://doi.org/10.3390/electronics13071272
Shou ZShi ZWen HLiu JZhang H(2023)Learning Peer Recommendation Based on Weighted Heterogeneous Information Networks on Online Learning PlatformsElectronics10.3390/electronics1209205112:9(2051)Online publication date: 29-Apr-2023
https://doi.org/10.3390/electronics12092051
Libreros JMayas CHirth M(2023)Recommender Systems in Continuing Professional Education for Public Transport: Challenges of a Human-Centered DesignAdjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3596995(331-336)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1145/3563359.3596995
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Index Terms

Comparing Peer Recommendation Strategies in a MOOC
1. Applied computing
  1. Education
    1. E-learning
2. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social recommendation
    2. Empirical studies in collaborative and social computing
  2. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies

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Published In

cover image ACM Conferences

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

Copyright � 2017 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

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Published: 09 July 2017

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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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33rd ACM Conference on User Modeling, Adaptation and Personalization

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Cited By

Shou ZChen YWen HLiu JMo JZhang H(2024)Research on Joint Recommendation Algorithm for Knowledge Concepts and Learning Partners Based on Improved Multi-Gate Mixture-of-ExpertsElectronics10.3390/electronics1307127213:7(1272)Online publication date: 29-Mar-2024
https://doi.org/10.3390/electronics13071272
Shou ZShi ZWen HLiu JZhang H(2023)Learning Peer Recommendation Based on Weighted Heterogeneous Information Networks on Online Learning PlatformsElectronics10.3390/electronics1209205112:9(2051)Online publication date: 29-Apr-2023
https://doi.org/10.3390/electronics12092051
Libreros JMayas CHirth M(2023)Recommender Systems in Continuing Professional Education for Public Transport: Challenges of a Human-Centered DesignAdjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3596995(331-336)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1145/3563359.3596995
Miladi FLemire DPsyché V(2023)Learning Engagement and Peer Learning in MOOC: A Selective Systematic ReviewAugmented Intelligence and Intelligent Tutoring Systems10.1007/978-3-031-32883-1_29(324-332)Online publication date: 22-May-2023
https://doi.org/10.1007/978-3-031-32883-1_29
Uddin IImran AMuhammad KFayyaz NSajjad M(2021)A Systematic Mapping Review on MOOC Recommender SystemsIEEE Access10.1109/ACCESS.2021.31010399(118379-118405)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3101039
Shukla DChowdary C(2021)Peer recommendation using negative relevance feedbackSādhanā10.1007/s12046-021-01763-546:4Online publication date: 23-Nov-2021
https://doi.org/10.1007/s12046-021-01763-5
Garg MGoel A(2021)A Data-Driven Approach for Peer Recommendation to Reduce Dropouts in MOOCAdvances in Computing and Network Communications10.1007/978-981-33-6977-1_18(217-229)Online publication date: 21-Apr-2021
https://doi.org/10.1007/978-981-33-6977-1_18
Sun JGeng JCheng XZhu MXu QLiu Y(2020)Leveraging personality information to improve community recommendation in e‐learning platformsBritish Journal of Educational Technology10.1111/bjet.1301151:5(1711-1733)Online publication date: 6-Aug-2020
https://doi.org/10.1111/bjet.13011
Elghomary KBouzidi D(2019)Dynamic Peer Recommendation System based on Trust Model for sustainable social tutoring in MOOCs2019 1st International Conference on Smart Systems and Data Science (ICSSD)10.1109/ICSSD47982.2019.9003154(1-9)Online publication date: Oct-2019
https://doi.org/10.1109/ICSSD47982.2019.9003154
Huptych MHlosta MZdrahal ZKocvara J(2018)Investigating Influence of Demographic Factors on Study RecommendersArtificial Intelligence in Education10.1007/978-3-319-93846-2_27(150-154)Online publication date: 20-Jun-2018
https://doi.org/10.1007/978-3-319-93846-2_27
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