short-paper

Using learning analytics to explore help-seeking learner profiles in MOOCs

Authors:

Paula G. de Barba,

Aneesha BakhariaAuthors Info & Claims

LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference

Pages 424 - 428

https://doi.org/10.1145/3027385.3027448

Published: 13 March 2017 Publication History

Abstract

In online learning environments, learners are often required to be more autonomous in their approach to learning. In scaled online learning environments, like Massive Open Online Courses (MOOCs), there are differences in the ability of learners to access teachers and peers to get help with their study than in more traditional educational environments. This exploratory study examines the help-seeking behaviour of learners across several MOOCs with different audiences and designs. Learning analytics techniques (e.g., dimension reduction with t-sne and clustering with affinity propagation) were applied to identify clusters and determine profiles of learners on the basis of their help-seeking behaviours. Five help-seeking learner profiles were identified which provide an insight into how learners' help-seeking behaviour relates to performance. The development of a more in-depth understanding of how learners seek help in large online learning environments is important to inform the way support for learners can be incorporated into the design and facilitation of online courses delivered at scale.

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https://dl.acm.org/doi/10.1145/3636555.3636856
Salman AAlexandron G(2024)The Digital Fingerprint of Learner Behavior: Empirical Evidence for Individuality in Learning Using Deep LearningComputers and Education: Artificial Intelligence10.1016/j.caeai.2024.100322(100322)Online publication date: Oct-2024
https://doi.org/10.1016/j.caeai.2024.100322
Yang B(2024)Combining Danmaku and Discussion Boards: Toward A Scalable and Sociable Environment for Mass Collaboration in MOOCsInternational Journal of Computer-Supported Collaborative Learning10.1007/s11412-024-09426-319:3(311-339)Online publication date: 5-Jun-2024
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Index Terms

Using learning analytics to explore help-seeking learner profiles in MOOCs
1. Applied computing
  1. Education
    1. E-learning

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Published In

cover image ACM Other conferences

LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference

March 2017

631 pages

ISBN:9781450348706

DOI:10.1145/3027385

General Chairs:
Alyssa Wise
New York University, USA
,
Phillip H. Winne
Simon Fraser University, Canada
,
Grace Lynch
Society for Learning Analytics Research
,
Program Chairs:
Xavier Ochoa
Escuela Superior Politecnica del Litoral, Ecuador
,
Inge Molenaar
Radboud University, The Netherlands
,
Shane Dawson
University of South Australia, Australia
,
Publications Chair:
Marek Hatala
Simon Fraser University, Canada

Copyright © 2017 ACM.

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 the author(s) 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

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Published: 13 March 2017

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LAK '17

LAK '17: 7th International Learning Analytics and Knowledge Conference

March 13 - 17, 2017

British Columbia, Vancouver, Canada

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LAK '17 Paper Acceptance Rate 36 of 114 submissions, 32%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

Yan LMartinez-Maldonado RGasevic D(2024)Generative Artificial Intelligence in Learning Analytics: Contextualising Opportunities and Challenges through the Learning Analytics CycleProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636856(101-111)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636856
Salman AAlexandron G(2024)The Digital Fingerprint of Learner Behavior: Empirical Evidence for Individuality in Learning Using Deep LearningComputers and Education: Artificial Intelligence10.1016/j.caeai.2024.100322(100322)Online publication date: Oct-2024
https://doi.org/10.1016/j.caeai.2024.100322
Yang B(2024)Combining Danmaku and Discussion Boards: Toward A Scalable and Sociable Environment for Mass Collaboration in MOOCsInternational Journal of Computer-Supported Collaborative Learning10.1007/s11412-024-09426-319:3(311-339)Online publication date: 5-Jun-2024
https://doi.org/10.1007/s11412-024-09426-3
Tseng YLin YYadav GBier NAleven V(2024)Curio: Enhancing STEM Online Video Learning Experience Through Integrated, Just-in-Time Help-SeekingTechnology Enhanced Learning for Inclusive and Equitable Quality Education10.1007/978-3-031-72315-5_30(437-451)Online publication date: 13-Sep-2024
https://doi.org/10.1007/978-3-031-72315-5_30
Cloude EBaker RFouh EHilliger IKhosravi HRienties BDawson S(2023)Online help-seeking occurring in multiple computer-mediated conversations affects grades in an introductory programming courseLAK23: 13th International Learning Analytics and Knowledge Conference10.1145/3576050.3576106(378-387)Online publication date: 13-Mar-2023
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Anghel ELittenberg-Tobias Jvon Davier M(2023)What I wanted and what I didComputers & Education10.1016/j.compedu.2023.104929207:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.compedu.2023.104929
Önder AAkçapınar G(2023)Investigating the effect of prompts on learners’ academic help-seeking behaviours on the basis of learning analyticsEducation and Information Technologies10.1007/s10639-023-11872-928:12(16909-16934)Online publication date: 20-May-2023
https://doi.org/10.1007/s10639-023-11872-9
Liao XLuo HXiao YMa LLi JZhu M(2022)Learning Patterns in STEAM Education: A Comparison of Three Learner ProfilesEducation Sciences10.3390/educsci1209061412:9(614)Online publication date: 12-Sep-2022
https://doi.org/10.3390/educsci12090614
Li CXing WLeite WWise AMartinez-Maldonado RHilliger I(2022)Do Gender and Race Matter? Supporting Help-Seeking with Fair Peer Recommenders in an Online Algebra Learning PlatformLAK22: 12th International Learning Analytics and Knowledge Conference10.1145/3506860.3506869(432-437)Online publication date: 21-Mar-2022
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Yang BHe HKizilcec RDavis KOchoa X(2022)Learning Alone Yet Together: Enhancing Between-Learner Social Connectivity at ScaleProceedings of the Ninth ACM Conference on Learning @ Scale10.1145/3491140.3528294(374-378)Online publication date: 1-Jun-2022
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