research-article

Can learner characteristics predict their behaviour on MOOCs?

Authors:

Alexandra I. Cristea,

Mohammad Alshehri,

Craig D. StewartAuthors Info & Claims

ICETC '18: Proceedings of the 10th International Conference on Education Technology and Computers

Pages 119 - 128

https://doi.org/10.1145/3290511.3290568

Published: 26 October 2018 Publication History

Abstract

Stereotyping is the first type of adaptation in education ever proposed. However, the early systems have never dealt with the numbers of learners that current MOOCs provide. Thus, the umbrella question that this work tackles is if learner characteristics can predict their overall, but also fine-grain behaviour. Earlier results point at differences related to gender or to age. We have also looked into more details into finer-grain analyzing the weekly behavior of females and males. Here, we further expand this, by showing how, depending on the way the comments are counted, significance can be found when comparing female and male commenting behavior, at the level of the week. Moreover, the topic of the course is an important factor in this behavior. These outcomes can help in informing future runs, in terms of potential personalised feedback for teachers and students.

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Cited By

Alshehri MAlfarsi FAlamri AAlrajhi LRizvi SPereira FIsotani SCristea A(2024)MOOCRev: A Large-Scale Data Repository for Course ReviewsArtificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky10.1007/978-3-031-64312-5_15(124-131)Online publication date: 2-Jul-2024
https://doi.org/10.1007/978-3-031-64312-5_15
Alshehri MCristea A(2022)MOOCs Paid Certification Prediction Using Students Discussion ForumsArtificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium10.1007/978-3-031-11647-6_111(542-545)Online publication date: 26-Jul-2022
https://doi.org/10.1007/978-3-031-11647-6_111
Alshehri MAlamri ACristea A(2022)Adopting Automatic Machine Learning for Temporal Prediction of Paid Certification in MOOCsArtificial Intelligence in Education10.1007/978-3-031-11644-5_73(717-723)Online publication date: 27-Jul-2022
https://doi.org/10.1007/978-3-031-11644-5_73
Show More Cited By

Index Terms

Can learner characteristics predict their behaviour on MOOCs?
1. Applied computing
  1. Education
    1. E-learning
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models
      2. User studies

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cover image ACM Other conferences

ICETC '18: Proceedings of the 10th International Conference on Education Technology and Computers

October 2018

391 pages

ISBN:9781450365178

DOI:10.1145/3290511

Conference Chairs:
Minoru Nakayama
STokyo Institute of Technology, Japan
,
Fang Lou
University of Hertfordshire, UK
,
Program Chairs:
Betsy J. Bannier
Lake Region State College
,
Harumi Watanabe
Tokai University, Japan

Copyright © 2018 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 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: 26 October 2018

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ICETC 2018

ICETC 2018: The 10th International Conference on Education Technology and Computers

October 26 - 28, 2018

Tokyo, Japan

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Cited By

Alshehri MAlfarsi FAlamri AAlrajhi LRizvi SPereira FIsotani SCristea A(2024)MOOCRev: A Large-Scale Data Repository for Course ReviewsArtificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky10.1007/978-3-031-64312-5_15(124-131)Online publication date: 2-Jul-2024
https://doi.org/10.1007/978-3-031-64312-5_15
Alshehri MCristea A(2022)MOOCs Paid Certification Prediction Using Students Discussion ForumsArtificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium10.1007/978-3-031-11647-6_111(542-545)Online publication date: 26-Jul-2022
https://doi.org/10.1007/978-3-031-11647-6_111
Alshehri MAlamri ACristea A(2022)Adopting Automatic Machine Learning for Temporal Prediction of Paid Certification in MOOCsArtificial Intelligence in Education10.1007/978-3-031-11644-5_73(717-723)Online publication date: 27-Jul-2022
https://doi.org/10.1007/978-3-031-11644-5_73
Alshehri MAlamri ACristea AStewart C(2021)Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCsInternational Journal of Artificial Intelligence in Education10.1007/s40593-021-00246-231:2(215-233)Online publication date: 23-Mar-2021
https://doi.org/10.1007/s40593-021-00246-2
Shi LYang BToda A(2020)Temporal Analysis in Massive Open Online Courses – Towards Identifying at-Risk Students Through Analyzing Demographical ChangesAdvances in Information Systems Development10.1007/978-3-030-49644-9_9(146-163)Online publication date: 1-Aug-2020
https://doi.org/10.1007/978-3-030-49644-9_9

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