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research-article

Modelling MOOC learners' social behaviours

Authors:

Ayse Saliha Sunar,

Rabeeh Ayaz Abbasi,

Naif R. AljohaniAuthors Info & Claims

Volume 107, Issue C

https://doi.org/10.1016/j.chb.2018.12.013

Published: 01 June 2020 Publication History

Abstract

MOOCs offer world-widely accessible online content typically including videos, readings, quizzes along with social communication tools on a platform that enables participants to learn at their own pace. The number of learners who sign up and attend the courses are exponentially growing. Consequently, MOOC platforms generate a large amount of data about their learners. Researchers use participants' digital traces to make sense of their engagement in a course and identify their needs to predict future patterns and to make interventions based on these patterns. The research reported here was conducted to further understand learners social engagement on a MOOC platform and the impact of engagement on course completion. The patterns of learners social engagement were modelled by using learning analytics techniques. The findings of this research show that the integrated social features such as commonly known follow features and deeper peer interactions have potential value in tracking, analysing, and generating insightful information related to participants' behaviours.

Highlights

•

Uniquely integrated follow feature is valuable to understand learners' engagement.

•

The patterns of learners' social interactions can be modelled as behaviour chains.

•

Number of learners having behaviour chains by deep peer interactions is quite low.

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Modelling MOOC learners' social behaviours

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Published In

cover image Computers in Human Behavior

Computers in Human Behavior Volume 107, Issue C

Jun 2020

473 pages

ISSN:0747-5632

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 June 2020

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Cited By

Torres REntrada MKatigbak KPosadas M(2024)Customers'Behavioral Intention and Actual Use of Mobile Shopping Platforms: Understanding Predictors Through Mathematical ModellingProceedings of the 2024 8th International Conference on E-Commerce, E-Business, and E-Government10.1145/3675585.3675595(7-13)Online publication date: 28-May-2024
https://dl.acm.org/doi/10.1145/3675585.3675595
Liu ZTang QOuyang FLong TLiu S(2024)Profiling students’ learning engagement in MOOC discussions to identify learning achievementComputers & Education10.1016/j.compedu.2024.105109219:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.compedu.2024.105109
Chen JWu ZJia HZhang QLiu FMu JLin Z(2024)A Student Performance Prediction Model Based on Feature Factor TransferKnowledge Science, Engineering and Management10.1007/978-981-97-5495-3_29(384-394)Online publication date: 16-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-5495-3_29
Suriyapaiboonwattana KHone K(2023)Exploring the Factors Affecting Learning Satisfaction in MOOC: A Case Study of Higher Education in a Developing CountryLearning and Collaboration Technologies10.1007/978-3-031-34550-0_39(551-569)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-34550-0_39
Wu BWang Y(2022)Formation mechanism of popular courses on MOOC platformsComputers & Education10.1016/j.compedu.2022.104629191:COnline publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1016/j.compedu.2022.104629
Chen JFan FJia HXu YDong HHuang XLi JZhang Z(2022)Student Behavior Analysis and Performance Prediction Based on Blended Learning DataKnowledge Science, Engineering and Management10.1007/978-3-031-10986-7_48(597-609)Online publication date: 6-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-10986-7_48
Visvizi ADaniela LChen C(2020)Beyond the ICT- and sustainability hypesComputers in Human Behavior10.1016/j.chb.2020.106304107:COnline publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1016/j.chb.2020.106304

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