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Prediction of course completion based on participants' social engagement on a social-constructive MOOC platform

Prediction of course completion based on participants' social engagement on a social-constructive MOOC platform
Prediction of course completion based on participants' social engagement on a social-constructive MOOC platform
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. In 2016, over 58 million people join MOOCs.

Far fewer people actually participate in MOOCs than originally sign up and then there is a steady attrition as courses progress. The observation of high attrition has prompted concerns among MOOC providers to mitigate their high attrition rates.

Recent studies have been able to correlate social engagement of learners to course completion. 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 social-constructivist MOOC platform, the impact of engagement on course completion, and to predict learners' course completion.

The findings of this research show that a commonly known social feature, follow, which is integrated into the Futurelearn MOOC platform has potential value in allowing tracking and analysing the behaviours of participants. The patterns of learners social engagement were modelled and a completion prediction model was developed. This model was successful at predicting those who might complete the course at a high or low success rate.

The contributions of this research are that the behaviour chains could be the basis of a personalised recommender system, and the completion model based on social behaviour could contribute to wider prediction model based on a wider range of factors.
University of Southampton
Sunar, Ayse Saliha
8a121335-66ed-4a7a-93b2-eb8194b89868
Sunar, Ayse Saliha
8a121335-66ed-4a7a-93b2-eb8194b89868
White, Susan
5f9a277b-df62-4079-ae97-b9c35264c146

Sunar, Ayse Saliha (2017) Prediction of course completion based on participants' social engagement on a social-constructive MOOC platform. University of Southampton, Doctoral Thesis, 144pp.

Record type: Thesis (Doctoral)

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. In 2016, over 58 million people join MOOCs.

Far fewer people actually participate in MOOCs than originally sign up and then there is a steady attrition as courses progress. The observation of high attrition has prompted concerns among MOOC providers to mitigate their high attrition rates.

Recent studies have been able to correlate social engagement of learners to course completion. 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 social-constructivist MOOC platform, the impact of engagement on course completion, and to predict learners' course completion.

The findings of this research show that a commonly known social feature, follow, which is integrated into the Futurelearn MOOC platform has potential value in allowing tracking and analysing the behaviours of participants. The patterns of learners social engagement were modelled and a completion prediction model was developed. This model was successful at predicting those who might complete the course at a high or low success rate.

The contributions of this research are that the behaviour chains could be the basis of a personalised recommender system, and the completion model based on social behaviour could contribute to wider prediction model based on a wider range of factors.

Text
Final Thesis - Version of Record
Available under License University of Southampton Thesis Licence.
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Published date: September 2017

Identifiers

Local EPrints ID: 419583
URI: http://eprints.soton.ac.uk/id/eprint/419583
PURE UUID: 0c063317-c034-4552-834e-1b6a6112f622
ORCID for Susan White: ORCID iD orcid.org/0000-0001-9588-5275

Catalogue record

Date deposited: 13 Apr 2018 16:30
Last modified: 30 Jan 2020 01:30

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Contributors

Author: Ayse Saliha Sunar
Thesis advisor: Susan White ORCID iD

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