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A case study on english as a second language speakers for sustainable MOOC study

A case study on english as a second language speakers for sustainable MOOC study
A case study on english as a second language speakers for sustainable MOOC study

Massive Open Online Courses (MOOCs) have a great potential for sustainable education. Millions of learners annually enrol on MOOCs designed to meet the needs of an increasingly diverse and international student population. Participants' backgrounds vary by factors including age, education, location, and first language. MOOC authors address consequent needs by ensuring courses are well-organised. Learning is structured into discrete steps, prioritising clear communication; video components incorporate subtitles. Variability in participants' language abilities inevitably create barriers to learning, a problem most extreme for those studying in a language which is not their first. This paper investigates how to identify ESL participants and how best to predict factors associated with their course completion. This study proposes a novel method for automatically categorising (English as Primary and Official Language; English as Official but not Primary Language; and English as a second Language groups) 25,598 participants studying FutureLearn "Understanding Language: Learning and Teaching" MOOC using natural language processing. We compared algorithms' performance when extracting discernible features in participants' engagement. Engagement in discussions at the end of the first week is one of the strongest predictive features, while overall, learner behaviours in the first two weeks were identified as the most strongly predictive feature.

English as a second language, MOOCs, Natural language processing, Prediction model, Sustainability
2071-1050
1-24
Duru, Ismail
deabd39c-9f2f-4d56-ac2e-bb205a89a55d
Sunar, Ayse Saliha
8a121335-66ed-4a7a-93b2-eb8194b89868
White, Su
5f9a277b-df62-4079-ae97-b9c35264c146
Diri, Banu
bb699481-69e7-47fd-9bef-5923de449d8d
Dogan, Gulustan
30ad4cd6-1955-4ced-882c-d76a0ec25741
Duru, Ismail
deabd39c-9f2f-4d56-ac2e-bb205a89a55d
Sunar, Ayse Saliha
8a121335-66ed-4a7a-93b2-eb8194b89868
White, Su
5f9a277b-df62-4079-ae97-b9c35264c146
Diri, Banu
bb699481-69e7-47fd-9bef-5923de449d8d
Dogan, Gulustan
30ad4cd6-1955-4ced-882c-d76a0ec25741

Duru, Ismail, Sunar, Ayse Saliha, White, Su, Diri, Banu and Dogan, Gulustan (2019) A case study on english as a second language speakers for sustainable MOOC study. Sustainability, 11 (10), 1-24. (doi:10.3390/su11102808).

Record type: Article

Abstract

Massive Open Online Courses (MOOCs) have a great potential for sustainable education. Millions of learners annually enrol on MOOCs designed to meet the needs of an increasingly diverse and international student population. Participants' backgrounds vary by factors including age, education, location, and first language. MOOC authors address consequent needs by ensuring courses are well-organised. Learning is structured into discrete steps, prioritising clear communication; video components incorporate subtitles. Variability in participants' language abilities inevitably create barriers to learning, a problem most extreme for those studying in a language which is not their first. This paper investigates how to identify ESL participants and how best to predict factors associated with their course completion. This study proposes a novel method for automatically categorising (English as Primary and Official Language; English as Official but not Primary Language; and English as a second Language groups) 25,598 participants studying FutureLearn "Understanding Language: Learning and Teaching" MOOC using natural language processing. We compared algorithms' performance when extracting discernible features in participants' engagement. Engagement in discussions at the end of the first week is one of the strongest predictive features, while overall, learner behaviours in the first two weeks were identified as the most strongly predictive feature.

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sustainability-11-02808 - Version of Record
Available under License Creative Commons Attribution.
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More information

Accepted/In Press date: 9 May 2019
Published date: 16 May 2019
Keywords: English as a second language, MOOCs, Natural language processing, Prediction model, Sustainability

Identifiers

Local EPrints ID: 432029
URI: https://eprints.soton.ac.uk/id/eprint/432029
ISSN: 2071-1050
PURE UUID: 3e31187e-8386-4f8d-a822-33ecf149ee7b
ORCID for Su White: ORCID iD orcid.org/0000-0001-9588-5275

Catalogue record

Date deposited: 27 Jun 2019 16:30
Last modified: 20 Jul 2019 01:09

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Contributors

Author: Ismail Duru
Author: Ayse Saliha Sunar
Author: Su White ORCID iD
Author: Banu Diri
Author: Gulustan Dogan

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