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Analysing and predicting recurrent interactions among learners during online discussions in a MOOC

Analysing and predicting recurrent interactions among learners during online discussions in a MOOC
Analysing and predicting recurrent interactions among learners during online discussions in a MOOC
High attrition rates are one of the biggest concerns in MOOCs. One of the possible causes may be learners’ lack of interactions and low levels of participations in MOOCs online discussions. Research to measure and predict recurrent interactions of learners in MOOCs online discussions has the potential to gain inside into the likely impact on the attrition rate. It is argued that personalisation in MOOCs has the potential to increase learners’ interactions and associated factors to continuous friendships. In this paper, a detailed analysis has been carried out of learners’ interactions within a MOOC. This paper investigates learners’ interaction habits and their recurrent interactions throughout the entire duration of a MOOC’s course, and consequently proposes a method to measure the interactions and predict possible interactions between peers. The findings denote that when a learner interacted with their peer, they most probably interact again in the following weeks. Moreover, our proposed prediction method also demonstrate promising results towards predicting future interactions between learners based on their previous relationships
Sunar, Ayse Saliha
8a121335-66ed-4a7a-93b2-eb8194b89868
Abdullah, Nor Aniza
44443c5c-daab-4541-a89a-5220c6f81273
White, Susan
5f9a277b-df62-4079-ae97-b9c35264c146
Davis, Hugh C.
1608a3c8-0920-4a0c-82b3-ee29a52e7c1b
Sunar, Ayse Saliha
8a121335-66ed-4a7a-93b2-eb8194b89868
Abdullah, Nor Aniza
44443c5c-daab-4541-a89a-5220c6f81273
White, Susan
5f9a277b-df62-4079-ae97-b9c35264c146
Davis, Hugh C.
1608a3c8-0920-4a0c-82b3-ee29a52e7c1b

Sunar, Ayse Saliha, Abdullah, Nor Aniza, White, Susan and Davis, Hugh C. (2015) Analysing and predicting recurrent interactions among learners during online discussions in a MOOC. 11th International Conference on Knowledge Management ICKM 2015, Osaka, Japan. 04 - 06 Nov 2015.

Record type: Conference or Workshop Item (Paper)

Abstract

High attrition rates are one of the biggest concerns in MOOCs. One of the possible causes may be learners’ lack of interactions and low levels of participations in MOOCs online discussions. Research to measure and predict recurrent interactions of learners in MOOCs online discussions has the potential to gain inside into the likely impact on the attrition rate. It is argued that personalisation in MOOCs has the potential to increase learners’ interactions and associated factors to continuous friendships. In this paper, a detailed analysis has been carried out of learners’ interactions within a MOOC. This paper investigates learners’ interaction habits and their recurrent interactions throughout the entire duration of a MOOC’s course, and consequently proposes a method to measure the interactions and predict possible interactions between peers. The findings denote that when a learner interacted with their peer, they most probably interact again in the following weeks. Moreover, our proposed prediction method also demonstrate promising results towards predicting future interactions between learners based on their previous relationships

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More information

Published date: November 2015
Venue - Dates: 11th International Conference on Knowledge Management ICKM 2015, Osaka, Japan, 2015-11-04 - 2015-11-06
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 381181
URI: http://eprints.soton.ac.uk/id/eprint/381181
PURE UUID: 2124b0c2-292c-4837-a51a-03e0b804ecf2
ORCID for Susan White: ORCID iD orcid.org/0000-0001-9588-5275
ORCID for Hugh C. Davis: ORCID iD orcid.org/0000-0002-1182-1459

Catalogue record

Date deposited: 25 Sep 2015 11:03
Last modified: 14 Jul 2020 00:27

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