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Tracking collective learner footprints: aggregate analysis of MOOC learner demographics and activity

Tracking collective learner footprints: aggregate analysis of MOOC learner demographics and activity
Tracking collective learner footprints: aggregate analysis of MOOC learner demographics and activity
Most MOOC platforms collect learner activity and demographics, and record them in datasets for their analysis. Cross-analysis of learner activity against demographic features such as age, profession, and country of origin can provide some insights on the diversity of learners and their behaviours. These analyses are often performed on individual courses, which not always provide comparable or generalisable results. Moreover, despite the massive numbers of learners, demographic data is often difficult to obtain, and represents a small proportion of the learning community. The analysis of aggregate data can help to enhance the significance generalisability of the results when combining activity and demographic data. However, aggregating data entails analytical challenges that can sometimes lead to misleading results. In this paper we analyse aggregate data from a set of MOOCs in a British university that uses the FutureLearn platform. Some of the results suggest that certain demographic features such as education level do not have a significant influence in engagement measures such as completion of courses. However, other demographic features such as age do have an influence. The paper then considers some of the caveats of aggregating data from different courses, and proposes solutions to overcome them.
1404-1413
IATED Academy
Wilde, Adriana
37ee0dec-a07f-4177-b291-96037fe48e14
Leon Urrutia, Manuel
4c9d6ced-5e35-4f09-827b-c2e4c702df3c
White, Su
5f9a277b-df62-4079-ae97-b9c35264c146
Gómez Chova, L
López Martínez, A
Candel Torres, I
Wilde, Adriana
37ee0dec-a07f-4177-b291-96037fe48e14
Leon Urrutia, Manuel
4c9d6ced-5e35-4f09-827b-c2e4c702df3c
White, Su
5f9a277b-df62-4079-ae97-b9c35264c146
Gómez Chova, L
López Martínez, A
Candel Torres, I

Wilde, Adriana, Leon Urrutia, Manuel and White, Su (2016) Tracking collective learner footprints: aggregate analysis of MOOC learner demographics and activity. Gómez Chova, L, López Martínez, A and Candel Torres, I (eds.) In ICERI2016 Proceedings (browse). IATED Academy. pp. 1404-1413 . (doi:10.21125/iceri.2016.1319).

Record type: Conference or Workshop Item (Paper)

Abstract

Most MOOC platforms collect learner activity and demographics, and record them in datasets for their analysis. Cross-analysis of learner activity against demographic features such as age, profession, and country of origin can provide some insights on the diversity of learners and their behaviours. These analyses are often performed on individual courses, which not always provide comparable or generalisable results. Moreover, despite the massive numbers of learners, demographic data is often difficult to obtain, and represents a small proportion of the learning community. The analysis of aggregate data can help to enhance the significance generalisability of the results when combining activity and demographic data. However, aggregating data entails analytical challenges that can sometimes lead to misleading results. In this paper we analyse aggregate data from a set of MOOCs in a British university that uses the FutureLearn platform. Some of the results suggest that certain demographic features such as education level do not have a significant influence in engagement measures such as completion of courses. However, other demographic features such as age do have an influence. The paper then considers some of the caveats of aggregating data from different courses, and proposes solutions to overcome them.

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Accepted/In Press date: 1 September 2016
e-pub ahead of print date: 15 November 2016
Venue - Dates: 9th Annual International Conference of Education, Research and Innovation (ICERI 2016), , Seville, Spain, 2015-11-14 - 2016-11-16
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 404190
URI: http://eprints.soton.ac.uk/id/eprint/404190
PURE UUID: 02e00e64-54e9-4519-a227-d92e6cdea805
ORCID for Adriana Wilde: ORCID iD orcid.org/0000-0002-1684-1539
ORCID for Su White: ORCID iD orcid.org/0000-0001-9588-5275

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Date deposited: 03 Jan 2017 14:39
Last modified: 06 Nov 2020 17:32

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Contributors

Author: Adriana Wilde ORCID iD
Author: Manuel Leon Urrutia
Author: Su White ORCID iD
Editor: L Gómez Chova
Editor: A López Martínez
Editor: I Candel Torres

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