How Knowledge Stock Exchanges can increase student success in Massive Open Online Courses.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2019
Historique:
received: 29 03 2019
accepted: 12 09 2019
entrez: 27 9 2019
pubmed: 27 9 2019
medline: 13 3 2020
Statut: epublish

Résumé

Massive Open Online Courses (MOOCs) allow lecturers to overcome spatiotemporal boundaries and reach large numbers of participants. However, the completion rates of MOOCs are relatively low, a critical obstacle to their ultimate success. Existing literature suggests that strengthening student interaction has the potential to increase student commitment. The goal of this study is to develop a novel, market-based knowledge-sharing method that fosters student engagement and interaction in MOOCs, addressing the problem of low completion rates and demonstrating how MOOC engagement can lead to greater student success. The proposed method, "Knowledge Stock Exchange" (KSX), is derived from the concept of crowd-based intelligence mechanisms for incentive-compatible information aggregation. Using a popular MOOC as the focus of our empirical study, we show that the KSX method increases student interaction as well as MOOC completion rates. Moreover, we find that KSX participation has a significant positive effect on participants' exam grades.

Identifiants

pubmed: 31557244
doi: 10.1371/journal.pone.0223064
pii: PONE-D-19-08958
pmc: PMC6762193
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0223064

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

PLoS One. 2009 Dec 30;4(12):e8500
pubmed: 20041139
Nature. 2013 Nov 21;503(7476):342
pubmed: 24256798
PLoS One. 2017 Mar 31;12(3):e0173403
pubmed: 28362821

Auteurs

Andreas Heusler (A)

Ludwig-Maximilians-University of Munich, Institute of Electronic Commerce and Digital Markets, Munich, Germany.

Dominik Molitor (D)

Gabelli School of Business, Fordham University, New York, NY, United States of America.

Martin Spann (M)

Ludwig-Maximilians-University of Munich, Institute of Electronic Commerce and Digital Markets, Munich, Germany.

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Classifications MeSH