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The use of gamification in xMOOCs about energy: Effects and predictive models for participants' learning

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dc.contributor.author Rincón-Flores E.G.
dc.contributor.author Mena J.
dc.contributor.author Ramírez-Montoya M.S.
dc.contributor.author Velarde R.R.
dc.date.accessioned 2021-02-25T20:56:20Z
dc.date.available 2021-02-25T20:56:20Z
dc.date.issued 2020
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/162735
dc.description.abstract © 2020 Australasian Society for Computers in Learning in Tertiary Education (ASCILITE). Open access education has significantly grown in strength as a new way of fostering innovation in schools. Such is the case of massive open online courses (MOOCs), which have the added benefit of encouraging the democratisation of learning. In this sense, the Bi-National Laboratory on Smart Sustainable Energy Management and Technology Training between Mexico and the United States of America was launched with the purpose of trying MOOC technology and measuring its impact on the academic, business, and social sectors. Under this scenario, this study aimed to show the relationship between using gamification and level of performance in a MOOC on energy topics. The methodology was quantitative, using the course analytical data for socio-demographic information and predictive models. A total of 6246 participants enrolled in the MOOC and 1060 finished it. The results showed that participants aged between 20 and 50 had the highest completion rates in the gamified challenge; the higher academic degree, the more inclined participants were to solve the gamified challenge; and no such distinction exists by gender. Articles published in the Australasian Journal of Educational Technology (AJET) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0).
dc.subject Gamification in education
dc.subject Gamified challenge
dc.subject Higher education
dc.subject MOOCs
dc.subject Teaching methods
dc.title The use of gamification in xMOOCs about energy: Effects and predictive models for participants' learning
dc.type Article
dc.relation.ispartofseries-issue 2
dc.relation.ispartofseries-volume 36
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 1
dc.source.id SCOPUS-2020-36-2-SID85083184560


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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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