Exploratory network analysis of learning motivation factors in e-learning facilitated computer programming courses
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journal contribution
posted on 2015-01-01, 00:00 authored by S C Ngan, Kris LawKris Law© 2014, De La Salle University. Educating our future engineers so that they can gain high proficiency in computational thinking is essential for their career prospects. As educators, acquiring a good understanding of the various learning motivation factors/tools as well as their inter-relationships is a significant step forward in achieving this goal. In this article, we describe an exploratory, data-analytic investigation into the influences of the various learning motivation factors on one another as well as on effecting e-learning of a group of science and engineering students taking computer programming courses. Based on the algorithmic results, we highlight concrete ideas that may have direct impact on improving an existing e-learning system. Further, we describe how the graphical visualization of the algorithmic results can guide us to set priority for focusing on which learning motivation factors first, and which factors next, in achieving a given education goal. These are among some of the new insights not easily obtainable from confirmatory-based analyses.
History
Journal
Asia-Pacific education researcherVolume
24Issue
4Pagination
705 - 717Publisher
SpringerLocation
Berlin, GermanyPublisher DOI
ISSN
0119-5646eISSN
2243-7908Language
engPublication classification
C Journal article; C1.1 Refereed article in a scholarly journalUsage metrics
Categories
Keywords
Intelligent systemsInteractive learning environmentsUndergraduate educationComputer programmingExploratory network analysisSocial SciencesEducation & Educational ResearchSELF-DETERMINATION THEORYINTRINSIC MOTIVATIONEXTRINSIC MOTIVATIONACHIEVEMENT GOALSSTUDENTSEFFICACYREWARDSPERFORMANCEBELIEFSCONSEQUENCESOrganisation of Information and Knowledge ResourcesEducationMechanical Engineering
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