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Exploratory network analysis of learning motivation factors in e-learning facilitated computer programming courses

Version 2 2024-06-05, 03:01
Version 1 2019-01-22, 10:50
journal contribution
posted on 2024-06-05, 03:01 authored by SC 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 researcher

Volume

24

Pagination

705-717

Location

Berlin, Germany

ISSN

0119-5646

eISSN

2243-7908

Language

eng

Publication classification

C Journal article, C1.1 Refereed article in a scholarly journal

Issue

4

Publisher

Springer