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Exploratory Network Analysis of Learning Motivation Factors in e-Learning Facilitated Computer Programming Courses

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Abstract

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.

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Acknowledgments

The authors very much appreciate the constructive comments and suggestions raised by the anonymous reviewers, the Associate Editor, and the Editor, which helped in improving the content and the presentation of this article.

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Authors and Affiliations

  1. Department of Systems Engineering and Engineering Management, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong

    Shing-Chung Ngan

  2. Department of Industrial and Systems Engineering, FJ 602, The Hong Kong Polytechnic University, Hunghom, Hong Kong

    Kris M. Y. Law

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  1. Shing-Chung Ngan
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Correspondence to Shing-Chung Ngan.

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Ngan, SC., Law, K.M.Y. Exploratory Network Analysis of Learning Motivation Factors in e-Learning Facilitated Computer Programming Courses. Asia-Pacific Edu Res 24, 705–717 (2015). https://doi.org/10.1007/s40299-014-0223-0

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