Exploratory Network Analysis of Learning Motivation Factors in e-Learning Facilitated Computer Programming Courses
Asia-Pacific Education Researcher Volume 24, Number 4, ISSN 0119-5646
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.
Ngan, S.C. & Law, K.M.Y. (2015). Exploratory Network Analysis of Learning Motivation Factors in e-Learning Facilitated Computer Programming Courses. Asia-Pacific Education Researcher, 24(4), 705-717.
Cited ByView References & Citations Map
Student enrollment, motivation and learning performance in a blended learning environment: The mediating effects of social, teaching, and cognitive presence
Kris M.Y. Law, School of Engineering, Australia; Shuang Geng, Department of Management Science, China; Tongmao Li, Department of Business Administration, China
Computers & Education Vol. 136, No. 1 (July 2019) pp. 1–12
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