Teaching Programming Using the Robot-Based Learning Approach

  • Stephanus Mberema Kangungu
  • Maizatul Hayati Mohamad Yatim
Keywords: Active Learning Theory, Computer Programming, Constructivism, Eduactional Robotics, STEM

Abstract

This paper discusses learning theories, STEM, educational robotics as well as the current generation of students found in classrooms by reviewing previous and current academic literature on these topics, to assist in ascertaining the current advancements and theories related to the Robot-based learning approach as well as how these advancements have helped improve this approach and enabled educators to better make use of it. Furthermore, this paper reviews previous academic literature on computer programming to discuss the current learning approaches in use and the kind of learning tools being utilized. Once this topics are reviewed the reader can have a clear picture of the learning approach, what learning theory does it belong too, the type of students found in the classroom as well as what motivates them and the subject that is being taught as well as the different learning tools for this subject. The reader will also learn why improving the effectiveness of how programming is learned helps create more students good in STEM and how it assists in realising the Malaysian Educational Development Plan. This paper reviewed literature from the year 2014 and above as the information is more relevant and current, except for literature that is from a leading or renowned individual in any field that is being discussed in this dissertation.

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Author Biographies

Stephanus Mberema Kangungu

Universiti Pendidikan Sultan Idris, Malaysia.

Maizatul Hayati Mohamad Yatim

Universiti Pendidikan Sultan Idris, Malaysia.

This is an open access article, licensed under CC-BY-SA

Creative Commons License
Published
        Views : 527
2020-12-07
    Downloads : 415
How to Cite
[1]
S. M. Kangungu and M. H. Mohamad Yatim, “Teaching Programming Using the Robot-Based Learning Approach”, International Journal of Artificial Intelligence, vol. 7, no. 2, pp. 22-28, Dec. 2020.
Section
Articles

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