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Instructional Design and Practice of Data Analysis Course for the Cultivation of Computational Thinking Ability

Received: 23 August 2019    Accepted: 06 September 2019    Published: 20 September 2019
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Abstract

Due to the rapid development of computer, network and data processing, computational thinking (CT) ability has played an important element for talent cultivation in the era of big data since it came out. As computer foundation course, Data Analysis Courses has a certain degree of fit with computational thinking training. In order to cultivate computational thinking, our team tried to integrate computational thinking into computer basic courses. To begin with, this paper analyzes the convergence of Data Analysis Courses and computational thinking, and puts forward the instructional design of Statistical Product and Service Solutions (SPSS) data analysis course from the perspective of computational thinking. Then based on the problem-based learning, the instructional design is carried out from three levels: comprehension and understanding, simple application and comprehensive application. Finally, aiming at improving students’ CT ability, the teaching activities are designed progressively in computational thinking knowledge, CT skills, CT consciousness and CT strategy. Based on the above work, we carried out the Data Analysis course for the development of computational thinking. After a semester of practice, the results demonstrates that the teaching model proposed in this article enhances the students' CT ability significantly, and Data Analysis Courses is of great significance to the cultivation of students' CT.

DOI 10.11648/j.edu.20190806.13
Published in Education Journal (Volume 8, Issue 6, November 2019)
Page(s) 249-258
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Computational Thinking, Data Analysis Courses, Teaching Model, Problem-based Learning

References
[1] ISTE&CSTA (2011a). Operational Definition of Computational Thinking for K-12 Education [EB/OL]. [2017-07-01]. http://www.iste.org/docs/ct-documents/computational-thinking-operational-definition-flyer.pdf?sfvrsn=2.
[2] Wu L. C., Li L. (2017), Computer Liberal Curriculum Construction under the Background of New Engineering. China University Teaching: 12, 62-69.
[3] Chen G. L., Dong R. S. (2011), Computational Thinking and Computer Basic Education in University. China University Teaching: 01, 7-11+32.
[4] Dong R. S. (2010), Joint Statement of the Nine School Alliance (C9) Computer Basic Teaching Development Strategy. China University Teaching: 10, 14-15.
[5] Chen P., Huang R. H., Liang Y., and Zhang J. B. How to develop computational thinking-based on 2006-2016 research literature and the latest international conference papers. Modern Distance Education Research: 01, 98-112.
[6] Wing, J. M. (2006). Computational thinking. Communications of the ACM: 49 (3). 33-35.
[7] Cuny, J., Snyder, L., & Wing, J. M. (2010). Demystifying computational thinking for non-computer scientists. Unpublished manuscript in progress, referenced in http://www. cs. cmu. edu/~CompThink/resources/TheLinkWing. pdf.
[8] ISTE&CSTA (2011a). Operational Definition of Computational Thinking for K-12 Education [EB/OL]. [2017-07-01]. http://www.iste.org/docs/ct-documents/computational-thinking-operational-definition-flyer.pdf?sfvrsn=2.
[9] Yadav, A., Hong, H., and Stephenson, C. (2016). Computational thinking for all: pedagogical approaches to embedding 21st century problem solving in K-12 classroom. Tech Trends: 60 (6), 565-568.
[10] Wing, J. M. (2010). Computational thinking: What and why?. Unpublished manuscript. Pittsburgh, PA: Computer Science Department, Carnegie Mellon University. Retrieved from https://www.cs.cmu.edu/~CompThink/resources/TheLinkWing.pdf.
[11] Denning, P. J., and Martell, C. H. (2015). Great Principles of Computing. ACM.
[12] Liu M. N., Zhang Q. W. (2018). Research Progress in Foreign Computing Thinking Education. Open Education Research: 24 (01), 41-53.
[13] Loyens, Sofie M. M., Marcq, Helene, Gijbels, David, Dolmans, Diana H J M. (2016). Deep and surface learning in problem-based learning: A review of the literature. Advances in Health Sciences Education: 21 (5), 1087-1112.
[14] Liu M. N., Zhang Q. W. (2018). Research Progress in Foreign Computing Thinking Education. Open Education Research: 24 (01), 41-53.
[15] Zhang L., Wei L. F. (2015). Exploration of the "Data Analysis" Course in the Age of Big Data. Education Teaching Forum: 25, 154-155.
[16] Barrows, H. S. (1996). Problem-based learning in medicine and beyond: A brief overview. In L. Wilkerson & W. H. Gijselaers (Eds.), New directions for teaching and learning (Vol. 68, pp. 3–12). San Francisco: Jossey-Bass.
Author Information
  • Faculty of Education, Beijing Normal University, Beijing, China

  • Faculty of Education, Beijing Normal University, Beijing, China

  • Faculty of Education, Beijing Normal University, Beijing, China

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    Xiulin Ma, Jingjing Liu, Jing Liang. (2019). Instructional Design and Practice of Data Analysis Course for the Cultivation of Computational Thinking Ability. Education Journal, 8(6), 249-258. https://doi.org/10.11648/j.edu.20190806.13

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    ACS Style

    Xiulin Ma; Jingjing Liu; Jing Liang. Instructional Design and Practice of Data Analysis Course for the Cultivation of Computational Thinking Ability. Educ. J. 2019, 8(6), 249-258. doi: 10.11648/j.edu.20190806.13

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    AMA Style

    Xiulin Ma, Jingjing Liu, Jing Liang. Instructional Design and Practice of Data Analysis Course for the Cultivation of Computational Thinking Ability. Educ J. 2019;8(6):249-258. doi: 10.11648/j.edu.20190806.13

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  • @article{10.11648/j.edu.20190806.13,
      author = {Xiulin Ma and Jingjing Liu and Jing Liang},
      title = {Instructional Design and Practice of Data Analysis Course for the Cultivation of Computational Thinking Ability},
      journal = {Education Journal},
      volume = {8},
      number = {6},
      pages = {249-258},
      doi = {10.11648/j.edu.20190806.13},
      url = {https://doi.org/10.11648/j.edu.20190806.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.edu.20190806.13},
      abstract = {Due to the rapid development of computer, network and data processing, computational thinking (CT) ability has played an important element for talent cultivation in the era of big data since it came out. As computer foundation course, Data Analysis Courses has a certain degree of fit with computational thinking training. In order to cultivate computational thinking, our team tried to integrate computational thinking into computer basic courses. To begin with, this paper analyzes the convergence of Data Analysis Courses and computational thinking, and puts forward the instructional design of Statistical Product and Service Solutions (SPSS) data analysis course from the perspective of computational thinking. Then based on the problem-based learning, the instructional design is carried out from three levels: comprehension and understanding, simple application and comprehensive application. Finally, aiming at improving students’ CT ability, the teaching activities are designed progressively in computational thinking knowledge, CT skills, CT consciousness and CT strategy. Based on the above work, we carried out the Data Analysis course for the development of computational thinking. After a semester of practice, the results demonstrates that the teaching model proposed in this article enhances the students' CT ability significantly, and Data Analysis Courses is of great significance to the cultivation of students' CT.},
     year = {2019}
    }
    

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    T1  - Instructional Design and Practice of Data Analysis Course for the Cultivation of Computational Thinking Ability
    AU  - Xiulin Ma
    AU  - Jingjing Liu
    AU  - Jing Liang
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    N1  - https://doi.org/10.11648/j.edu.20190806.13
    DO  - 10.11648/j.edu.20190806.13
    T2  - Education Journal
    JF  - Education Journal
    JO  - Education Journal
    SP  - 249
    EP  - 258
    PB  - Science Publishing Group
    SN  - 2327-2619
    UR  - https://doi.org/10.11648/j.edu.20190806.13
    AB  - Due to the rapid development of computer, network and data processing, computational thinking (CT) ability has played an important element for talent cultivation in the era of big data since it came out. As computer foundation course, Data Analysis Courses has a certain degree of fit with computational thinking training. In order to cultivate computational thinking, our team tried to integrate computational thinking into computer basic courses. To begin with, this paper analyzes the convergence of Data Analysis Courses and computational thinking, and puts forward the instructional design of Statistical Product and Service Solutions (SPSS) data analysis course from the perspective of computational thinking. Then based on the problem-based learning, the instructional design is carried out from three levels: comprehension and understanding, simple application and comprehensive application. Finally, aiming at improving students’ CT ability, the teaching activities are designed progressively in computational thinking knowledge, CT skills, CT consciousness and CT strategy. Based on the above work, we carried out the Data Analysis course for the development of computational thinking. After a semester of practice, the results demonstrates that the teaching model proposed in this article enhances the students' CT ability significantly, and Data Analysis Courses is of great significance to the cultivation of students' CT.
    VL  - 8
    IS  - 6
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