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

Xiulin Ma; Jingjing Liu; Jing Liang

doi:doi:10.11648/j.edu.20190806.13

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

Xiulin Ma, Jingjing Liu, Jing Liang

Published in Education Journal (Volume 8, Issue 6)

Received: 23 August 2019 Accepted: 6 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.

Published in	Education Journal (Volume 8, Issue 6)
DOI	10.11648/j.edu.20190806.13
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), 2019. Published by Science Publishing Group

Keywords

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

References

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[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.

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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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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://article.sciencepublishinggroup.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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TY  - JOUR
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
Y1  - 2019/09/20
PY  - 2019
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
ER  -

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Author Information

Xiulin Ma

Faculty of Education, Beijing Normal University, Beijing, China
Jingjing Liu

Faculty of Education, Beijing Normal University, Beijing, China
Jing Liang

Faculty of Education, Beijing Normal University, Beijing, China

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Plain Text BibTeX RIS

APA Style

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://article.sciencepublishinggroup.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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TY  - JOUR
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
Y1  - 2019/09/20
PY  - 2019
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
ER  -

Copy | Download