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Computational What? Relating Computational Thinking to Teaching

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Abstract

Computational thinking is one of the skills critical for successfully solving problems posed in a technology driven and complex society. The limited opportunities in school settings to help students develop computational thinking skills underscores the need for helping teachers integrate it in their practices. Besides developing the knowledge of technology, content, and pedagogy, teachers need to recognize the relevance of computational thinking to their teaching, a factor influencing their future practice with it. Drawing from the literature on problem-solving and TPACK framework, this paper discusses strategies, including content-specific examples, problem-solving nature of computational thinking, and the methods of teaching problem-solving for enabling teachers to make the connections between computational thinking and their practices.

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References

  • Akcaoglu, M. (2014). Learning problem-solving through making games at the game design and learning summer program. Educational Technology Research & Development., 62(5), 583–600. https://doi.org/10.1007/s11423-014-9347-4.

    Article  Google Scholar 

  • Authors et al. (2016). Details removed for peer review.

  • Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48–54.

    Article  Google Scholar 

  • Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. Learning & Leading With Technology, 38(6), 20–23.

    Google Scholar 

  • Corradini, I., Lodi, M., & Nardelli, E. (2017). Conceptions and Misconceptions about Computational Thinking among Italian Primary School Teachers. In Proceedings of the 2017 ACM Conference on International Computing Education Research (pp. 136–144). ACM.

  • Duncan, C., Bell, T., & Atlas, J. (2017). What do the teachers think?: Introducing computational thinking in the primary school curriculum. In Proceedings of the Nineteenth Australasian Computing Education Conference (pp. 65–74). ACM.

  • Ertmer, P. A., Ottenbreit-Leftwich, A. T., Sadik, O., Sendurur, E., & Sendurur, P. (2012). Teacher beliefs and technology integration practices: A critical relationship. Computers and Education, 59(2), 423–435. https://doi.org/10.1016/j.compedu.2012.02.001.

    Article  Google Scholar 

  • Garet, M. S., Porter, A. C., Desimone, L., Birman, B. F., & Kwang Suk, Y. (2001). What makes professional development effective? Results from a national sample of teachers. American Educational Research Journal, 38(4), 915–945.

    Article  Google Scholar 

  • Giannakos, M. N., Doukakis, S., Pappas, I. O., et al. (2015). Investigating teachers’ confidence on technological pedagogical and content knowledge: An initial validation of TPACK scales in K-12 computing education context. Journal of Computers in Education., 2(1), 43–59. https://doi.org/10.1007/s40692-014-0024-8.

    Article  Google Scholar 

  • Gick, M. L., & Holyoak, K. J. (1980). Analogical problem solving. Cognitive Psychology, 12, 306–355.

    Article  Google Scholar 

  • Gick, M. L., & Holyoak, K. J. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15, 1–38.

    Article  Google Scholar 

  • Goh, D., & Kale, U. (2015). The urban-rural gap: Project-based learning with technology among west Virginian teachers. Technology Pedagogy, and Education. https://doi.org/10.1080/1475939X.2015.1051490.

  • Google Inc., & Gallup Inc. (2016). Trends in the state of computer science in U.S. K-12 schools. In Retrieved from http://goo.gl/j291E0.

    Google Scholar 

  • Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051.

    Article  Google Scholar 

  • Greiff, S., Wüu, S., WuS, S., Csapó, B., Demetriou, A., Hautamäki, J., Graesser, A. C., & Martin, R. (2014). Domain-general problem solving skills and education in the 21st century. Educational Research Review, 13, 74–83.

    Article  Google Scholar 

  • Guzdial, M. (2016). State of the states: Progress toward CS for all. Communications of the ACM. Retrieved from http://cacm.acm.org/blogs/blog-cacm/198790-state-of-the-states-progress-toward-cs-for-all/fulltext

  • Herold, B. (2017). Computer science for all in San Francisco schools: 7 Early takeaways [Blog post]. Retrieved from http://blogs.edweek.org/edweek/DigitalEducation/2017/04/computer_science_for_all_san_francisco_7_takeaways.html?cmp=SOC-SHR-twitter

  • Hulleman, C. S., Godes, O., Hendricks, B. L., & Harackiewicz, J. M. (2010). Enhancing interest and performance with a utility value intervention. Journal of Educational Psychology, 102(4), 880–895. https://doi.org/10.1037/a0019506.

    Article  Google Scholar 

  • International Society for Technology in Education (ISTE). (2016). ISTE national educational technology standards (NETS) for students. Eugene, OR :International Society for Technology in Education.

  • Jonassen, D. H. (2004). Learning to solve problems: An instructional design guide (Vol. 6). San Francisco, CA: Wiley.

  • Kilpatrick, J., Swafford, J., & Findell, B. (2001). Adding it up: Helping children learn mathematics. Washington, DC: National Academy Press.

    Google Scholar 

  • Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86.

    Article  Google Scholar 

  • Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge university press.

  • Linden, M., & Wittrock, M. C. (1981). The teaching of reading comprehension according to the model of generative learning. Reading Research Quarterly, 44–57.

  • Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. The American Psychologist, 59(1), 14–19. https://doi.org/10.1037/0003-066X.59.1.14.

    Article  Google Scholar 

  • Mayer, R. E., & Wittrock, M. C. (1996). Problem-solving transfer. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 47–62). New York, NY: Macmillan Library Reference.

    Google Scholar 

  • Mayer, R. E., & Wittrock, M. C. (2006). Problem solving. In P. A. Alexander, P. H. Winne, P. A. Alexander, P. H. Winne (Eds.), Handbook of educational psychology (pp. 287–303). Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers.

  • van Merriënboer, J. J. G. (2013). Perspectives on problem solving and instruction. Computers & Education, 64, 153–160. https://doi.org/10.1016/j.compedu.2012.11.025.

    Article  Google Scholar 

  • Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A new framework for teacher knowledge. Teachers College Record, 108, 1017–1054.

    Article  Google Scholar 

  • OECD. (2003). PISA 2003 assessment framework: Mathematics, reading, science and problem solving knowledge and skills. OECD Publishing. Retrieved from http://www.oecd.org/edu/school/programmeforinternationalstudentassessmentpisa/33694881.pdf

  • Palinscar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and Instruction, 1(2), 117–175.

    Article  Google Scholar 

  • Quilici, J. L., & Mayer, R. E. (2002). Teaching students to recognize structural similarities between statistics word problems. Applied Cognitive Psychology, 16(3), 325–342.

    Article  Google Scholar 

  • Rampey, B.D., Finnegan, R., Goodman, M., Mohadjer, L., Krenzke, T., Hogan, J., and Provasnik, S. (2016). Skills of U.S. unemployed, young, and older adults in sharper focus: Results from the program for the international assessment of adult competencies (PIAAC) 2012/2014: First look (NCES 2016-039). U.S. Department of Education. Washington, DC: National Center for Education Statistics Retrieved from http://nces.ed.gov/pubsearch

  • Resnick, L. B. (1987). The 1987 presidential address: Learning in school and out. Educational Researcher, 16(9), 13–20.

    Google Scholar 

  • Samuels, S. J. (1979). The method of repeated readings. The Reading Teacher, 32(4), 403–408.

    Google Scholar 

  • Scratch. (n.d.-a). Monthly project shares. Retrieved March 28, 2017, from https://scratch.mit.edu/statistics/

  • Scratch ED. (n.d.-b). Search resources. Retrieved March 28, 2017, from http://scratched.gse.harvard.edu/resources

  • Sonnleitner, P., Brunner, M., Keller, U., & Martin, R. (2014). Differential relations between facets of complex problem solving and students’ immigration background. Journal of Educational Psychology, 106(3), 681–695. https://doi.org/10.1037/a0035506.

    Article  Google Scholar 

  • The Fourth Industrial Revolution: What it means and how to respond. (n.d.). Retrieved from: http://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond .

  • Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015). Computational thinking in compulsory education: Towards an agenda for research and practice. Education and Information Technologies, 20(4), 715–728. https://doi.org/10.1007/s10639-015-9412-6.

    Article  Google Scholar 

  • Wing, J. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A, 366, 3717–3725. https://doi.org/10.1098/rsta.2008.0118.

    Article  Google Scholar 

  • White, B. Y. (1993). ThinkerTools: Causal models, conceptual change, and science education. Cognition and Instruction, 10(1), 1–100.

    Article  Google Scholar 

  • Wittrock, M. C. (1989). Generative processes of comprehension. Educational Psychologist, 24(4), 345–376.

    Article  Google Scholar 

  • Wittrock, M. C. (1991). Generative teaching of comprehension. The Elementary School Journal, 92(2), 169–184.

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Learning Sciences and Human Development Department, West Virginia University, 504-F Allen Hall, PO Box 6122, Morgantown, WV, 26505, USA

    Ugur Kale

  2. Georgia Southern University, Statesboro, GA, USA

    Mete Akcaoglu

  3. University of Oklahoma, Norman, OK, USA

    Theresa Cullen

  4. California University of Pennsylvania, California, PA, USA

    Debbie Goh

  5. West Virginia Virtual Schools, Charleston, WV, USA

    Leah Devine

  6. Hollywood Elementary School, Hollywood, MD, USA

    Nathan Calvert

  7. Mercyhurst Preparatory School, Erie, PA, USA

    Kara Grise

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  1. Ugur Kale
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  2. Mete Akcaoglu
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  3. Theresa Cullen
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  4. Debbie Goh
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  5. Leah Devine
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  6. Nathan Calvert
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  7. Kara Grise
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Correspondence to Ugur Kale.

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Kale, U., Akcaoglu, M., Cullen, T. et al. Computational What? Relating Computational Thinking to Teaching. TechTrends 62, 574–584 (2018). https://doi.org/10.1007/s11528-018-0290-9

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