Publication Trends Over 10 Years of Computational Thinking Research

Year 2018, Volume: 9 Issue: 2, 131 - 153, 16.04.2018

Ulas Ilic , Halil İbrahim Haseski Ufuk Tugtekin

https://doi.org/10.30935/cet.414798

Cited By: 2

Abstract

The current study aimed to review studies
on computational thinking (CT) indexed in Web of Science (WOS) and ERIC
databases. A thorough search in electronic databases revealed 96 studies on
computational thinking which were published between 2006 and 2016. Studies were
exposed to a quantitative content analysis through using an article control form
developed by the researchers. Studies were summarized under several themes
including the research purpose, design, methodology, sampling characteristics,
data analysis, and main findings. The findings were reported using descriptive
statistics to see the trends. It was observed that there was an increase in the
number of CT studies in recent years, and these were mainly conducted in the
field of computer sciences. In addition, CT studies were mostly published in journals
in the field of Education and Instructional Technologies. Theoretical paradigm
and literature review design were preferred more in previous studies. The most commonly
used sampling method was the purposive sampling. It was also revealed that
samples of previous CT studies were generally pre-college students. Written
data collection tools and quantitative analysis were mostly used in reviewed
papers. Findings mainly focused on CT skills. Based on current findings, recommendations
and implications for further researches were provided. 

Keywords

Computational thinking, ; Research trends, Content analysis, Problem solving

References

• Aho, A. V. (2012). Computation and computational thinking. The Computer Journal, 55(7), 832-835. https://doi.org/10.1093/comjnl/bxs074
• Akbulut, Y. & Cardak, C. S. (2012). Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011. Computers & Education, 58(2), 835-842. https://doi.org/10.1016/j.compedu.2011.10.008
• Baker, M. J. (2003). Data collection– questionnaire design. The Marketing Review, 3(3), 343–370. https://doi.org/10.1362/146934703322383507
• 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. https://doi.org/10.1145/1929887.1929905
• Bauer, M.W. (2000). Classic content analysis: A review. In M.W. Bauer, & G. Gaskell (Eds.), Qualitative researching with text, image and sound: A practical handbook (pp. 131–151). London: Sage.
• Berland, M. & Wilensky, U. (2015). Comparing virtual and physical robotics environments for supporting complex systems and computational thinking. Journal of Science Education and Technology, 24(5), 628-647. https://doi.org/10.1007/s10956-015-9552-x
• Bers, M. U. (2010). The TangibleK robotics program: Applied computational thinking for young children. Early Childhood Research & Practice, 12(2).
• Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education, 72, 145-157. https://doi.org/10.1016/j.compedu.2013.10.020
• Buyukozturk, S., Kilic Cakmak, E., Akgun, O. E., Karadeniz, S., & Demirel, F. (2009). Bilimsel araştirma yontemleri. Ankara: Pegem Akademi.
• Cecilia Martinez, M. & Emilia Echeveste, M. (2015). Primary and secondary school students' representation about computer sciences and their job. RED-Revista De Educacion A Distancia, (46).
• Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. Thousand Oaks, CA: Sage.
• Creswell, J. W. & Poth, C. N. (2017). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). USA: Sage Publications.
• Denner, J., Werner, L., Campe, S., & Ortiz, E. (2014). Pair programming: Under what conditions is it advantageous for middle school students? Journal of Research on Technology in Education, 46(3), 277-296. https://doi.org/10.1080/15391523.2014.888272
• Denning, P. J. (2009). The profession of IT beyond computational thinking. Communications of The ACM, 52(6), 28-30. https://doi.org/10.1145/1516046.1516054
• Denning, P. J. (2012). Reflections on a symposium on computation. The Computer Journal, 55(7), 799-802. https://doi.org/10.1093/comjnl/bxs064
• DeSchryver, M. D. & Yadav, A. (2015). Creative and computational thinking in the context of new literacies: Working with teachers to scaffold complex technology-mediated approaches to teaching and learning. Journal of Technology and Teacher Education, 23(3), 411-431.
• Espino Espino, E. E., & Gonzalez Gonzalez, C. S. (2015). A study on gender differences in the skills and educational strategies for the development of computational thinking. RED-Revista De Educacion A Distancia, (46).
• Farris, A. V. & Sengupta, P. (2016). Democratizing children's computation: Learning computational science as aesthetic experience. Educational Theory, 66(1-2), 279-296. https://doi.org/10.1111/edth.12168
• Flick, U. (2008). Designing qualitative research. London: Sage.
• Fraenkel, J. R. & Wallen, N. (2000). How to design and evaluate research in education (4th ed.). New York: McGraw-Hill.
• Garfield, E. (1972). Citation analysis as a tool in journal evaluation. Science, 178(4060), 471-479.
• Goktas, Y., Kucuk, S., Aydemir, M., Telli, E., Arpacik, O., Yildirim, G., & Reisoglu, I. (2012). Educational technology research trends in Turkey: A content analysis of the 2000-2009 decade. Educational Sciences: Theory and Practice, 12(1), 191-199.
• Google for Education (2015). Exploring computational thinking. Retrieved on March 31, 2017, from https://edu.google.com/resources/programs/exploring-computational-thinking/
• 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
• Grover, S., Pea, R., & Cooper, S. (2015). Designing for deeper learning in a blended computer science course for middle school students. Computer Science Education, 25(2), 199-237. https://doi.org/10.1080/08993408.2015.1033142
• Guzdial, M. (2008). Education paving the way for computational thinking. Communications of The ACM, 51(8), 25-27. https://doi.org/10.1145/1378704.1378713
• Haseski, H. İ., İlic, U., & Tugtekin, U. (2017). Computational thinking in educational digital games: An assessment tool proposal. In H. Ozcinar, G. Wong & H.T. Ozturk (Eds.), Teaching computational thinking in primary education (pp. 256-287). Hershey, PA: IGI Global.
• Hew, K. F., Kale, U., & Kim, N. (2007). Past research in instructional technology: Results of a content analysis of empirical studies published in three prominent instructional technology journals from the year 2000 through 2004. Journal of Educational Computing Research, 36 (3), 269-300. https://doi.org/10.2190/K3P8-8164-L56J-33W4
• Hranstinski, S. & Keller, C. (2007). An examination of research approaches that underlie research on educational technology: A review from 2000 to 2004. Journal of Educational Computing Research, 36 (2), 175-190. https://doi.org/10.2190/H16L-4662-6000-0446
• Hwang, G. J. & Tsai, C. C. (2011). Research trends in mobile and ubiquitous learning: A review of publications in selected journals from 2001 to 2010. British Journal of Educational Technology, 42(4), E65-E70. https://doi.org/10.1111/j.1467-8535.2011.01183.x
• Ioannidou, A., Repenning, A., & Webb, D. C. (2009). AgentCubes: Incremental 3D end-user development. Journal of Visual Languages & Computing, 20(4), 236-251. https://doi.org/10.1016/j.jvlc.2009.04.001
• Israel, M., Pearson, J. N., Tapia, T., Wherfel, Q. M., & Reese, G. (2015). Supporting all learners in school-wide computational thinking: A cross-case qualitative analysis. Computers & Education, 82, 263-279. https://doi.org/10.1016/j.compedu.2014.11.022
• ISTE. (2016). CT leadership toolkit. Retrived on January 21, 2016, from http://www.iste.org/ docs/ct-documents/ct-leadershipt-toolkit.pdf?sfvrsn=4
• ISTE and CSTA. (2011). Operational definition of computational thinking for K-12 education. Retrieved on March 25, 2016, from http://www.iste.org/docs/ct-documents/computational-thinking-operational-definition-flyer.pdf?sfvrsn=2
• Jenkins, C. (2015). Poem generator: A comparative quantitative evaluation of a microworlds based learning approach for teaching English. International Journal of Education and Development using Information and Communication Technology, 11(2), 153.
• Jun, S., Han, S., Kim, H., & Lee, W. (2014). Assessing the computational literacy of elementary students on a national level in Korea. Educational Assessment, Evaluation and Accountability, 26(4), 319-332. https://doi.org/10.1007/s11092-013-9185-7
• Kafai, Y. B. & Burke, Q. (2013). Computer programming goes back to school. Phi Delta Kappan, 95(1), 61-65. https://doi.org/10.1177/003172171309500111
• Kim, B., Kim, T., & Kim, J. (2013). Paper-and-pencil programming strategy toward computational thinking for non-majors: Design your solution. Journal of Educational Computing Research, 49(4), 437-459. https://doi.org/10.2190/EC.49.4.b
• Kirk, J. & Miller, M. L. (1986). Reliability and validity in qualitative research. Sage.
• Kucuk, S., Aydemir, M., Yildirim, G., Arpacik, O., & Goktas, Y. (2013). Educational technology research trends in Turkey from 1990 to 2011. Computers & Education, 68, 42-50. https://doi.org/10.1016/j.compedu.2013.04.016
• Lim, S. (2015). Designing a literature instruction using big data. Advanced Science and Technology Letters, 97, 82-87. http://dx.doi.org/10.14257/astl.205.97.14
• Lomax, R. G. & Hahs-Vaughn, D. L. (2012). An introduction to statistical concepts (3rd ed.). New York: Taylor and Francis Group.
• Lu, J. J. & Fletcher, G. H. L. (2009). Thinking about computational thinking. ACM SIGCSE Bulletin, 41(1), 260. https://doi.org/10.1145/1539024.1508959
• Manson, J. R. & Olsen, R. J. (2010). Diagnostics and rubrics for assessing learning across the computational science curriculum. Journal of Computational Science, 1(1), 55-61. https://doi.org/10.1016/j.jocs.2010.03.012
• Marshall, C. & Rossman, G. B. (2014). Designing qualitative research (6th ed.). New York: Sage.
• Merriam, S. B. & Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation. New York: John Wiley & Sons.
• Miller, C. S., & Settle, A. (2011). When practice doesn’t make perfect: Effects of task goals on learning computing concepts. ACM Transactions on Computing Education (TOCE), 11(4), 22. https://dl.acm.org/citation.cfm?doid=2048931.2048933
• Moreno-León, J., Robles, G., & Román-González, M.(2015). Dr. Scratch: Automatic analysis of Scratch projects to assess and foster computational thinking. RED. Revista de Educación a Distancia, 15(46), 1-23.
• Mohaghegh, M. & McCauley, M. (2016). Computational thinking: The skill set of the 21st century. International Journal of Computer Science and Information Technologies (IJCSIT), 7(3), 1524-1530.
• Navlakha, S. & Bar‐Joseph, Z. (2011). Algorithms in nature: The convergence of systems biology and computational thinking. Molecular systems biology, 7(1), 546. . https://doi.org/10. 1038/msb.2011.78
• Olabe, X. B., Basogain, M. A. O., & Basogain, J. C. O. (2015). Computational thinking trough programming: A learning paradigm. RED-Revista De Educacion A Distancia, 46(6), 1-33.
• Papert, S. (1996). An exploration in the space of mathematics educations. International Journal of Computers for Mathematical Learning, 1(1), 95-123.
• Patton, M. Q. (2014). Qualitative research & evaluation methods: Integrating theory and practice (4th ed.). New York: Sage.
• Pellas, N. & Peroutseas, E. (2016). Gaming in Second Life via Scratch4SL: Engaging high school students in programming courses. Journal of Educational Computing Research, 54(1), 108-143. https://doi.org/10.1177/0735633115612785
• Psycharis, S. (2016). Inquiry based-computational experiment, acquisition of threshold concepts and argumentation in science and mathematics education. Educational Technology & Society, 19(3), 282-293.
• Repenning, A., Webb, D. C., Brand, C., Gluck, F., Grover, R., Miller, S., ... & Song, M. (2014). Beyond Minecraft: Facilitating computational thinking through modeling and programming in 3d. IEEE Computer Graphics and Applications, 34(3), 68-71. https://doi.org/10.1109/MCG.2014.46
• Repenning, A., Webb, D. C., Koh, K. H., Nickerson, H., Miller, S. B., Brand, C., ... & Repenning, N. (2015). Scalable game design: A strategy to bring systemic computer science education to schools through game design and simulation creation. ACM Transactions on Computing Education (TOCE), 15(2), 11. https://doi.org/10.1145/2700517
• Riley, D. D. & Hunt, K. A. (2014). Computational thinking for the modern problem solver. Florida: CRC Press.
• Ross, S. M., Morrison, G. R., & Lowther, D. L. (2010). Educational technology research past and present: Balancing rigor and relevance to impact school learning. Contemporary Educational Technology, 1(1), 17-35.
• Selby, C. C. (2014). How can the teaching of programming be used to enhance computational thinking skills? (Unpublished doctoral dissertation). University of Southampton, Southampton.
• Shell, D. F. & Soh, L. K. (2013). Profiles of motivated self-regulation in college computer science courses: Differences in major versus required non-major courses. Journal of Science Education and Technology, 22(6), 899-913. https://doi.org/10.1007/s10956-013-9437-9
• Shih, M. L., Feng, J., & Tsai, C. C. (2008). Research and trends in the field of e-learning from 2001 to 2005: A content analysis of cognitive studies in selected journals. Computers & Education, 51(2), 955–967. https://doi.org/10.1016/j.compedu.2007.10.004
• Simsek, A., Ozdamar, N., Uysal, O., Kobak, K., Berk, C., Kılıcer, T., & Cigdem, H. (2009). Current trends in educational technology research in Turkey in the new millennium. Educational Sciences: Theory & Practice, 9(2), 941-996.
• Snodgrass, M. R., Israel, M., & Reese, G. C. (2016). Instructional supports for students with disabilities in K-5 computing: Findings from a cross-case analysis. Computers & Education, 100, 1-17. https://doi.org/10.1016/j.compedu.2016.04.011
• Sorguc, A. G. (2013). Computational Models in Architecture: Understanding Multi-Dimensionality and Mapping. Nexus Network Journal, 15(2), 349-362. https://doi.org/10.1007/s00004-013-0150-z
• Srihari, S. N. & Singer, K. (2014). Role of automation in the examination of handwritten items. Pattern Recognition, 47(3), 1083-1095. https://doi.org/10.1016/j.patcog.2013.09.032
• Sullivan, F. R. & Heffernan, J. (2016). Robotic construction kits as computational manipulatives for learning in the STEM disciplines. Journal of Research on Technology in Education, 48(2), 105-128. https://doi.org/10.1080/15391523.2016.1146563
• Tahy, Z. S. (2016). How to teach programming indirectly - Using spreadsheet application. Acta Didactica Napocensia, 9(1), 15-22.
• Vallance, M. & Towndrow, P. A. (2016). Pedagogic transformation, student-directed design and computational thinking. Pedagogies: An International Journal, 11(3), 218-234. https://doi.org/10.1080/1554480X.2016.1182437
• 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
• Wang, D., Wang, T., & Liu, Z. (2014). A tangible programming tool for children to cultivate computational thinking. The Scientific World Journal, 2014, 1-10. http://dx.doi.org/10.1155/2014/428080
• Weber, R.P. (1988), Basic Content Analysis, Sage University Paper Series on Quantitative Applications in the Social Sciences, Series No. 07-049, Sage, Beverly Hills, CA, and London
• Weintrop, D. (2016). Modality matters: Understanding the effects of programming language representation in high school computer science classrooms (Unpublished Doctoral Dissertation), Northwestern University.
• Weintrop, D., Holbert, N., Horn, M. S., & Wilensky, U. (2016). Computational thinking in constructionist video games. International Journal of Game-Based Learning (IJGBL), 6(1), 1-17.
• Werner, L., Denner, J., & Campe, S. (2015). Children programming games: A strategy for measuring computational learning. ACM Transactions on Computing Education (TOCE), 14(4), 24. https://doi.org/10.1145/2677091
• Willis, J. W. (2008). Qualitative research methods in education and educational technology. USA: Age Publishing.
• Wilkerson-Jerde, M. H. (2014). Construction, categorization, and consensus: Student generated computational artifacts as a context for disciplinary reflection. Educational Technology Research and Development, 62(1), 99-121. https://doi.org/10.1007/s11423-013-9327-0
• Wing, J. M. (2006). Computational thinking. Communications of The ACM, 49(3), 33-35.
• Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of The Royal Society A: Mathematical, Physical And Engineering Sciences, 366(1881), 3717-3725. https://doi.org /10.1098/rsta.2008.0118
• Wing, J., Henderson, P., Hazzan, O., & Cortina, T. (2005). Computational thinking. Retrieved on April 21, 2016, from http://www.cs.cmu.edu/afs/cs/usr/wing/www/ct-paper.pdf.
• Xu, Z. W. & Tu, D. D. (2011). Three new concepts of future computer science. Journal of Computer Science and Technology, 26(4), 616-624. https://doi.org/10.1007/s11390-011-1161-4
• Yadav, A., Hong, H., & Stephenson, C. (2016). Computational thinking for all: Pedagogical approaches to embedding 21st century problem solving in k-12 classrooms. TechTrends, 60(6), 565-568. https://doi.org/10.1007/s11528-016-0087-7
• Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education (TOCE), 14(1), 5. https://doi.org/10.1145/2576872
• Zapata-Ros, M. (2015). Pensamientocomputacional: Unanuevaalfabetización digital. Revista de Educación a Distancia, (46).