Improvement of Pre-Service Teachers’ Computational Thinking Skills through an Educational Technology Course

Yıl 2021, , 97 - 112, 30.12.2021

Ebru Albayrak , Şule Yılmaz Ozden

https://doi.org/10.47156/jide.1027431

Cited By: 1

Öz

This study examines the improvement of pre-service teachers’ computational thinking skill levels through an educational technology course redesigned within the computational thinking context. 27 pre-service teachers from the Literacy Education Program enrolled in the Instructional Technologies and Material Development course in a public university in Turkey. Pre-service teachers engaged in some structured activities throughout the course and they were asked to complete a final project. Pre and post-survey results showed that pre-service teachers’ algorithmic thinking skills and computational thinking skills in general were improved after the course. Analysis of final projects also showed that pre-service teachers were able to use their problem solving, algorithmic thinking, and collaborative skills. However, they had difficulty in using their critical thinking skills and creativity. Findings have implications for the design of an educational technology course that pre-service teachers comprehend and practice computational thinking concepts.

Anahtar Kelimeler

Computational thinking, computational thinking skills, pre-service teachers, course design, educational technology course design

Kaynakça

• Aksoy, B. (2004). Problem based learning approach in geography teaching. (Doctoral Thesis). Ankara: Institute of Educational Sciences Gazi University in Turkey https://tez.yok.gov.tr/UlusalTezMerkezi/
• Angeli, C., & Giannakos, M. (2020). Computational thinking education: Issues and challenges. Computers in Human Behavior, 105, 106185.
• Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., & Zagami, J. (2016). A K-6 computational thinking curriculum framework: Implications for teacher knowledge. Journal of Educational Technology & Society, 19(3), 47-58.
• Balanskat, A., & Engelhardt, K. (2014). Computing our future: Computer programming and coding - Priorities, school curricula and initiatives across Europe. Brussels, Belgium: European Schoolnet. Retrieved from https://goo.gl/i5aQiv
• Barefoot, (2019). Classroom Resources. Retrieved from https://www.barefootcomputing.org/about-barefoot
• Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. Learning & Leading with Technology, 38(6), 20-23.
• 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. doi: 10.1145/1929887.1929905
• Bell, T., Alexander, J., Freeman, I., & Grimley, M. (2008, July). Computer science without computers: new outreach methods from old tricks. Paper presented at the 21st Annual Conference of the National Advisory Committee on Computing Qualifications, Auckland, New Zealand. Retrieved from https://www.citrenz.ac.nz/conferences/2008/127.pdf
• Binkley, M., Erstad, O., Herman, J., Raizen, S., Ripley, M., Miller-Ricci, M., & Rumble, M. (2012). Defining twenty-first century skills. In P. Griffin, B. McGaw, & E. Care (Eds.) Assessment and Teaching of 21st Century Skills (pp. 17–66). New York, NY: Springer.
• Bower, M., & Falkner, K. (2015, January). Computational thinking, the notional machine, pre-service teachers, and research opportunities, Paper presented at the 17th Australasian Computer Education Conference, Sydney. Retrieved from https://pdfs.semanticscholar.org/c2df/f4fdd833c44015fedff1e9ae480740894a7b.pdf
• Bower, M., Wood, L. N., Lai, J. W., Howe, C., Lister, R., Mason, R., Highfield, K., & Veal, J. (2017). Improving the Computational Thinking Pedagogical Capabilities of School Teachers. Australian Journal of Teacher Education, 42(3). doi: 10.14221/ajte.2017v42n3.4
• Brennan, K., & Resnick, M. (2012, April). New frameworks for studying and assessing the development of computational thinking. Paper presented at the 2012 annual meeting of the American Educational Research Association, Vancouver, Canada. Retrieved from https://web.media.mit.edu/~kbrennan/files/Brennan_Resnick_AERA2012_CT.pdf
• Brown, W. (2015). Introduction to algorithmic thinking. Retrieved from https://raptor.martincarlisle.com/Introduction%20to%20Algorithmic%20Thinking.doc
• Bundy, A. (2007). Computational thinking is pervasive. Journal of Scientific and Practical Computing, 1(2), 67-69. Retrieved from https://core.ac.uk/download/pdf/28961399.pdf
• Burton, B. A. (2010). Encouraging algorithmic thinking without a computer. Olympiads in Informatics, 4, 3-14.
• Cook, T. D. (2003). Why have educational evaluators chosen not to do randomized experiments?. The Annals of the American Academy of Political and Social Science, 589(1), 114-149.
• Creswell, J. W. (2009). Mapping the field of mixed methods research. Journal of Mixed Methods Research, 3(2), 95-108. doi: 10.1177/1558689808330883
• CSTA & ISTE (2011). Operational definition of computational thinking for K-12 education. Retrieved from https://id.iste.org/docs/ct-documents/computational-thinking-operational-definition-flyer.pdf
• Denning, P. J. (June 2009). The profession of IT. Beyond computational thinking. Communications of the ACM, (52)6, 28–30. Retrieved from https://sgd.cs.colorado.edu/wiki/images/7/71/Denning.pdf
• Denning, P. J., & Tedre, M. (2019). Computational thinking. Cambridge, MA: MIT Press.
• Doleck, T., Bazelais, P., Lemay, D. J., Saxena, A., & Basnet, R. B. (2017). Algorithmic thinking, cooperativity, creativity, critical thinking, and problem solving: Exploring the relationship between computational thinking skills and academic performance. Journal of Computers in Education, 4(4), 355-369. doi: 10.1007/s40692-017-0090-9
• Doppelt, Y. (2003). Implementation and assessment of project-based learning in a flexible environment. International journal of technology and design education, 13(3), 255-272.
• Ehrenberg, R. G., Brewer, D. J., Gamoran, A., & Willms, J. D. (2001). Does class size matter?. Scientific American, 285(5), 78-85.
• Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education. (8th ed.). New York: McGraw-Hill Education.
• Futschek, G. (2006). Algorithmic thinking: the key for understanding computer science. Algorithmic thinking: the key for understanding computer science. Lecture Notes in Computer Science (pp. 159-168). doi: 10.1007/11915355_15
• Gretter, S., & Yadav, A. (2016). Computational thinking and media & information literacy: An integrated approach to teaching twenty-first century skills. TechTrends, 60(5), 510-516. doi: 10.1007/s11528-016-0098-4
• Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38-43. doi: 10.3102/0013189X12463051
• Gross, S., Kim, M., Schlosser, J., Mohtadi, C., Lluch, D., & Schneider, D. (2014, April). Fostering computational thinking in engineering education: Challenges, examples, and best practices. In IEEE Global Engineering Education Conference (EDUCON) (pp. 450-459). Istanbul, Turkey.
• Hodgson, T., & Riley, K. J. (2001). Real-World Problems as Contexts for Proof. Mathematics Teacher, 94(9), 724-728. Retrieved from https://search.proquest.com/openview/5df029d3c44a057002183041da9ba239/1?pqorigsite=gscholar&cbl=41299
• Hsu, T. C., Chang, S. C., & Hung, Y. T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296-310. doi: 10.1016/j.compedu.2018.07.004
• ISTE (2015). CT Leadership Toolkit. Retrieved from https://id.iste.org/docs/ct-documents/ct-leadershipt-toolkit.pdf?sfvrsn=4
• Johanson, G. A., & Brooks, G. P. (2010). Initial scale development: sample size for pilot studies. Educational and psychological measurement, 70(3), 394-400
• Jones, B. F., Rasmussen, C. M., & Moffitt, M. C. (1997). Real-life problem solving: A collaborative approach to interdisciplinary learning. Washington DC: American Psychological Association. doi:10.1037/10266-000
• Jumaat, N.F., Tasir, Z., Halim, N.D.A. & Ashari, Z.M. (2017). Project-based learning from constructivism point of view. Advanced Science Letters, 23 (8), 7904-7906.
• 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.
• Knuth, D.E. (1985). Algorithmic thinking and mathematical thinking, The American Mathematical Monthly, 92(3), 170-181, doi: 10.1080/00029890.1985.11971572
• Korkmaz, O., Cakir, R., & Ozden, M.Y. (2017). A validity and reliability study of the Computational Thinking Scales (CTS). Computers in Human Behavior, 72, 558-569. doi: 10.1016/j.chb.2017.01.005
• Kotsopoulos, D., Floyd, L., Khan, S., Namukasa, I. K., Somanath, S., Weber, J., & Yiu, C. (2017). A pedagogical framework for computational thinking. Digital Experiences in Mathematics Education, 3(2), 154-171. doi: 10.1007/s40751-017-0031-2
• Lou, Y., Abrami, P. C., & d’Apollonia, S. (2001). Small group and individual learning with technology: A meta-analysis. Review of educational research, 71(3), 449-521.
• Marquez Lepe, E., Jimenez-Rodrigo, M.L. (2014). Project-based learning in virtual environments: a case study of a university teaching experience. International Journal of Educational Technology in Higher Education 11(1), 76–90. doi: 10.7238/rusc.v11i1.1762
• McKenney, S., Kali, Y., Markauskaite, L., & Voogt, J. (2015). Teacher design knowledge for technology enhanced learning: an ecological framework for investigating assets and needs. Instructional Science, 43(2), 181-202. doi: 10.1007/s11251-014-9337-2
• Meerbaum-Salant, O., Armoni, M., & Ben-Ari, M. (2013). Learning computer science concepts with scratch. Computer Science Education, 23(3), 239-264. doi: 10.1007/s11251-014-9337-2
• Merriam, S. B. (2015). Qualitative research: Designing, implementing, and publishing a study. In Victor X. Wang (Ed.), Handbook of Research on Scholarly Publishing and Research Methods (pp. 125-140). Hershey, PA: IGI Global.
• Merriam, S. B., & Grenier, R. S. (Eds.). (2019). Assessing and evaluating qualitative research. Qualitative Research in Practice: Examples for Discussion and Analysis (pp. 19-32). USA: Jossey-Bass.
• Missiroli, M., Russo, D., & Ciancarini, P. (2017, November). Cooperative Thinking, or: Computational Thinking meets Agile. In Proceedings of the 30th Conference on Software Engineering Education and Training (CSEE&T) (pp. 187-191). Savannah, USA.
• Mumcu, H. Y., & Yildiz, S. (2018). The Investigation of Algorithmic Thinking Skills of 5th and 6th Graders at a Theoretical Dimension. MATDER Journal of Mathematics Education, (1), 41-48.
• Nishida, T., Kanemune, S., Idosaka, Y., Namiki, M., Bell, T., & Kuno, Y. (2009). A CS unplugged design pattern. ACM SIGCSE Bulletin, 41(1), 231-235. doi: 10.1145/1539024.1508951
• Pala, F. K., & Mihci Turker, P. (2019). The effects of different programming trainings on the computational thinking skills. Interactive Learning Environments, 1-11. doi: 10.1080/10494820.2019.1635495
• Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice (4th ed.). Thousand Oaks, CA: Sage.
• Saban, A., & Saban, A. I. (2017). Examination of Science Perceptions of Teacher Candidates. In I. Koleva, & G. Duman (Eds.), Educational Research and Practice, (pp. 214-224). Sofia: St. Kliment Ohridski University Press.
• Sahiner, A., & Kert, S. B. (2016). Examining studies related with the concept of computational thinking between the years of 2006-2015. EJOSAT: European Journal of Science and Technology, 5(9). Retrieved from http://dergipark.gov.tr/download/issue-full-file/30420
• Sarıtepeci, M., & Durak, H. (2017). Analyzing the effect of block and robotic coding activities on computational thinking in programming education. In I. Koleva, & G. Duman (Eds.), Educational Research and Practice, (pp. 490-501). Sofia: St. Kliment Ohridski University Press.
• Springate, S. D. (2012). The effect of sample size and bias on the reliability of estimates of error: a comparative study of Dahlberg's formula. The European Journal of Orthodontics, 34(2), 158-163.
• Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers & Education, 148, 103798.
• Thomas, J. W. (2000). A review of research on project-based learning, San Rafael, CA: Autodesk Foundation.
• 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. doi: 10.1007/s10639-015-9412-6
• Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. doi: 10.1145/1118178.1118215
• 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.
• Wing, J. (2011). Research notebook: Computational thinking—What and why?. The Link Magazine, Spring. Carnegie Mellon University, Pittsburgh. Retrieved from http://link.cs.cmu.edu/article.php?a=600
• Wing, J. M. (2014). Computational thinking benefits society. 40th Anniversary Blog of Social Issues in Computing, 2014. Retrieved from http://socialissues.cs.toronto.edu/index.html%3Fp=279.html
• 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, 14(1). doi: 10.1145/2576872
• Yadav, A., Stephenson, C., & Hong, H. (2017). Computational thinking for teacher education. Communications of the ACM, 80(4), 55-62. doi: 10.1145/2994591
• Yadav, A., Zhou, N., Mayfield, C., Hambrusch, S., & Korb, J. T. (2011, March). Introducing computational thinking in education courses. In Proceedings of the 42nd ACM technical symposium on Computer science education (pp. 465-470). ACM. doi: 10.1145/1953163.1953297