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A Qualitative Case Study: Pre-service Teachers as Novice Programmers

Year 2024, Volume: 12 Issue: 23, 292 - 318, 21.03.2024
https://doi.org/10.18009/jcer.1435182

Abstract

To meet the needs of 21st-century learners in today’s classrooms, it is needed that teachers be familiar with programming and computational thinking. Particularly, subject-area pre-service teachers should be exposed to programming instruction in their teacher education programs. This case study including three participants aims to explore the process of pre-service teachers’ learning of programming while completing CT-oriented tasks through observations and interviews in a 14-week educational technology course at a public university in Turkey. The findings demonstrate that pre-service teachers, being novice programmers, prefer contextualized, structured and visually well-designed programming tasks. They use various strategies to face challenges, and the effort they put into dealing with these challenges enables them to produce higher-quality programs. Accordingly, implications for further research are also discussed in this study.

Ethical Statement

Name of the board that carries out ethical assessment: Boğaziçi University Social and Humanities Scientific Research and Publication Ethics Board The date and number of the ethical assessment decision: 24.01.2023-109746

References

  • Albayrak, E., & Ozden, Ş. Y. (2021). Improvement of pre-service teachers’ computational thinking skills through an educational technology course. Journal of Individual Differences in Education, 3(2), 97-112.
  • Bal, I. A., Alvarado–Albertorio, F., Marcelle, P., & Oaks–Garcia, C. T. (2022). Pre–service teachers computational thinking (CT) and pedagogical growth in a micro–credential: A mixed methods study. TechTrends, 66(3), 468-482.
  • Bers, M. U. (2019). Coding as another language: A pedagogical approach for teaching computer science in early childhood. Journal of Computers in Education, 6(4), 499–528.
  • Brennan, K., & Resnick, M. (2012, April). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 Annual Meeting of the American Educational Research Association, Vancouver, Canada (Vol. 1, p. 25).
  • Buitrago Flórez, F., Casallas, R., Hernández, M., Reyes, A., Restrepo, S., & Danies, G. (2017). Changing a generation’s way of thinking: Teaching computational thinking through programming. Review of Educational Research, 87(4), 834-860.
  • Butler, D., & Leahy, M. (2021). Developing preservice teachers' understanding of computational thinking: A constructionist approach. British Journal of Educational Technology, 52(3), 1060-1077.
  • Cooper, J.E., Brandon, P.R., & Lindberg, M.A. (1998). Evaluators’ use of peer debriefing: Three impressionist tales. Qualitative Inquiry, 4(2), 265-279.
  • Creative Computing Lab (2021). Getting unstuck. Harvard Graduate School of Education. Retrieved from https://gettingunstuck.gse.harvard.edu/
  • Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches (3rd ed.). SAGE Publications.
  • Cutumisu, M., Adams, C., Glanfield, F., Yuen, C., & Lu, C. (2021). Using Structural Equation Modeling to Examine the Relationship Between Preservice Teachers’ Computational Thinking Attitudes and Skills. IEEE Transactions on Education, 65(2), 177-183.
  • Çiftçi, A., & Topçu, M. S. (2022). Improving early childhood pre-service teachers’ computational thinking teaching self-efficacy beliefs in a STEM course. Research in Science & Technological Education, 41(4), 1215-1241.
  • Çoklar, A., N. & Akçay, A. (2018). Evaluating programming self-efficacy in the context of inquiry skills and problem-solving skills: A perspective from teacher education. World Journal on Educational Technology: Current Issues. 10(3), 153-164.
  • Denning, P. J. (2017). Remaining trouble spots with computational thinking. Communications of the ACM, 60(6), 33-39.
  • Dong, Y., Marwan, S., Catete, V., Price, T., & Barnes, T. (2019, February). Defining tinkering behavior in open-ended block-based programming assignments. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 1204-1210).
  • Fagerlund, J., Häkkinen, P., Vesisenaho, M., & Viiri, J. (2021). Computational thinking in programming with Scratch in primary schools: A systematic review. Computer Applications in Engineering Education, 29(1), 12-28.
  • Flyvbjerg, B. (2011). Case study. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (1st ed., p. 301-316). Sage Publications.
  • Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38-43.
  • Hanbay-Tiryaki, S., & Balaman, F. (2021). Açık kaynak kodlu yazılımlardan scratch, arduino ve python kullanımı hakkında öğrenci görüşleri. Journal of Computer and Education Research, 9(18), 831-852. https://doi.org/10.18009/jcer.938706
  • Ilic, U. (2021). The impact of scratch-assisted instruction on computational thinking (ct) skills of pre-service teachers. International Journal of Research in Education and Science, 7(2), 426-444.
  • Instefjord, E. J., & Munthe, E. (2017). Educating digitally competent teachers: A study of integration of professional digital competence in teacher education. Teaching and Teacher Education, 67, 37-45.
  • ISTE (2016). ISTE standards for students. Retrieved from https://www.iste.org/standards/standards/for-students-2016
  • Jin, H. Y., & Cutumisu, M. (2023). Predicting pre-service teachers’ computational thinking skills using machine learning classifiers. Education and Information Technologies, 28, 11447–11467.
  • Kafai, Y., & Burke, Q. (2013). Computer programming goes back to school. Phi Delta Kappan, 95(1), 61–65. https://doi.org/10.1177/003172171309500111
  • Kapur, M. (2015). Learning from productive failure. Learning: Research and Practice, 1(1), 51–65.
  • Kaya, E., Yesilyurt, E., Newley, A., & Deniz, H. (2019). Examining the impact of a computational thinking intervention on pre-service elementary science teachers’ computational thinking teaching efficacy beliefs, interest and confidence. Journal of Computers in Mathematics and Science Teaching, 38(4), 385-392.
  • Kazimoglu, C., Kiernan, M., Bacon, L., & MacKinnon, L. (2012). Learning programming at the computational thinking level via digital game-play. Procedia Computer Science, 9, 522-531.
  • Klahr, D., & Nigam, M. (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning. Psychological Science, 15(10), 661–667.
  • Lee, D. M. C., Rodrigo, M. M. T., Baker, R. S. D., Sugay, J. O., & Coronel, A. (2011). Exploring the relationship between novice programmer confusion and achievement. In Affective Computing and Intelligent Interaction: 4th International Conference, ACII 2011, Memphis, TN, USA, October 9–12, Proceedings, Part I 4 (pp. 175-184). Springer Berlin Heidelberg.
  • Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage Publications.
  • Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12?. Computers in Human Behavior,41, 51-61.
  • Maloney, J., Burd, L., Kafai, Y., Rusk, N., Silverman, B., & Resnick, M. (2004). Scratch: A sneak preview [Conference proceeding]. In Proceedings of Second International Conference on Creating, Connecting and Collaborating through Computing. (pp. 104-109). IEEE.
  • Merriam, S. B. (2009). Qualitative research: A guide to design and implementation. Jossey-Bass.
  • Miyake, N., & Kirschner, P. A. (2014). The social and interactive dimensions of collaborative learning. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 418-438). Cambridge.
  • Mouza, C., Yang, H., Pan, Y. C., Ozden, S. Y., & Pollock, L. (2017). Resetting educational technology coursework for pre-service teachers: A computational thinking approach to the development of technological pedagogical content knowledge (TPACK). Australasian Journal of Educational Technology, 33(3), 61-76.
  • Mugayitoglu, B. (2016). Attitudes of pre-service teachers toward computational thinking in education. [Doctoral Dissertation, Duquesne University].
  • OpenAI. (2022). Whisper [Automatic speech recognition system]. https://openai.com/research/whisper
  • Piedade, J., Dorotea, N., Pedro, A., & Matos, J. F. (2020). On teaching programming fundamentals and computational thinking with educational robotics: A didactic experience with pre-service teachers. Education Sciences, 10(9), 214.
  • Qualls, J. A., & Sherrell, L. B. (2010). Why computational thinking should be integrated into the curriculum. Journal of Computing Sciences in Colleges, 25(5), 66-71.
  • Resnick, M., & Rosenbaum, E. (2013). Designing for tinkerability. In M. Honey (Ed.), Design, make, play: Growing the next generation of STEM innovators (pp. 163-181). Routledge.
  • Saldana, J. (2013). The coding manual for qualitative researchers (2nd ed.). Sage.
  • Umutlu, D. (2022). An exploratory study of pre-service teachers’ computational thinking and programming skills. Journal of Research on Technology in Education, 54(5), 754-768.
  • VERBI Software. (2022). MAXQDA [Computer software]. https://www.maxqda.com/
  • Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
  • Wakil, K., Khdir, S., Sabir, L., & Nawzad, L. (2019). Student ability for learning computer programming languages in primary schools. International e-Journal of Educational Studies, 3(6), 109-115.
  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
  • 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), 1-16.
  • Yin, R. K. (2009). Case study research: Design and methods (5th ed.). SAGE Publications.

A Qualitative Case Study: Pre-service Teachers as Novice Programmers

Year 2024, Volume: 12 Issue: 23, 292 - 318, 21.03.2024
https://doi.org/10.18009/jcer.1435182

Abstract

To meet the needs of 21st-century learners in today’s classrooms, it is needed that teachers be familiar with programming and computational thinking. Particularly, subject-area pre-service teachers should be exposed to programming instruction in their teacher education programs. This case study including three participants aims to explore the process of pre-service teachers’ learning of programming while completing CT-oriented tasks through observations and interviews in a 14-week educational technology course at a public university in Turkey. The findings demonstrate that pre-service teachers, being novice programmers, prefer contextualized, structured and visually well-designed programming tasks. They use various strategies to face challenges, and the effort they put into dealing with these challenges enables them to produce higher-quality programs. Accordingly, implications for further research are also discussed in this study.

References

  • Albayrak, E., & Ozden, Ş. Y. (2021). Improvement of pre-service teachers’ computational thinking skills through an educational technology course. Journal of Individual Differences in Education, 3(2), 97-112.
  • Bal, I. A., Alvarado–Albertorio, F., Marcelle, P., & Oaks–Garcia, C. T. (2022). Pre–service teachers computational thinking (CT) and pedagogical growth in a micro–credential: A mixed methods study. TechTrends, 66(3), 468-482.
  • Bers, M. U. (2019). Coding as another language: A pedagogical approach for teaching computer science in early childhood. Journal of Computers in Education, 6(4), 499–528.
  • Brennan, K., & Resnick, M. (2012, April). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 Annual Meeting of the American Educational Research Association, Vancouver, Canada (Vol. 1, p. 25).
  • Buitrago Flórez, F., Casallas, R., Hernández, M., Reyes, A., Restrepo, S., & Danies, G. (2017). Changing a generation’s way of thinking: Teaching computational thinking through programming. Review of Educational Research, 87(4), 834-860.
  • Butler, D., & Leahy, M. (2021). Developing preservice teachers' understanding of computational thinking: A constructionist approach. British Journal of Educational Technology, 52(3), 1060-1077.
  • Cooper, J.E., Brandon, P.R., & Lindberg, M.A. (1998). Evaluators’ use of peer debriefing: Three impressionist tales. Qualitative Inquiry, 4(2), 265-279.
  • Creative Computing Lab (2021). Getting unstuck. Harvard Graduate School of Education. Retrieved from https://gettingunstuck.gse.harvard.edu/
  • Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches (3rd ed.). SAGE Publications.
  • Cutumisu, M., Adams, C., Glanfield, F., Yuen, C., & Lu, C. (2021). Using Structural Equation Modeling to Examine the Relationship Between Preservice Teachers’ Computational Thinking Attitudes and Skills. IEEE Transactions on Education, 65(2), 177-183.
  • Çiftçi, A., & Topçu, M. S. (2022). Improving early childhood pre-service teachers’ computational thinking teaching self-efficacy beliefs in a STEM course. Research in Science & Technological Education, 41(4), 1215-1241.
  • Çoklar, A., N. & Akçay, A. (2018). Evaluating programming self-efficacy in the context of inquiry skills and problem-solving skills: A perspective from teacher education. World Journal on Educational Technology: Current Issues. 10(3), 153-164.
  • Denning, P. J. (2017). Remaining trouble spots with computational thinking. Communications of the ACM, 60(6), 33-39.
  • Dong, Y., Marwan, S., Catete, V., Price, T., & Barnes, T. (2019, February). Defining tinkering behavior in open-ended block-based programming assignments. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 1204-1210).
  • Fagerlund, J., Häkkinen, P., Vesisenaho, M., & Viiri, J. (2021). Computational thinking in programming with Scratch in primary schools: A systematic review. Computer Applications in Engineering Education, 29(1), 12-28.
  • Flyvbjerg, B. (2011). Case study. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (1st ed., p. 301-316). Sage Publications.
  • Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38-43.
  • Hanbay-Tiryaki, S., & Balaman, F. (2021). Açık kaynak kodlu yazılımlardan scratch, arduino ve python kullanımı hakkında öğrenci görüşleri. Journal of Computer and Education Research, 9(18), 831-852. https://doi.org/10.18009/jcer.938706
  • Ilic, U. (2021). The impact of scratch-assisted instruction on computational thinking (ct) skills of pre-service teachers. International Journal of Research in Education and Science, 7(2), 426-444.
  • Instefjord, E. J., & Munthe, E. (2017). Educating digitally competent teachers: A study of integration of professional digital competence in teacher education. Teaching and Teacher Education, 67, 37-45.
  • ISTE (2016). ISTE standards for students. Retrieved from https://www.iste.org/standards/standards/for-students-2016
  • Jin, H. Y., & Cutumisu, M. (2023). Predicting pre-service teachers’ computational thinking skills using machine learning classifiers. Education and Information Technologies, 28, 11447–11467.
  • Kafai, Y., & Burke, Q. (2013). Computer programming goes back to school. Phi Delta Kappan, 95(1), 61–65. https://doi.org/10.1177/003172171309500111
  • Kapur, M. (2015). Learning from productive failure. Learning: Research and Practice, 1(1), 51–65.
  • Kaya, E., Yesilyurt, E., Newley, A., & Deniz, H. (2019). Examining the impact of a computational thinking intervention on pre-service elementary science teachers’ computational thinking teaching efficacy beliefs, interest and confidence. Journal of Computers in Mathematics and Science Teaching, 38(4), 385-392.
  • Kazimoglu, C., Kiernan, M., Bacon, L., & MacKinnon, L. (2012). Learning programming at the computational thinking level via digital game-play. Procedia Computer Science, 9, 522-531.
  • Klahr, D., & Nigam, M. (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning. Psychological Science, 15(10), 661–667.
  • Lee, D. M. C., Rodrigo, M. M. T., Baker, R. S. D., Sugay, J. O., & Coronel, A. (2011). Exploring the relationship between novice programmer confusion and achievement. In Affective Computing and Intelligent Interaction: 4th International Conference, ACII 2011, Memphis, TN, USA, October 9–12, Proceedings, Part I 4 (pp. 175-184). Springer Berlin Heidelberg.
  • Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage Publications.
  • Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12?. Computers in Human Behavior,41, 51-61.
  • Maloney, J., Burd, L., Kafai, Y., Rusk, N., Silverman, B., & Resnick, M. (2004). Scratch: A sneak preview [Conference proceeding]. In Proceedings of Second International Conference on Creating, Connecting and Collaborating through Computing. (pp. 104-109). IEEE.
  • Merriam, S. B. (2009). Qualitative research: A guide to design and implementation. Jossey-Bass.
  • Miyake, N., & Kirschner, P. A. (2014). The social and interactive dimensions of collaborative learning. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 418-438). Cambridge.
  • Mouza, C., Yang, H., Pan, Y. C., Ozden, S. Y., & Pollock, L. (2017). Resetting educational technology coursework for pre-service teachers: A computational thinking approach to the development of technological pedagogical content knowledge (TPACK). Australasian Journal of Educational Technology, 33(3), 61-76.
  • Mugayitoglu, B. (2016). Attitudes of pre-service teachers toward computational thinking in education. [Doctoral Dissertation, Duquesne University].
  • OpenAI. (2022). Whisper [Automatic speech recognition system]. https://openai.com/research/whisper
  • Piedade, J., Dorotea, N., Pedro, A., & Matos, J. F. (2020). On teaching programming fundamentals and computational thinking with educational robotics: A didactic experience with pre-service teachers. Education Sciences, 10(9), 214.
  • Qualls, J. A., & Sherrell, L. B. (2010). Why computational thinking should be integrated into the curriculum. Journal of Computing Sciences in Colleges, 25(5), 66-71.
  • Resnick, M., & Rosenbaum, E. (2013). Designing for tinkerability. In M. Honey (Ed.), Design, make, play: Growing the next generation of STEM innovators (pp. 163-181). Routledge.
  • Saldana, J. (2013). The coding manual for qualitative researchers (2nd ed.). Sage.
  • Umutlu, D. (2022). An exploratory study of pre-service teachers’ computational thinking and programming skills. Journal of Research on Technology in Education, 54(5), 754-768.
  • VERBI Software. (2022). MAXQDA [Computer software]. https://www.maxqda.com/
  • Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
  • Wakil, K., Khdir, S., Sabir, L., & Nawzad, L. (2019). Student ability for learning computer programming languages in primary schools. International e-Journal of Educational Studies, 3(6), 109-115.
  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
  • 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), 1-16.
  • Yin, R. K. (2009). Case study research: Design and methods (5th ed.). SAGE Publications.
There are 48 citations in total.

Details

Primary Language English
Subjects Instructional Technologies
Journal Section Research Article
Authors

Burcu Şener 0000-0002-0487-6849

Duygu Umutlu 0000-0002-2030-2626

Early Pub Date March 20, 2024
Publication Date March 21, 2024
Submission Date February 12, 2024
Acceptance Date March 19, 2024
Published in Issue Year 2024 Volume: 12 Issue: 23

Cite

APA Şener, B., & Umutlu, D. (2024). A Qualitative Case Study: Pre-service Teachers as Novice Programmers. Journal of Computer and Education Research, 12(23), 292-318. https://doi.org/10.18009/jcer.1435182

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