Abstract
There has been a growing interest to integrate “coding education” into K-12 curriculum in recent years. Teachers play a major role in this integration process, and in order for it to be successful, they should have strong sense of efficacy. Hence, the primary purpose of this chapter was to examine the self-efficacy skills that teachers should possess for effective coding education. In addition, teachers’ opinions about the benefits and potential barriers of coding education were investigated. Convergent parallel mixed-method design was employed to address research questions. Participants of the study consisted of two independent groups of samples with 15 and 272 participants, respectively. Both qualitative and quantitative data were collected concurrently through a series of semi-structured interviews and the application of an online survey form. Results revealed six main self-efficacy skills themes: content knowledge, personal characteristics, motivating students, pedagogical knowledge, classroom management, and material development. Furthermore, findings suggested that the most significant challenges experienced during coding education were infrastructure-related problems, lack of resources, and inadequate teacher skills. The present findings have important implications for researchers, practitioners, and policy makers to deliver effective and efficient coding education.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akpinar, Y., & Altun, A. (2014). Bilgi Toplumu Okullarında Programlama Eğitimi Gereksinimi. Elementary Education Online, 13(1), 1–4.
Askar, P., & Davenport, D. (2009). An investigation of factors related to self-efficacy for JAVA programming among engineering students. The Turkish Online Journal of Educational Technology, 8(1), 26–32.
Atmatzidou, S., & Demetriadis, S. (2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems, 75, 661–670. Retrieved from https://doi.org/10.1016/j.robot.2015.10.008
Balanskat, A., & Engelhardt, K. (2015). Computing our future. Brussels, Belgium: European Schoolnet. Retrieved from http://www.eun.org/c/document_library/get_file?uuid=3596b121-941c-4296-a760-0f4e4795d6fa&groupId=43887
Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W.H. Freeman and Company.
Bandura, A. (2006). Guide for constructing self-efficacy scales. In F. Pajares & T. Urdan (Eds.), Self-efficacy beliefs of adolescents (pp. 307–337). Greenwich, CT: Information Age Publishing.
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. Retrieved from https://doi.org/10.1145/1929887.1929905
Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers and Education, 72, 145–157. Retrieved from https://doi.org/10.1016/j.compedu.2013.10.020
Bower, M., & Falkner, K. (2015). Computational thinking, the notional machine, pre-service teachers, and research opportunities. In Proceedings of the 17th Australasian Computing Education Conference (ACE 2015) (pp. 37–46). Sydney, Australia. Retrieved from http://crpit.com/confpapers/CRPITV160Bower.pdf
Brown, N. C. C., Sentance, S., Crick, T., & Humphreys, S. (2013). Restart: The resurgence of computer science in UK schools. ACM Transactions on Computing Education, 1(1), 1–22. Retrieved from https://doi.org/10.1145/0000000.0000000
Buss, A., & Gamboa, R. (2017). Teacher transformations in developing computational thinking: Gaming and robotics use in after-school settings. In P. J. Rich & C. B. Hodges (Eds.), Emerging research, practice, and policy on computational thinking (pp. 189–203). Cham, Switzeland: Springer. Retrieved from https://doi.org/10.1007/978-3-319-52691-1_12
Calao, L. A., Moreno-León, J., Correa, H. E., & Robles, G. (2015). Design for teaching and learning in a networked world. In Developing mathematical thinking with Scratch an experiment with 6th grade students (Vol. 9307, pp. 17–27). Springer. Retrieved from https://doi.org/10.1007/978-3-319-24258-3_2
Chan, D. W. (2005). Teacher self-efficacy research and teacher education. Educational Research Journal, 20(2).
Code.org. (2016). Teach with Code Studio. Retrieved May 1, 2017, from https://studio.code.org/courses?view=teacher
Code.org. (2017). Promote Computer Science. Retrieved September 1, 2017, from https://code.org/promote
Creswell, J. W., & Plano Clark, V. L. (2007). Designing and conducting mixed methods research. Thousand Oaks, CA: Sage.
Creswell, J. W., Plano Clark, V. L., Gutmann, M. L., & Hanson, W. E. (2002). Advanced mixed methods research designs. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social & behavioral research (pp. 209–240). Thousand Oaks, CA: Sage.
D’Alba, A., & Huett, K. C. (2017). Learning computational skills in uCode@UWG: Challenges and recommendations. In P. J. Rich & C. B. Hodges (Eds.), Emerging research, practice, and policy on computational thinking (pp. 3–20). Cham, Switzeland: Springer. Retrieved from https://doi.org/10.1007/978-3-319-52691-1_1
Dejarnette, N. K. (2012). America’s children: Providing early exposure to STEM (Science, Technology, Engineering, & Math) initiatives. Education, 133(1), 77–84.
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.
Duncan, C., Bell, T., & Tanimoto, S. (2014). Should your 8-year-old learn coding? Proceedings of the 9th Workshop in Primary and Secondary Computing Education on – WiPSCE ’14, pp. 60–69. Retrieved from https://doi.org/10.1145/2670757.2670774
Ertmer, P. A. (2005). Teacher pedagogical beliefs: The final frontier in our quest for technology integration? Educational Technology Research and Development, 53(4), 25–39. Retrieved from https://doi.org/10.1007/BF02504683
Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). New York: McGraw-Hill.
Gal-Ezer, J., & Stephenson, C. (2014). A tale of two countries. ACM Transactions on Computing Education, 14(2). Retrieved from https://doi.org/10.1145/2602483
Ghaith, G., & Yaghi, H. (1997). Relationships among experience, teacher efficacy, and attitudes toward the implementation of instructional innovation. Teaching and Teacher Education, 13(4), 451–458. Retrieved from https://doi.org/10.1016/S0742-051X(96)00045-5
Girvan, C., Tangney, B., & Savage, T. (2013). SLurtles: Supporting constructionist learning in second life. Computers & Education, 61(1), 115–132. Retrieved from https://doi.org/10.1016/j.compedu.2012.08.005
Google. (2016). Computational thinking for educators. Retrieved from https://computationalthinkingcourse.withgoogle.com
Greene, J. C., Caracelli, V. J., & Graham, W. F. (1989). Toward a conceptual framework for mixed-method evaluation designs. Educational Evaluation and Policy Analysis, 11(3), 255–274. Retrieved from https://doi.org/10.3102/01623737011003255
Grover, S., & Pea, R. (2013). Computational thinking in K-12: A review of the state of the field. Educational Researcher, 42(1), 38–43. Retrieved from https://doi.org/10.3102/0013189X12463051
Howland, K., & Good, J. (2015). Learning to communicate computationally with flip: A bi-modal programming language for game creation. Computers & Education, 80, 224–240. Retrieved from https://doi.org/10.1016/j.compedu.2014.08.014
Jick, T. D. (1979). Mixing qualitative and quantitative methods: Triangulation in action. Qualitative Methodology, 24(4), 602–611. Retrieved from https://doi.org/10.2307/2392366
Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14–26. Retrieved from https://doi.org/10.3102/0013189X033007014
Kalelioǧlu, F., & Gülbahar, Y. (2014). The effects of teaching programming via Scratch on problem solving skills: A discussion from learners’ perspective. Informatics in Education, 13(1), 33–50.
Koh, K. H. K., Repenning, A., Nickerson, H., Endo, Y., & Motter, P. (2013). Will it stick?: Exploring the sustainability of computational thinking education through game design. In SIGCSE 2013 (p. 597). Denver, CO. Retrieved from https://doi.org/10.1145/2445196.2445372
Kordaki, M. (2013). High school computing teachers’ beliefs and practices: A case study. Computers & Education, 68, 141–152. Retrieved from https://doi.org/10.1016/j.compedu.2013.04.020
Lee, I., Martin, F., & Apone, K. (2014). Integrating computational thinking across the K-8 curriculum. ACM Inroads, 5(4), 64–71. Retrieved from https://doi.org/10.1145/2684721.2684736
Lee, M. (2017). Computational thinking: Efforts in Korea. In P. J. Rich & C. B. Hodges (Eds.), Emerging research, practice, and policy on computational thinking, practice, and policy on computational thinking (pp. 363–366). Cham, Switzeland: Springer. Retrieved from https://doi.org/10.1007/978-3-319-52691-1_22
Lu, J. J., & Fletcher, G. H. L. (2009). Thinking about computational thinking. In SIGCSE 2009 (pp. 260–264). Chattanooga, TN. Retrieved from https://doi.org/10.1145/1539024.1508959
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. Retrieved from https://doi.org/10.1016/j.chb.2014.09.012
Menekse, M. (2015). Computer science teacher professional development in the United States: A review of studies published between 2004 and 2014. Computer Science Education, 25(4), 325–350. Retrieved from https://doi.org/10.1080/08993408.2015.1111645
Millî Eğitim Bakanliği. (2016). Bilgisayar bilimi dersi öğretim programı.
Millî Eğitim Bakanliği. (2017). Bilisim teknolojileri ve yazilim dersi ogretim programi. Retrieved from http://mufredat.meb.gov.tr/Dosyalar/2017717142646998-14BİLİŞİMTEKNOLOJİLERİ 5-6.pdf
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. Retrieved from https://doi.org/10.6018/red/46/10
Mueller, J., Beckett, D., Hennessey, E., & Shodiev, H. (2017). Assessing computational thinking across the curriculum. In P. J. Rich & C. B. Hodges (Eds.), Emerging research, practice, and policy on computational thinking (pp. 251–267). Cham, Switzeland: Springer. Retrieved from https://doi.org/10.1007/978-3-319-52691-1_16
Nickerson, H., Brand, C., & Repenning, A. (2015). Grounding computational thinking skill acquisition through contextualized instruction. In ICER 2015 (pp. 207–216). Retrieved from https://doi.org/10.1145/2787622.2787720
Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books.
Prensky, M. (2001). Digital natives, digital immigrants part 2: Do they really think differently? On the Horizon, 9(6), 1–6. Retrieved from https://doi.org/10.1108/10748120110424843
Prieto-Rodriguez, E., & Berretta, R. (2014). Digtial technology teachers’ perceptions of Computer Science: It is no all about programming. In IEEE 2014 (pp. 1–5). Retrieved from https://doi.org/10.1109/FIE.2014.7044134
Repenning, A., Grover, R., Gutierrez, K., Repenning, N., Webb, D. C., Koh, K. H., et al. (2015). Scalable game design. ACM Transactions on Computing Education, 15(2), 1–31. Retrieved from https://doi.org/10.1145/2700517
Resnick, M. (2012). Reviving Papert’ s dream. Educational Technology, 52(4), 42–46.
Resnick, M., Maloney, J., Hernández, A. M., Rusk, N., Eastmond, E., Brennan, K., et al. (2009). Scratch: Programming for everyone. Communications of the ACM, 52, 60–67. Retrieved from http://web.media.mit.edu/~mres/scratch/scratch-cacm.pdf
Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., et al. (2009). Scratch: Programming for all. Communications of the ACM, 52(11), 60–67. Retrieved from https://doi.org/10.1145/1592761.1592779
Sanford, J. F., & Naidu, J. T. (2017). Mathematical modeling and computational thinking. Contemporary Issues in Education Research, 10(2), 159–168.
Schulte, C., Hornung, M., Sentence, S., Dagiene, V., Jevsikova, T., Thota, N., et al. (2012). Computer science at school/CS teacher education. In Koli Calling 2012 (pp. 29–38). Tahko, Finland. Retrieved from https://doi.org/10.1145/2401796.2401800
Stephenson, C., Gal-ezer, J., Haberman, B., Verno, A., & Cutler, R. (2005). The new educational imperative: Improving high school computer science education. New York: CSTA.
Teddlie, C., & Tashakkori, A. (2009). Foundations of mixed methods research: Integrating quantitative and qualitative approaches in the social and behavioral sciences. Los Angeles: Sage.
The Royal Society. (2012). Shut down or restart? The way forward for computing in. London: UK Schools. Retrieved from https://royalsociety.org/~/media/education/computing-in-schools/2012-01-12-computing-in-schools.pdf
Toikkanen, T., & Leinonen, T. (2017). The code ABC MOOC: Experiences from a coding and computational thinking MOOC for Finnish primary school teachers. In P. J. Rich & C. B. Hodges (Eds.), Emerging research, practice, and policy on computational thinking (pp. 239–248). Cham, Switzeland: Springer. Retrieved from https://doi.org/10.1007/978-3-319-52691-1_15
Ucgul, M., & Cagiltay, K. (2014). Design and development issues for educational robotics training camps. International Journal of Technology and Design Education, 24(2), 203–222. Retrieved from https://doi.org/10.1007/s10798-013-9253-9
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. Retrieved from https://doi.org/10.1145/1118178.1118215
Yadav, A., Gretter, S., Good, J., & McLean, T. (2017). Computational thinking in teacher education. In P. J. Rich & C. B. Hodges (Eds.), Emerging research, practice, and policy on computational thinking (pp. 205–220). Cham, Switzeland: Springer. Retrieved from https://doi.org/10.1007/978-3-319-52691-1_13
Yadav, A., Gretter, S., Hambrusch, S., & Sands, P. (2017). Expanding computer science education in schools: Understanding teacher experiences and challenges. Computer Science Education, 26(4), 235–254. Retrieved from https://doi.org/10.1080/08993408.2016.1257418
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), 1–16. Retrieved from https://doi.org/10.1145/2576872
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Appendix: Interview Questions
Appendix: Interview Questions
-
1.
Could you please introduce yourself? (Age, education level, teaching experience)
-
2.
What is your experience in coding education? (Both as a teacher and a student)
-
3.
What does “coding” mean for you?
-
4.
Do you think really coding is important for students? Why?
-
5.
Is it really necessary for individuals to learn coding? Why?
-
6.
What should be included in the content of a coding course?
-
7.
How should coding be taught?
-
8.
What kinds of difficulties do you face during a coding course? How do you overcome them?
-
9.
Do you feel qualified to teach coding?
-
10.
What kinds of qualification should a teacher have to teach coding effectively?
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Kadirhan, Z., Gül, A., Battal, A. (2018). Self-Efficacy to Teach Coding in K-12 Education. In: Hodges, C. (eds) Self-Efficacy in Instructional Technology Contexts. Springer, Cham. https://doi.org/10.1007/978-3-319-99858-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-99858-9_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99857-2
Online ISBN: 978-3-319-99858-9
eBook Packages: EducationEducation (R0)