Abstract
The purpose of this convergent mixed-methods study was to evaluate the effect of educational robotics on pre-service teachers’ programming comprehension and motivation. Computer science is increasingly being integrated into K-8 curricula. However, a shortage of teachers trained to teach basic computer science concepts remains unresolved. This study thus utilized educational robotics as “mindtools” to teach programming concepts to pre-service teachers. Data were obtained through a pre-post comprehension assessment, a pre-post motivation survey, field notes, and individual interviews. The findings of this study indicated that pre-service teachers’ comprehension of programming concepts and motivation related to programming can be improved through educational robotics to statistically significant levels. Design implications on integrating educational robotics into pre-service teacher programming instruction are discussed.
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Ala-Mutka, K. (2004). Problems in learning and teaching programming. Codewitz Needs Analysis, 1–13. http://www.cs.tut.fi/~edge/literature_study.pdf
Alimisis, D., Moro, M., Arlegui, J., Pina, A., Stassini, F., & Papanikolaou, K. (2007). Robotics & constructivism in education: The TERECoP project. EuroLogo, 1–11. http://users.sch.gr/adamopou/docs/syn_eurologo2007_alimisis.pdf
Alkaria, A., & Alhassan, R. (2017). The effect of in-service training of computer science teachers on scratch programming language skills using an electronic learning platform on programming skills and the attitudes towards teaching programming. Journal of Education and Training Studies. https://doi.org/10.11114/jets.v5i11.2608
Arwood, L. (2004). Teaching cell biology to nonscience majors through forensics, or how to design a killer course. Cell Biology Education, 3, 131–138.
Bandura, A. (1997). Self-efficacy. Harvard Mental Health Letter, 13(9), 4–5.
Bayman, P., & Mayer, R. E. (1983). A diagnosis of beginning programmers’ misconceptions of BASIC programming statements. Communications of the ACM, 26(9), 677–679.
Black, A. E., & Deci, E. L. (2000). The effects of instructors’ autonomy support and students’ autonomous motivation on learning organic chemistry: A self-determination theory perspective. Science Education, 84(6), 740–756.
Bruner, J. (1996). The culture of education. Harvard University Press. https://www.hup.harvard.edu/catalog.php?isbn=9780674179530
Bucks, G. W. (2010). A phenomenographic study of the ways of understanding conditional and repetition structures in computer programming languages. https://www.proquest.com/docview/858607918
Burke, Q., Schep, M., & Dalton, T. (2016). CS for SC: A landmark report on K-12 computer science in South Carolina (pp. 1–19). National Science Foundation. https://doi.org/10.1145/3017680.3022413
Creswell, J. W. (2017). Qualitative inquiry and research design: choosing among the five traditions: Sage.
Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.): Sage.
DeVellis, R. F. (2003). Scale development: theory and applications. Thousand Oaks, CA: Sage.
El-Hamamsy, L., Chessel-Lazzarotto, F., Bruno, B., Roy, D., Cahlikova, T., Chevalier, M., & Mondada, F. (2020). A computer science and robotics integration model for primary school: Evaluation of a large-scale in-service K-4 teacher-training program. Education and Information Technologies. https://doi.org/10.1007/s10639-020-10355-5
Erol, O., & Kurt, A. A. (2017). The effects of teaching programming with scratch on pre-service information technology teachers’ motivation and achievement. Computers in Human Behavior, 77, 11–18. https://doi.org/10.1016/j.chb.2017.08.017
Falloon, G. (2016). An analysis of young students’ thinking when completing basic coding tasks using scratch Jnr. On the iPad. Journal of Computer Assisted Learning, 32(6), 576–593. https://doi.org/10.1111/jcal.12155
Fegely, A., Winslow, J., Lee, C., & Rubbo, L. J. (2021). The effects of robotics professional development on science and mathematics teaching performance and student achievement in underserved middle schools. Contemporary Issues in Technology and Teacher Education, 21(4), 655–679.
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109.
Gleasman, C., & Kim, C. (2020). Pre-service teachers’ use of block-based programming and computational thinking to teach elementary mathematics. Digital Experiences in Mathematics Education, 6(1), 52–90. https://doi.org/10.1007/s40751-019-00056-1
Glynn, S. M., Brickman, P., Armstrong, N., & Taasoobshirazi, G. (2011). Science motivation questionnaire II: Validation with science majors and nonscience majors. Journal of Research in Science Teaching, 48(10), 1159–1176. https://doi.org/10.1002/tea.20442
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
Gupta, S. D. (1960). Point biserial correlation coefficient and its generalization. Psychometrika, 25(4), 393–408.
Han, I. (2013). Embodiment: A new perspective for evaluating physicality in learning. Journal of Educational Computing Research, 49(1), 41–59. https://doi.org/10.2190/EC.49.1.b
Hanus, M. D., & Fox, J. (2015). Assessing the effects of gamification in the classroom: a longitudinal study on intrinsic motivation, social comparison, satisfaction, effort, and academic performance. Computers & Education, 80, 152–161.
Jaipal-Jamani, K., & Angeli, C. (2017). Effect of robotics on elementary pre-service teachers’ self-efficacy, science learning, and computational thinking. Journal of Science Education and Technology, 26(2), 175–192. https://doi.org/10.1007/s10956-016-9663-z
Kay, J. S., Moss, J. G., Engelman, S., & McKlin, T. (2014). Sneaking in through the back door: Introducing K-12 teachers to robot programming. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education. https://doi.org/10.1145/2538862.2538972
Kaya, E., Newley, A., Deniz, H., Yesilyurt, E., & Newley, P. (2015). Introducing engineering design to a science teaching methods course through educational robotics and exploring changes in views of preservice elementary teachers. Journal of College Science Teaching, 47(2), 66–75.
Kelleher, C., Pausch, R., & Kiesler, S. (2007). Storytelling Alice motivates middle school girls to learn computer programming. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’07, ACM.
Kim, C., Kim, D., Yuan, J., Hill, R. B., Doshi, P., & Thai, C. N. (2015). Robotics to promote elementary education pre-service teachers’ STEM engagement, learning, and teaching. Computers & Education, 91, 14–31. https://doi.org/10.1016/j.compedu.2015.08.005
Kim, C., Yuan, J., Vasconcelos, L., Shin, M., & Hill, R. B. (2018). Debugging during block-based programming. Instructional Science, 46(5), 767–787. https://doi.org/10.1007/s11251-018-9453-5.
Kopcha, T. J., McGregor, J., Shin, S., Qian, Y., Choi, J., Hill, R., & Choi, I. (2017). Developing an integrative STEM curriculum for robotics education through educational design research. Journal of Formative Design in Learning, 1(1), 31–44. https://doi.org/10.1007/s41686-017-0005-1.
Kucuk, S., & Sisman, B. (2018). Pre-service teachers’ experiences in learning robotics design and programming. Informatics in Education, 17(2), 301–320. https://doi.org/10.15388/infedu.2018.16
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage.
Majherová, J., & Králík, V. (2017). Innovative methods in teaching programming for future informatics teachers. European Journal of Contemporary Education, 6(3), 390–401. https://doi.org/10.13187/ejced.2017.3.390
Mannila, L., Dagiene, V., Demo, B., Grgurina, N., Mirolo, C., Rolandsson, L., & Settle, A. (2014). Computational thinking in K-9 education. Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference - ITiCSE-WGR ’14, 1–29. https://doi.org/10.1145/2713609.2713610
Martin, F. G., Scribner-MacLean, M., Christy, S., Rudnicki, I., Londhe, R., Manning, C., & Goodman, I. F. (2011). Reflections on iCODE: using web technology and hands-on projects to engage urban youth in computer science and engineering. Autonomous Robots, 30(3), 265–280. https://doi.org/10.1007/s10514-011-9218-3.
Marzano, R. J. (2007). The art and science of teaching. ASCD.
McGill, T. J., & Volet, S. E. (1997). A conceptual framework for analyzing students’ knowledge. Journal of Research on Computing in Education, 29(3), 276–298.
Mertler, C. A. (2017). Action research: improving schools and empowering educators (5th ed.). Sage.
Ortiz, A., Bos, B., & Smith, S. (2015). The power of educational robotics as an integrated STEM learning experience in teacher preparation programs. Journal of College Science Teaching. https://doi.org/10.2505/4/jcst15_044_05_42
Pallant, J. (2007). SPSS survival manual:A step by step guide to data analysis using SPSS for Windows (3rd ed.). McGraw Hill Open University Press.
Piaget, J. (1973). To understand is to invent. Basic Books.
Rogerson, C., & Scott, E. (2010). The fear factor: How it affects students learning to program in a tertiary environment. Journal of Information Technology Education: Research, 9, 147–171. https://doi.org/10.28945/1183
Roschelle, J., & Teasley, S. D. (1994). The construction of shared knowledge in collaborative problem solving. NATO ASI Series F Computer and Systems Sciences, 128, 69–69.
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860.
Saldaña, J. (2016). The coding manual for qualitative researchers (3rd ed.). Sage.
Salkind, N. J. (2010). Encyclopedia of research design (Vols. 1 – 0). Sage. https://doi.org/10.4135/9781412961288
Sentance, S., & Csizmadia, A. (2017). Computing in the curriculum: Challenges and strategies from a teacher’s perspective. Education and Information Technologies. https://doi.org/10.1007/s10639-016-9482-0
Sisman, B., & Kucuk, S. (2019). An educational robotics course: Examination of educational potentials and pre-service teachers’ experiences. International Journal of Research in Education and Science, 5(1), 510–531.
Soloway, E., & Ehrlich, K. (1984). Empirical studies of programming knowledge. IEEE Transactions on Software Engineering, 10(5), 595–609.
Sullivan, F., & Moriarty, M. (2009). Robotics and discover learning: Pedagogical beliefs, teacher practice, and technology integration. Journal of Technology and Teacher Education, 17, 109–142. http://people.umass.edu/florence/jtate.pdf%5Cnpapers2://publication/uuid/284416E1-4D1B-48FA-8F07-583B7FCCFA47
Thompson, G. (2008). Beneath the apathy. Educational Leadership, 65(6), 50–54.
Weintrop, D., & Wilensky, U. (2017). Comparing block-based and text-based programming in high school computer science classrooms. ACM Transactions on Computing Education, 18(1), 1–25. https://doi.org/10.1145/3089799
Yuan, J., Kim, C., Vasconcelos, L., Shin, M. Y., Gleasman, C., & Umutlu, D. (2022). Preservice elementary teachers’ engineering design during a robotics project. Contemporary Issues in Technology and Teacher Education. https://citejournal.org/volume-22/issue-1-22/science/preservice-elementary-teachers-engineering-design-during-a-robotics-project/
Yukselturk, E., & Altiok, S. (2017). An investigation of the effects of programming with scratch on the preservice IT teachers’ self-efficacy perceptions and attitudes towards computer programming. British Journal of Educational Technology, 48(3), 789–801. https://doi.org/10.1111/bjet.12453
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Fegely, A., Tang, H. Learning programming through robots: the effects of educational robotics on pre-service teachers’ programming comprehension and motivation. Education Tech Research Dev 70, 2211–2234 (2022). https://doi.org/10.1007/s11423-022-10174-0
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DOI: https://doi.org/10.1007/s11423-022-10174-0