The effect of python programming language teaching on 7th grade students' programming self-efficacy skills
Authors
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Feryad DoğuşKırlı Secondary School, Ministry of National Education, Ordu, Turkey
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Yasin ÖzkanDepartment of Computer Technologies, Zonguldak Bulent Ecevit University, Zonguldak, Turkey
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Sibel Barın ÖzkanDepartment of Computer Technologies, Zonguldak Bulent Ecevit University, Zonguldak, Turkey
DOI:
10.58583/EM.3.2.5Keywords:
Python, Programming education, Programming self-efficacyAbstract
This study aimed to investigate the impact of teaching the Python programming language on the programming self-efficacy of 7th-grade students. A quantitative research design was employed, utilizing a one-group pretest-posttest model. The study sample consisted of 10 students enrolled in the 7th grade at a public school located in a rural district of northern Turkey during the spring semester of the 2023-2024 academic year. These students attended a school affiliated with the Ministry of National Education. The study group was selected using the convenience sampling method, a type of purposive sampling. Data for this research was collected using the Programming Self-Efficacy Scale for Secondary School Students, developed by Kukul, Gökçearslan, and Günbatar (2017). The collected data were analyzed using the Jamovi software. To assess the suitability of the data for normal distribution, skewness and kurtosis coefficients, as well as Shapiro-Wilk test values (a normality test), were examined, alongside an inspection of histogram plots. The results indicated that the data were normally distributed with respect to both research questions. For the first research question, a paired sample t-test was conducted to determine whether there was a statistically significant difference between the pre-test and post-test scores. The analysis revealed that Python instruction did not have a significant effect on the programming self-efficacy of the 7th-grade students. Regarding the second research question, the homogeneity of the data was assessed. Levene's test for equality of variances was performed, confirming that the assumption of homogeneity was met. Consequently, independent sample t-tests were conducted to examine the significance of the difference in pre-test and post-test scores based on gender. The results indicated no statistically significant difference in programming self-efficacy scores between male and female students.
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References
Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation, (2), pp. 89-195. Academic Press. https://doi.org/10.1016/S0079-7421(08)60422-3
Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman and Company.
Futschek, G. (2006). Algorithmic thinking: The key for understanding computer science. In International Conference on Informatics in Secondary Schools – Evolution and Perspectives (pp. 159-168). Springer. https://doi.org/10.1007/11915355_15
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
Guzdial, M. (2015). Learner-centered design of computing education: Research on computing for everyone. Synthesis Lectures on Human-Centered Informatics, 8(6), 1-165. https://doi.org/10.2200/S00684ED1V01Y201511HCI033
Kafai, Y. B., & Burke, Q. (2015). Connected code: Why children need to learn programming. MIT Press. https://doi.org/10.7551/mitpress/9992.001.0001
Kukul, V., Gökçearslan, Ş., & Günbatar, M. S. (2017). Programming self-efficacy scale for middle school students: Development, validity, and reliability. Educational Technology Theory and Practice, 7(1), 158-179.
Luxton-Reilly, A. (2016). Learning to program is easy. Proceedings of the 19th Australasian Computing Education Conference (ACE 2016), 160, 284-293. https://dl.acm.org/doi/10.1145/2899415.2899432
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. https://doi.org/10.1016/j.chb.2014.09.012
McMillan, J. H., & Schumacher, S. (2014). Research in education: Evidence-based inquiry (7th ed.). Pearson Higher Ed.
Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc.
Piaget, J. (1972). The principles of genetic epistemology. Routledge.
Piaget, J. (1972). The psychology of the child. Basic Books.
Sáez-López, J. M., Román-González, M., & Vázquez-Cano, E. (2016). Visual programming languages integrated across the curriculum in elementary school: A two year case study using “Scratch” in five schools. Computers & Education, 97, 129-141. https://doi.org/10.1016/j.compedu.2016.03.003
Schunk, D. H., & Pajares, F. (2009). Self-efficacy theory. In K. R. Wentzel & A. Wigfield (Eds.), Handbook of motivation at school (pp. 35-53). Routledge.
Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158. https://doi.org/10.1016/j.edurev.2017.09.003
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. https://doi.org/10.2307/j.ctvjf9vz4
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.111821
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Copyright (c) 2024 Feryad Doğuş, Yasin Özkan, Sibel Barın Özkan
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