##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 30, 2023

Abdullah Koray  

Eda Bilgin

Abstract

This study was carried out to determine the effect of Scratch-based coding applications integrated into the 5E learning model used in science teaching on students’ computational thinking skills and self-efficacy towards block-based programming. In addition, students’ perceptions of the activity were measured after each Scratch activity, which was applied at different stages of the course and with different difficulty. The study employed the pretest-posttest control group less design, one of the quasi-experimental methods. The study sample consist of 22 6th grade students attending a public school in Turkey located in a district center in the Eastern Black Sea region. The study was carried out in a five-week period in the 2022-2023 academic years. Computational thinking scale and robotics attitude scale, self-efficacy perception scale related to block-based programming and activity perception scale were used as data collection tools. The data were analyzed using the dependent samples t-test. The findings suggest that computational thinking skills level of students and their self-efficacy perception related to block-based programming increased significantly with the Scratch-based activities integrated into 5E learning model applied in science subjects. In addition, students have positive attitudes towards these activities. Thus, it is recommended to apply Scratch-based coding applications in teaching science subjects.

Downloads

Download data is not yet available.

##plugins.themes.bootstrap3.article.details##

Keywords

Science Teaching, Scratch, 5E Learning Model, Computational Thinking, Self-Efficacy

References
Abdusselam, M. S., Kilis, S., Sahin Cakir, C., & Abdusselam, Z. (2018). Examining microscopic organisms under augmented reality microscope: A 5E learning model lesson. Science Activities, 55(1-2):68-74. DOI: https://doi.org/10.1080/00368121.2018.1517717

Aho, A. V. (2012). Computation and computational thinking. The Computer Journal, 55(7):832-835. DOI: https://doi.org/10.1093/comjnl/bxs074

Akar, S. G. M., & Altun, A. (2017). Individual differences in learning computer programming: A social cognitive approach. Contemporary Educational Technology, 8(3):195-213. Available at: https://dergipark.org.tr/en/pub/cet/issue/30468/329156

Appleton, K. (1997). Analysis and description of students’ learning during science classes using a constructivist‐based model. Journal of Research in Science Teaching: the Official Journal of the National Association for Research in Science Teaching, 34(3):303-318. DOI: https://doi.org/10.1002/(SICI)1098-2736(199703)34:3<303::AID-TEA6>3.0.CO;2-W

Arslan, E., & Isbulan, O. (2021). The Effect of Individual and Group Learning on Block-Based Programming Self-Efficacy and Robotic Programming Attitudes of Secondary School Students. Malaysian Online Journal of Educational Technology, 9(1):108-121. DOI: http://dx.doi.org/10.17220/mojet.2021.9.1.249

Askar, P., & Davenport, D. (2009). An investigation of factors related to self-efficacy for Java programming among engineering students. TOJET: The Turkish Online Journal of Educational Technology, 8(1):26-32. Available at: http://files.eric.ed.gov/fulltext/ED503900.pdf

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2):191-215. DOI: https://doi.org/10.1037/0033-295X.84.2.191

Benton, L., Saunders, P., Kalas, I., Hoyles, C., & Noss, R. (2018). Designing for learning mathematics through programming: A case study of pupils engaging with place value. International Journal of Child-Computer Interaction, 16(2018):68-76. DOI: https://doi.org/10.1016/j.ijcci.2017.12.004

Blikstein, P., Walter-Herrmann, J., & Büching, C. (2013). FabLabs: Of machines, makers and inventors. Fab Lab-Of Machines, Makers and Inventors; Julia Walter-Herrmann & Corinne Büching, Bielefeld, ISBN: 978-3837623826. pp. 203-233.

Buyukkarci, A., and Taslidere, E. (2021). the effect of coding education on students’ efficiency and scratch achievements. i-manager’s Journal of Educational Technology, 18(2):63-74. DOI: https://doi.org/10.26634/jet.18.2.17970

Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2018). Eğitimde bilimsel araştırma yöntemleri. Ankara: Pegem Akademi. ISBN: 978-6052419649. p:202.

Bybee, R. W., Taylor, J. A., Gardner, A., Van Scotter, P., Powell, J. C., Westbrook, A., & Landes, N. (2006). The BSCS 5E instructional model: Origins and effectiveness. Colorado Springs, Co: BSCS, 5, 88-98. Available at: https://fremonths.org/ourpages/auto/2008/5/11/1210522036057/bscs5efullreport2006.pdf

Cakir, M. (2008). Constructivist approaches to learning in science and their implications for science pedagogy: A literature review. International Journal of Environmental and Science Education, 3(4):193-206.

Celik, C., Guven, G., & Kozcu Cakir, N. (2020). Integration of mobile augmented reality (MAR) applications into biology laboratory: Anatomic structure of the heart. Research in Learning Technology, 28(2020):1-11. DOI: https://doi.org/10.25304/rlt.v28.2355

Copley, J. (1992). The integration of teacher education and technology; a constructivist model. In D. Carey., R. Carey., D. Willis & S. Willis (Eds.), Technology and teacher education. AACE. pp. 681.

Coşkunserçe, O. (2023). Comparing the use of block‐based and robotprogramming in introductory programmingeducation: Effects on perceptions of programmingself‐efficacy, Computer Applications in Engineering Education. (Early View). DOI: https://doi.org/10.1002/cae.2263722

Çoruhlu, T. Ş. (2013). Determining the effectiveness of guided materials based on enriched 5e instructional model related to solar system and beyond: Space puzzle unit. Ph. D. Diss, Karadeniz Technical University, Council of Higher Education Thesis Center, Turkey.

De Jong, T., Linn, M. C., & Zacharia, Z. C. (2013). Physical and virtual laboratories in science and engineering education. Science, 340(6130):305-308. DOI: https://doi.org/10.1126/science.1230579

Deci, E. L., Eghrari, H., Patrick, B. C., & Leone, D. (1994). Facilitating internalization: The selfdetermination theory perspective. Journal of Personality, 62(1):119-142.

Durak, H. Y., Yilmaz, F. G. K., & Yilmaz, R. (2019). Computational thinking, programming self-efficacy, problem solving and experiences in the programming process conducted with robotic activities. Contemporary Educational Technology, 10(2):173-197. DOI: https://doi.org/10.30935/cet.554493

Foerster, K. T. (2016). Integrating programming into the Mathematics curriculum: Combining Scratch and Geometry in grades 6 and 7. In Proceedings of the 17th annual conference on information technology education (pp. 91-96). September 28. DOI: https://doi.org/10.1145/2978192.2978222

Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (Eight Edition), p:264 . New York: McGraw-Hill. ISBN: 978-0078097850.

Fraser, B. J. (2023). The evolution of the field of learning environments research. Education Sciences, 13(3):257. https://doi.org/10.3390/educsci13030257

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: https://doi.org/10.3102/0013189X12463051

Güleryüz, H. (2022). The effect of robotic coding (mblock-arduino) activities on students’ self-efficacy and attitudes. Acta Scientific Computer Sciences, 4(8):2-9.

Hacıoğlu, Y., & Dönmez Usta, N. (2020). Digital game design-based STEM activity: Biodiversity example. Science Activities, 57(1):1-15. DOI: https://doi.org/10.1080/00368121.2020.1764468

Hand, B., & Treagust, D. F. (1991). Student achievement and science curriculum development using a constructivist framework. School Science and Mathematics, 91(4):172–176. DOI: https://doi.org/10.1111/j.1949-8594.1991.tb12073.x

Hew, K.F., Brush, T. (2007). Integrating technology into K-12 teaching and learning: current knowledge gaps and recommendations for future research. Education Tech Research Development, 55(3):223-252. DOI: https://doi.org/10.1007/s11423-006-9022-5

Hsu, PS. (2016). Examining current beliefs, practices and barriers about technology integration: A case study. TechTrends 60(1):30-40. DOI: https://doi.org/10.1007/s11528-015-0014-3

Iskrenovic-Momcilovic, O. (2020). Improving geometry teaching with scratch. International Electronic Journal of Mathematics Education, 15(2):em0582. DOI: https://doi.org/10.29333/iejme/7807

Jiang, B., & Li, Z. (2021). Effect of Scratch on computational thinking skills of Chinese primary school students. Journal of Computers in Education, 8(4):505-525. DOI: https://doi.org/10.1007/s40692-021-00190-z

Kafai, Y. B., & Burke, Q. (2013). Computer programming goes back to school. Phi Delta Kappan, 95(1):61-65. DOI: https://doi.org/10.1177/003172171309500111

Kasalak, I. (2017). Effects of robotic coding activities on the effectiveness of secondary school students’ self-efficacy and student experience about activities. Unpublished master’s thesis. Hacettepe University, Ankara.

Kasalak, İ. & Altun, A. (2018). Perceıved self-effıcacy scale development study related to blockbased programmıng: Scratch case. Educatıonal Technology Theory And Practıce, 8 (1):209-225. DOI: https://doi.org/10.17943/Etku.335916

Kim, C., & Hannafin, M. J. (2011). Scaffolding problem-solving in technology-enhanced learning environments (TELEs): Bridging research and theory with practice. Computers & Education, 56(2):403-417. DOI: https://doi.org/10.1016/j.compedu.2010.08.024

Korkmaz, Ö., Çakır, R., & Özden, M. Y. (2015). Computatıonal thınkıng levels scale (ctls) adaptatıon for secondary school level. Gazi Journal of Educational Science, 1(2):143-162.

Koyunlu Unlu, Z. & Dokme, I. (2020). The effect of technology-supported inquiry-based learning in science education: Action research. Journal of Education in Science, Environment and Health (JESEH), 6(2):120-133. DOI: https://doi.org/10.21891/jeseh.632375

Kozcu Cakir, N., & Guven, G. (2019). Arduino-assisted robotic and coding applications in science teaching: Pulsimeter activity in compliance with the 5E learning model. Science Activities, 56(2):42-51. DOI: https://doi.org/10.1080/00368121.2019.1675574

Lai, A. F., Lai, H. Y., Chuang, W. H., & Wu, Z. H. (2015). Developing a mobile learning management system for outdoors nature science activities based on 5e learning cycle. Paper Presented at the International Association for Development of the Information Society (IADIS) International Conference on e-Learning, Spain.

Lai, C. S., & Lai, M. H. (2012, June). Using computer programming to enhance science learning for 5th graders in Taipei. In 2012 International Symposium on Computer, Consumer and Control, Taiwan, June 4-6, pp. 146-148. IEEE. DOI: https://doi.org/10.1109/IS3C.2012.45

Lye, S. Y., Wee, L. K., Kwek, Y. C., Abas, S., & Tay, L. Y. (2014). Design, customization and implementation of energy simulation with 5E model in elementary classroom. Journal of Educational Technology & Society, 17(3):121-137. Available at: https://www.jstor.org/stable/10.2307/jeductechsoci.17.3.121

Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of Cardiac Anaesthesia, 22(1):67. DOI: https://doi.org/10.4103/aca.ACA_157_18

Molina-Ayuso, Á., Adamuz-Povedano, N., Bracho-López, R., & Torralbo-Rodríguez, M. (2022). Introduction to computational thinking with scratch for teacher training for spanish primary school teachers in mathematics. Education Sciences, 12(12):899. DOI: https://doi.org/10.3390/educsci12120899

Moreno-León, J., & Robles, G. (2016). Code to learn with Scratch? A systematic literature review. In 2016 IEEE Global Engineering Education Conference (EDUCON), United Arab Emirates, April 10-13, pp. 150-156. IEEE. DOI: https://doi.org/10.1109/EDUCON.2016.7474546

Ogegbo, A. A., & Ramnarain, U. (2022). A systematic review of computational thinking in science classrooms. Studies in Science Education, 58(2):203-230. DOI: https://doi.org/10.1080/03057267.2021.1963580

Oliveira, A., Feyzi Behnagh, R., Ni, L., Mohsinah, A. A., Burgess, K. J., & Guo, L. (2019). Emerging technologies as pedagogical tools for teaching and learning science: A literature review. Human Behavior and Emerging Technologies, 1(2):149-160. DOI: https://doi.org/10.1002/hbe2.141

Özbek, Z. T., & Uslu, N. A. (2021). Technology integration into science education: Systematic review and mapping of postgraduate theses in Turkey. Başkent University Journal of Education, 8(2):427-440. Available at: http://buje.baskent.edu.tr/index.php/buje/article/view/404

Papert, S., & Solomon, C. (1971). Twenty things to dowith a computer (Artificial Intelligence Memo Number248).Cambridge, MA: Massachusetts Institute of Technology. Available at: https://files.eric.ed.gov/fulltext/ED077240.pdf

Resnick, M. (2013). Learn to code, code to learn. how programming prepares kids for more than math. EdSurge (May 8, 2013). Available at: https://www.edsurge.com/news/2013-05-08-learn-to-code-code-to-learn

Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., & Kafai, Y. (2009). Scratch: programming for all. Communications of the ACM, 52(11):60-67. DOI: https://doi.org/10.1145/1592761.1592779

Rodríguez-Martínez, J. A., González-Calero, J. A., & Sáez-López, J. M. (2020). Computational thinking and mathematics using Scratch: an experiment with sixth-grade students. Interactive Learning Environments, 28(3):316-327. DOI: https://doi.org/10.1080/10494820.2019.1612448

Shamir, M., Kocherovsky, M., & Chung, C. (2019). A paradigm for teaching math and computer science concepts in k-12 learning environment by integrating coding, animation, dance, music and art. In 2019 IEEE Integrated STEM Education Conference (ISEC), Princeton, March 16, pp. 62-68. IEEE. DOI: https://doi.org/10.1109/ISECon.2019.8882015

Silva, A., Silva, J., Gouveia, C., Silva, E., Rodrigues, P., Barbot, A.,. & Coelho, D. (2020). Science education and computational thinking–adapting two projects from classroom learning to emergency distance learning. International Journal on Lifelong Education and Leadership, 6(2):31-38. DOI: https://doi.org/10.25233/ijlel.803552

Talan, T. (2020). Investigation of the Studies on the Use of Scratch Software in Education. Journal of Education and Future, 2020(18): 95-111. DOI: https://doi.org/10.30786/jef.556701

Tedre, M., & Denning, P. J. (2016). The long quest for computational thinking. In Proceedings of the 16th Koli Calling international conference on computing education research, Finland, November 24-27, pp. 120-129. DOI: https://doi.org/10.1145/2999541.2999542

Vinayakumar R., Soman K. & Menon, P. (2018). Fractal Geometry: Enhancing Computational Thinking with MIT Scratch, 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Bengaluru, India, July 10-12. pp. 1-6, DOI: https://doi.org/10.1109/ICCCNT.2018.8494172

Wing, J. (2006). Computational thinking. Communications of the ACM, 49(3):33–36. DOI: https://doi.org/10.1145/1118178.1118215
Yamamori, K. (2019). Classroom practices of low-cost STEM education using scratch. Journal of Advanced Research in Social Sciences and Humanities, 4(6):192-198. DOI: http://dx.doi.org/10.2139/ssrn.3791166

Yukselturk, E. & Altıok, S. (2016). Pre-Service Information Technology Teachers` Perceptions about Using Scratch Tool in Teaching Programming. Mersin University Journal of the Faculty of Education, 12(1):39-52. DOI: https://doi.org/10.17860/efd.94270
How to Cite
Koray, A., & Bilgin, E. (2023). The Effect of Block Coding (Scratch) Activities Integrated into the 5E Learning Model in Science Teaching on Students’ Computational Thinking Skills and Programming Self-Efficacy. Science Insights Education Frontiers, 18(1), 2825–2845. https://doi.org/10.15354/sief.23.or410
Section
Original Article