Examination of the Transitions between Modal Representations in Coding Training
This study aims to determine the perceptions of undergraduates, who are receiving coding training in a faculty of education, on modal representations employed in the training process and identify their transition skills between representations. The research used the quantity search method, non-experimental design, and descriptive search models, calculating the obtained data frequencies by numerical analysis. The study was carried out with the participation of 58 undergraduates in the Computer and Instructional Technology Department of an education faculty in the 2018-2019 academic year. The representational skill-testing used in the study consists of 12 open-ended questions developed by the researchers. The reliability of the test was calculated as .96 with the Pearson product-moment correlation coefficient value. Transitions between the representation of mathematics, verbal, flowchart, and code were rankly listed in the test, which was applied in a single session. The obtained data were scored with a grading key and undergraduate achievement was assessed according to the transition between representations. The analysis has revealed that representation transition skills may differ from each other and that coding training, which takes into account these transition skills, should be carried out with flow chart, verbal, mathematical and ultimately code representations, respectively.
Aslanyürek M., Korkmaz A., Büyükgöze S.B. ve Gezgin D.M (2018) Algorithm and Programming All Resolved Question Bank, Edt. Gezgin D.M., Efeakademi Publisher, Istanbul.
Baist, A., & Pamungkas, A. S. (2017). Analysis of Student Difficulties in Computer Programming. VOLT: Jurnal Ilmiah Pendidikan Teknik Elektro, 2(2), 81-92. https://doi.org/10.30870/volt.v2i2.2211
Barr, V., & Guzdial, M. (2015). Advice on teaching CS, and the learnability of programming languages. Communications of the ACM, 58(3), 8–9.
Bodur, F. (2010). Additives to learning visual elements in distance teaching textbooks: Evaluation of Anadolu University distance teaching student views. Eskişehir: Anadolu University.
Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational psychology review, 3(3), 149-210. https://doi.org/10.1007/BF01320076
Çakıroğlu,Ü, Er, B. Uğur, N., & Aydoğdu, E. (2018). Exploring the use of self-regulation strategies in programming with regard to learning styles. International Journal of Computer Science Education in Schools, 2(2), 14-28. https://doi.org/10.21585/ijcses.v2i2.29
Crafton, L., Brennan, M., and Silvers, P. (2009). Creating a critical multiliteracies curriculum: Repositioning art in the early childhood classroom. M. J. Narey (ed.), Making Meaning, 2009.
Demirer, V., & Nurcan, S. A. K. (2015). Information and communication technology (ICT) education in Turkey and changing roles of the ICT teachers. The Journal of International Education, (5), 434-448.
Demirel, Ö. (2007). Teaching principles and methods: The art of teaching.Ankara: Pegem A Yayıncılık.
Du Boulay, B. (1986). Some difficulties of learning to program. Journal of Educational Computing Research, 2(1), 57-73. https://doi.org/10.2190/3LFX-9RRF-67T8-UVK9
Eker M. (2005), Understanding the algorithm, II. Baskı, Niravan Yayınları, Ankara
Ensmenger, N. (2016). The multiple meanings of a flowchart. Information & Culture, 51(3), 321-351. https://doi.org/10.7560/IC51302
Ergin, H., & İpek,J (2017). Collaborative Creative Problem Solving Model in Programming Language Teaching: A Case Study. Ege Eğitim Teknolojileri Dergisi, 1(2), 135-148.
Erümit, K. A., Karal, H., Şahin, G., Aksoy, D. A., Aksoy, A., & Benzer, A. I. (2019). A model suggested for programming teaching: Programming in seven steps. Education and Science, 44(197), 155-183. http://dx.doi.org/10.15390/EB.2018.7678
Ginat, D. (2004). On novice loop boundaries and range conceptions. Computer Science Education, 14(3), 165-181.
Gunel, M., Atila, M. E., & Buyukkasap, E. (2009). The impact of using multi modal representations within writing to learn activities on learning electricity unit at 6th grade. Elementary Education Online, 8(1), 183-199. http://ilkogretim-online.org.tr/index.php/io/article/viewFile/1705/1541
Gülbahar, Y., & Karal, H. (2018). Teaching programming from the theory to application [Kuramdan uygulamaya programlama öğretimi]. Ankara: Pegem Akademi Yayıncılık.
Han, S. J., & Kim, S. S. (2016). The effects of app programing education using m-Bizmaker on creative problem solving ability. The Journal of Korean Association of Computer Education, 19(6), 25-32.
Hagevik, R., Beilfuss, M. and Dickerson, D.(2006). Multiple representations in science education. NARST Conference, April
Hu, M. (2004). Teaching novices programming with core language and dynamic visualisation. Proceedings of the 17th NACCQ, 94-103. Retrieved from https://www.citrenz.ac.nz/conferences/2004/hu.pdf
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. https://doi.org/10.1016/j.procs.2012.04.056
Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys (CSUR), 37(2), 83-137. https://doi.org/10.1145/1089733.1089734
Kuljis, J., & Baldwin, L. P. (2000). Visualisation Techniques for Learning and Teaching Programming. Journal of computing and information technology, 8(4), 285-291. https://doi.org/10.2498/cit.2000.04.03
Lahtinen, E., Ala-Mutka, K. ve Jarvinen, H. (2005) A Study of Difficulties of Novice Programmers. In Acm Sigcse Bulletin, ACM, 37(3), 14-18.
Lau, W. W., & Yuen, A. H. (2011). Modelling programming performance: Beyond the influence of learner characteristics. Computers & Education, 57(1), 1202-1213. https://doi.org/10.1016/j.compedu.2011.01.002
Lemke, J. (1993). Multypling meaning: Literacy in a multimedia world draft. National reading conference 16p.43rd.
Lemke, J. (1998). Multiplying meaning: Visual and verbal semiotics in scientific text. In J. R. Martin and R. Veel (Eds.), Reading Science (pp. 87-113). London: Routledge.
Law, K. M., Lee, V. C., & Yu, Y. T. (2010). Learning motivation in e-learning facilitated computer programming courses. Computers & Education, 55(1), 218-228. https://doi.org/10.1016/j.compedu.2010.01.007
Owens, K. D., & Clements, M. K. (1998). Representations in spatial problem solving in the classroom. The Journal of Mathematical Behavior, 17(2), 197-218. https://doi.org/10.1016/S0364-0213(99)80059-7
Pineda, L., & Garza, G. (2000). A model for multimodal reference resolution. Computational Linguistics, 26(2), 139-193. https://doi.org/10.1162/089120100561665
Porter, R., & Calder, P. (2004, January). Patterns in learning to program: an experiment?. In Proceedings of the Sixth Australasian Conference on Computing Education-Volume 30 (pp. 241-246). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.4.2526&rep=rep1&type=pdf
Qian, Y., & Lehman, J. (2020). An Investigation of High School Students’ Errors in Introductory Programming: A Data-Driven Approach. Journal of Educational Computing Research, 58(5), 919-945. https://doi.org/10.1177/0735633119887508
Ramadhan, H. A. (2000). Programming by discovery. Journal of Computer Assisted Learning, 16(1), 83-93. https://doi.org/10.1046/j.1365-2729.2000.00118.x
Renumol, V., Jayaprakash, S., & Janakiram, D. (2009). Classification of cognitive difficulties of students to learn computer programming. Indian Institute of Technology, India, 12. http://dos.iitm.ac.in/publications/LabPapers/techRep2009-01.pdf
Robins, A., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer science education, 13(2), 137-172. https://doi.org/10.1076/csed.18.104.22.16800
Saeli, M., Perrenet, J., Jochems, W. M., & Zwaneveld, B. (2011). Teaching programming in Secondary school: A pedagogical content knowledge perspective. Informatics in education, 10(1), 73-88. https://doi.org/10.15388/infedu.2011.06
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.
Safari, E., & Hejazi, M. (2017). Learning styles and self-regulation: an associational study on high school students in iran. Mediterranean Journal of Social Sciences, 8(1), 463. https://doi.org/10.5901/mjss.2017.v8n1p463
Salleh, S. M., Shukur, Z., & Judi, H. M. (2018). Scaffolding model for efficient programming learning based on cognitive load theory. Int. J. Pure Appl. Math, 118(7), 77-83. https://acadpubl.eu/jsi/2018-118-7-9/articles/7/10.pdf
Seeger, F. (1996). Representations in the mathematics classroom: Reflections and constructions. University of Helsinki.
Shadiev, R., Hwang, W. Y., Yeh, S. C., Yang, S. J., Wang, J. L., Han, L., & Hsu, G. L. (2014). Effects of unidirectional vs. reciprocal teaching strategies on web-based computer programming learning. Journal of educational computing research, 50(1), 67-95. https://doi.org/10.2190/EC.50.1.d
Sheard, J., Simon, S., Hamilton, M., & Lönnberg, J. (2009, August). Analysis of research into the teaching and learning of programming. In Proceedings of the fifth international workshop on Computing education research workshop (pp. 93-104). https://doi.org/10.1145/1584322.1584334
Shneiderman, B., Mayer, R., McKay, D., & Heller, P. (1977). Experimental investigations of the utility of detailed flowcharts in programming. Communications of the ACM, 20(6), 373-381. https://doi.org/10.1145/359605.359610
Sterritt, R., Hanna, P., & Campbell, J. (2015, March). Reintroducing programming to the school environment. In INTED 2015-9th International Technology, Education and Development Conference (pp. 7630-7631). International Academy of Technology, Education and Development. Madrid, Spain. Retrieved from http://library.iated.org/view/STERRITT2015REI
Türker, P. M., & Pala, F. K. (2020). The effect of algorithm education on students’ computer programming self-efficacy perceptions and computational thinking skills. International Journal of Computer Science Education in Schools, 3(3), 19-32.
Van der Meij, J., & de Jong, T. (2006). Supporting students' learning with multiple representations in a dynamic simulation-based learning environment. Learning and instruction, 16(3), 199-212. https://doi.org/10.1016/j.learninstruc.2006.03.007
Yağcı, M. (2018). A Study on Computational Thinking and High School Students’ Computational Thinking Skill Levels. International Online Journal of Educational Sciences, 10(2), 81-96. https://doi:10.15345/iojes.2018.02.006
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