Implementing Whole Brain Teaching Within A Pre-Service Teachers’ Introductory Programming Instruction: An Action Research Study
DOI:
https://doi.org/10.70232/jrmste.v2i2.32Keywords:
Whole-Brain Teaching and Learning, Programming Walk-Around, Cooperative Learning, Collaborative Learning, Instructional ScaffoldingAbstract
This study investigated the implementation of whole brain teaching within a constructivist teaching strategy in pre-service teachers’ programming education, towards enhancing their knowledge of procedural programming. From a qualitative paradigm lens, hermeneutic phenomenology guided the inquiry. The research strategy involves two action research cycles involving fifty-eight pre-service teachers purposively sampled over two academic sessions. The Whole Brain instructional plan was designed to facilitate the programming instruction. Data were collected through surveys, interviews, and classroom observations. The findings generated two themes: programming intervention promoted student engagement and pre-service teachers’ teaching strategies through self-confidence in problem-solving and self-regulation of learning. These results contributed to the literature on pedagogical innovations in the teaching of programming and provided recommendations for enhancing pre-service teachers’ programming learning with a focus on holistic brain development through the implementation of the whole-brain programming walk-around and instructional plan for programming lessons. For transparency in research, it is necessary to state that even though the data for this study were collected in 2016 and 2017, the data are still significant within the context of programming education. The findings of this study support current literature on constructivist learning and also address gaps in the implementation of whole-brain learning for programming teaching. The findings of this study cannot be generalized due to the nature of action research studies, but can be replicated in another context. Further exploration with larger sample sizes and diverse contexts could bolster the robustness and generalizability of these findings.
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References
Ahmadi S. M. & Motaghi, F. (2021). Cognitive vs metacognitive scaffolding strategies and EFL learners’ listening comprehension development. Language Teaching Research. https://doi.org/10.1177/13621688211021821
Angeli, C. (2022). The effects of scaffolded programming scripts on pre-service teachers’ computational thinking: Developing algorithmic thinking through programming robots. International Journal of Child-Computer Interaction, 31(100329). https://doi.org/10.1016/j.ijcci.2021.100329
Bawaneh, A. K. A., Abdullah, A. G. K., Saleh, S., & Yin, K. Y. (2011). Jordanian students’ thinking styles based on Herrmann whole brain model. International Journal of Humanities and Social Science, 1(9), 89–97.
Bawaneh, A. K. A., Nurulazam Md Zain, A., & Salmiza, S. (2011). The Effect of Herrmann Whole Brain Teaching Method on Students’ Understanding of Simple Electric Circuits. European Journal of Physics Education, 2(2), 1–23.
Belland, B. R. (2017). Instructional scaffolding in STEM education: Strategies and efficacy evidence. Springer.
Bennedsen, J. & Caspersen, M. E. (2019). Failure rates in introductory programming: 12 years later. ACM Inroads, 10(2), 30–36. https://doi.org/10.1145/3324888
Berssanette, J. H., & de Francisco, A. C. (2021). Active learning in the context of the teaching/learning of computer programming: A systematic review. Journal of Information Technology Education. Research, 20(201). http://dx.doi.org/10.28945/4767
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Çakıroğlu, U & Öztürk, M. (2017). Flipped Classroom with Problem Based Activities: Exploring Self-regulated Learning in a Programming Language Course. Journal of Educational Technology & Society, 20(1), 337–349. http://www.jstor.org/stable/jeductechsoci.20.1.337
Cecchini, J. A, Fernandez-Rio, J, Mendez-Gimenez, A, Gonzalez, C., Sanchez-Martínez, B, & Carriedo, A. (2020). High versus low-structured cooperative learning. Effects on prospective teachers’ regulation dominance, motivation, content knowledge and responsibility. Journal of Teacher Education, 44(4), 486–501. https://doi.org/10.1080/02619768.2020.1774548
Cheah, C. S. (2020). Factors Contributing to the Difficulties in Teaching and Learning of Computer Programming: A Literature Review. Contemporary Educational Technology, 12(2). https://doi.org/10.30935/cedtech/8247
Coffield, F., Ecclestone, K., Hall, E., & Moseley, D. (2004). Learning styles and pedagogy in post-16 learning: A systematic and critical review. Learning and Skills Research Centre.
Creswell, J. W. (2014). A concise introduction to mixed methods research. SAGE.
Dakhel, A. M, Majdinasab, V, Nikanjam, A., Khomh, F, Desmarais, M. C, & Jiang, Z. M. J. (2023). Github copilot AI pair programmer: Asset or liability?. Journal of Systems and Software, 203.
Dasgupta, S., & Hill, B. M. (2017). Learning to code in localized programming languages. Proceedings of the Fourth (2017) ACM Conference on Learning (pp.33–39). https://doi.org/10.1145/3051457.3051464
De Boer, A. L, Du Toit, P, Scheepers, D., & Bothma, T. (2013). Whole Brain® Learning in higher education. Evidence-based practice. Elsevier.
Denner, J., Werner, L., & Ortiz, E. (2012). Computer games created by middle school girls: Can they be used to measure understanding of computer science concepts? Computers & Education, 58(1), 240–249. https://doi.org/10.1016/j.compedu.2011.08.006
Du Toit, P. (2009). An action research approach to monitoring one’s professional development as manager. Foundation for Professional Development.
Elfiky, D. E. G. (2022). The Effect of a Whole Brain Teaching Based Instruction on Developing Number Competencies and Arithmetic Fluency in Kindergarten Children. International Journal of Instruction, 15(1), 673–684. https://doi.org/10.29333/iji.2022.15138a
Entwistle, N., Hughes, J. C., & Mighty, J. (2010). Taking stock: An overview of research findings. Research on Teaching and Learning in Higher Education, 15–51.
Entwistle, N., McCune, V., & Hounsell, J. (2002). Approaches to studying and perceptions of university teaching-learning environments: Concepts, measures and preliminary findings (No. Occasional report; 1, pp. 1–9).
Garcia, M. B. (2021). Cooperative learning in computer programming: A quasi-experimental evaluation of Jigsaw teaching strategy with novice programmers. Education and Information Technologies, 26(4), 4839–4856. https://doi.org/10.1007/s10639-021-10502-6
Ghavifekr, S. (2020). Collaborative Learning: A Key to Enhance Students’ Social Interaction Skills. MOJES: Malaysian Online Journal of Educational Sciences, 8(4), 9–21.
Grover, S., Pea, R., & Cooper, S. (2015). Designing for deeper learning in a blended computer science course for middle school students. Computer Science Education, 25(2), 199–237. https://doi.org/10.1080/08993408.2015.1033142
Hawlitschek, A., Köppen, V., Dietrich, A., & Zug, S. (2020). Drop-out in programming courses–prediction and prevention. Journal of Applied Research in Higher Education, 12(1), 124–136. http://dx.doi.org/10.1108/JARHE-02-2019-0035
Healy, M., Doran, J., & McCutcheon, M. (2018). Cooperative learning outcomes from cumulative experiences of group work: Differences in student perceptions. Accounting Education, 27(3), 286–308. http://dx.doi.org/10.1080/09639284.2018.1476893
Herrera-Pavo, M. Á. (2021). Collaborative learning for virtual higher education. Learning, Culture and Social Interaction, 28, 100437. https://doi.org/10.1016/j.lcsi.2020.100437
Herrmann, N. (1995). The creative brain. Quebecor Printing Book Group.
Herrmann, N. (1996). The whole brain business book. McGraw Hill
Hou, X., Ericson, B. J., & Wang, X. (2022). Using adaptive parsons problems to scaffold write-code problems. ACM Conference on International Computing Education Research, 15–26. http://dx.doi.org/10.1145/3501385.3543977
Isong, B. (2014). A Methodology for Teaching Computer Programming: First year students’ perspective. International Journal of Modern Education and Computer Science, 6(9), 15. https://doi.org/10.5815/ijmecs.2014.09.03
Jakovljevic, M. (2003). Concept mapping and appropriate instructional strategies in promoting programming skills of holistic learners. 308–315. https://dl.acm.org/doi/10.5555/954014.954048
Johnson, D. W., & Johnson, R. T. (2018). Cooperative learning: The foundation for active learning. Active Learning—Beyond the Future, 59–71. https://doi.org/10.5772/intechopen.81086
Johnson, D. W., & Johnson, R. T. (2021). Learning together and alone: The history of our involvement in cooperative learning. In Pioneering perspectives in cooperative learning (pp. 44–62). Routledge.
Kim, C., Vasconcelos, L., Belland, B. R., Umutlu, D., & Gleasman, C. (2022). Debugging behaviors of early childhood teacher candidates with or without scaffolding. International Journal of Educational Technology in Higher Education, 19(1), 26. http://dx.doi.org/10.1186/s41239-022-00319-9
Kim, N. J., Belland, B. R., Lefler, M., Andreasen, L., Walker, A., & Axelrod, D. (2020). Computer-based scaffolding targeting individual versus groups in problem-centered instruction for STEM education: Meta-analysis. Educational Psychology Review, 32, 415–461. https://doi.org/10.1007/s10648-019-09502-3
Kirstein, M., & Kunz, R. (2016). A whole brain® learning approach to an undergraduate auditing initiative–An exploratory study. Meditari Accountancy Research, 24(4), 527–544. https://doi.org/10.1108/MEDAR-02-2014-0029
Klein, H. K., & Myers, M. D. (1999). A set of principles for conducting and evaluating interpretive field studies in information systems. MIS Quarterly, 23(1), 67–93. https://doi.org/10.2307/249410
Koulouri, T., Lauria, S., & Macredie, R. D. (2016). Do (and say) as I say: Linguistic adaptation in human–computer dialogs. Human–Computer Interaction, 31(1), 59–95. https://doi.org/10.1080/07370024.2014.934180
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
Le Roux, I. (2011). New large class pedagogy: Developing students’ whole brain thinking skills. Procedia-Social and Behavioral Sciences, 15, 426–435. https://doi.org/10.1016/j.sbspro.2011.03.116
Lee, D., Morrone, A. S., & Siering, G. (2018). From swimming pool to collaborative learning studio: Pedagogy, space, and technology in a large active learning classroom. Educational Technology Research and Development, 66, 95–127. https://doi.org/10.1007/s11423-017-9550-1
Lee, H., Han, Y.T, & Chen, M.P. (2010). The effect of scaffolding support on programming performance and the use of self-regulation in learning computer programming. Enhancing and Sustaining New Knowledge Through the Use of Digital Technology in Education, 517–524. https://doi.org/10.1108/ITSE-04-2021-0074
Lubarda, M., Phan, A., Schurgers, C., Delson, N., Ghazinejad, M., Baghdadchi, S., Minnes, M., Kim, M., Pilegard, C., Relaford-Doyle, J., Sandoval, C. L., & Qi, H. (2024). Virtual pair programming and online oral exams: Effects on social interaction, performance, and academic integrity in a remote computer programming course. Computer Science Education, 1–41. https://doi.org/10.1080/08993408.2024.2344401
Luna Scott, C. (2015). The futures of learning 2: What kind of learning for the 21st century? UNESCO.
Ma, Q., Wu, T., & Koedinger, K. (2023). Is AI the better programming partner? Human-human pair programming vs. Human-ai pair programming. arXiv Preprint arXiv:2306.05153.
Malliarakis, C., Satratzemi, M., & Xinogalos, S. (2013). A holistic framework for the development of an educational game aiming to teach computer programming. University of Macedonia, 359–369.
Margulieux, L. E., & Catrambone, R. (2021). Scaffolding problem solving with learners’ own self explanations of subgoals. Journal of Computing in Higher Education, 33(2), 499–523. https://doi.org/10.1007/s12528-021-09275-1
McLoughlin, C., & Lee, M. J. (2008). The three p’s of pedagogy for the networked society: Personalization, participation, and productivity. International Journal of Teaching and Learning in Higher Education, 20(1), 10–27.
Medeiros, R. P., Ramalho, G. L., & Falcão, T. P. (2018). A systematic literature review on teaching and learning introductory programming in higher education. IEEE Transactions on Education, 62(2), 77–90. https://doi.org/10.1109/TE.2018.2864133
Näykki, P., Isohätälä, J., & Järvelä, S. (2021). “You really brought all your feelings out”–Scaffolding students to identify the socio-emotional and socio-cognitive challenges in collaborative learning. Learning, Culture and Social Interaction, 30, 100536. https://doi.org/10.1016/j.lcsi.2021.100536
Ngozo, B. P. (2012). Dynamics of learning style flexibility in teaching and learning. University of Pretoria (South Africa).
Olsson, J., & Granberg, C. (2024). Teacher-student interaction supporting students’ creative mathematical reasoning during problem solving using Scratch. Mathematical Thinking and Learning, 26(3), 278–305. https://doi.org/10.1080/10986065.2022.2105567
Opdecam, E., Everaert, P., Van Keer, H., & Buysschaert, F. (2014). Preferences for team learning and lecture-based learning among first-year undergraduate accounting students. Research in Higher Education, 55, 400–432. https://doi.org/10.1007/s11162-013-9315-6
Pritchard, A. (2017). Ways of learning: Learning theories for the classroom. Routledge.
Qureshi, M. A., Khaskheli, A., Qureshi, J. A., Raza, S. A., & Yousufi, S. Q. (2023). Factors affecting students’ learning performance through collaborative learning and engagement. Interactive Learning Environments, 31(4), 2371–2391. https://doi.org/10.1080/10494820.2021.1884886
Saka, A. O. (2020). Learning to Write Programs using Think-Pair-Share Programming Strategy: What are the Students’ Perceptions and Experiences? Journal of Educational Sciences, 4(4), 705–717. https://doi.org/10.31258/jes.4.4.p.705-717
Santoso, D. (2016). Improving the Students’ Spiritual Intelligence in English Writing through Whole Brain Learning. English Language Teaching, 9(4), 230–238. https://doi.org/10.5539/elt.v9n4p230
Schunk, D. H. (2020). Learning theories: An educational perspective. Pearson.
Sharma, H. L., & Saarsar, P. (2018). TPS (think-pair–share): An effective cooperative learning strategy for unleashing discussion in classroom interaction. International Journal of Research in Social Sciences, 8(5), 91–100.
Shin, D. D., Lee, M., & Bong, M. (2022). Beyond left and right: Learning is a whole-brain process. Theory into Practice, 61(3), 347–357. https://doi.org/10.1080/00405841.2022.2096386
Simon, B., & Hanks, B. (2008). First-year students’ impressions of pair programming in CS1. Journal on Educational Resources in Computing, 7(4), 1–28. https://doi.org/10.1145/1316450.1316455
Sontillano, R. D. (2018). Impact of whole brain teaching based instruction on academic performance of grade 8 students in Algebra: Compendium of WBT-based lesson plans. International Journal of Teaching, Education and Learning, 2(2), 98–114. https://doi.org/10.20319/pijtel.2018.22.98114
Su, Y.-S., Wang, S., & Liu, X. (2024). Using Epistemic Network Analysis to Explore Primary School Students’ Computational Thinking in Pair Programming Learning. Journal of Educational Computing Research, 62(2), 559–593. https://doi.org/10.1177/07356331231210560
Tan, S. C., Lee, A. V. Y., & Lee, M. (2022). A systematic review of artificial intelligence techniques for collaborative learning over the past two decades. Computers and Education: Artificial Intelligence, 3, 100097. https://doi.org/10.1016/j.caeai.2022.100097
Thota, N., & Whitfield, R. (2010). Holistic approach to learning and teaching introductory object-oriented programming. Computer Science Education, 20(2), 103–127. https://doi.org/10.1080/08993408.2010.486260
Tijani, F., Callaghan, R., & de Villers, R. (2020). An investigation into pre-service teachers’ experiences while transitioning from Scratch programming to procedural programming. African Journal of Research in Mathematics, Science and Technology Education, 24(2), 266–278. https://doi.org/10.1080/18117295.2020.1820798
Tikva, C., & Tambouris, E. (2023). The effect of scaffolding programming games and attitudes towards programming on the development of Computational Thinking. Education and Information Technologies, 28(6), 6845–6867. https://doi.org/10.1007/s10639-022-11465-y
Topalli, D., & Cagiltay, N. E. (2018). Improving programming skills in engineering education through problem-based game projects with Scratch. Computers & Education, 120, 64–74. https://doi.org/10.1016/j.compedu.2018.01.011
Van Laar, E., Van Deursen, A. J., Van Dijk, J. A., & De Haan, J. (2020). Determinants of 21st-century skills and 21st-century digital skills for workers: A systematic literature review. Sage Open, 10(1), 2158244019900176. http://dx.doi.org/10.1177/2158244019900176
Warsah, I., Morganna, R., Uyun, M., Afandi, M., & Hamengkubuwono, H. (2021). The impact of collaborative learning on learners’ critical thinking skills. International Journal of Instruction, 14(2), 443–460. https://doi.org/10.29333/iji.2021.14225a
Watkins, K. Z., & Watkins, M. J. (2009). Towards minimizing pair incompatibilities to help retain under-represented groups in beginning programming courses using pair programming. Journal of Computing Sciences in Colleges, 25(2), 221–227.
Yurdugül, H., & Aşkar, P. (2013). Learning programming, problem solving and gender: A longitudinal study. Procedia-Social and Behavioral Sciences, 83, 605–610. https://doi.org/10.1016/j.sbspro.2013.06.115
Zhang, J.-H., Meng, B., Zou, L.-C., Zhu, Y., & Hwang, G.-J. (2023). Progressive flowchart development scaffolding to improve university students’ computational thinking and programming self-efficacy. Interactive Learning Environments, 31(6), 3792–3809. http://dx.doi.org/10.1080/10494820.2021.1943687
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