Modelling School Zone Border as Rich Modelling Problem for Secondary School Students

Gede Suweken

doi:10.28991/ESJ-2024-08-05-019

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Gede Suweken
gede.suweken@undiksha.ac.id
Ganesha University of Education, Jl. Udayana No.11 Singajara, Bali,, Indonesia

Vol. 8 No. 5 (2024): October

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This article explores the potential of using the school zoning problem in Indonesia as a vehicle for teaching mathematical modeling to secondary school students. This problem is highly suitable for students as a modeling challenge because it is (i) contextual, (ii) rich, (iii) challenging, and (iv) within students' Zone of Proximal Development (ZPD). School zoning involves a concept called Voronoi, essentially a partitioning problem. For simpler or special-case problems, these partitions can be created using concepts already taught in secondary schools, such as perpendicular bisectors and radical axes. However, for more complex problems with multiple sites, an algorithm is required, which involves advanced mathematical concepts beyond the typical secondary curriculum. Yet, with the rise of visual programming languages like Scratch, Snap!, StarLogo, and TurboWarp, it becomes possible to tackle these partitioning challenges using coding and only basic mathematical principles. This approach not only enhances students' understanding of foundational mathematical concepts but also fosters the integration of computational thinking and coding within mathematics. In summary, the school zoning problem serves as an ideal topic for mathematical modeling for secondary school students, promoting the integration of mathematical concepts, computational thinking, and coding skills.

Doi: 10.28991/ESJ-2024-08-05-019

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Ma, Y., & Qin, X. (2021). Measurement invariance of information, communication and technology (ICT) engagement and its relationship with student academic literacy: Evidence from PISA 2018. Studies in Educational Evaluation, 68. doi:10.1016/j.stueduc.2021.100982.

Triatmanto, B., & Bawono, S. (2023). The interplay of corruption, human capital, and unemployment in Indonesia: Implications for economic development. Journal of Economic Criminology, 2, 100031. doi:10.1016/j.jeconc.2023.100031.

Dursun, B., Cesur, R., & Mocan, N. (2018). The Impact of Education on Health Outcomes and Behaviors in a Middle-Income, Low-Education Country. Economics and Human Biology, 31, 94–114. doi:10.1016/j.ehb.2018.07.004.

Dida, S., Hafiar, H., Kadiyono, A. L., & Lukman, S. (2021). Gender, education, and digital generations as determinants of attitudes toward health information for health workers in West Java, Indonesia. Heliyon, 7(1), e05916. doi:10.1016/j.heliyon.2021.e05916.

Suhariadi, F., Sugiarti, R., Hardaningtyas, D., Mulyati, R., Kurniasari, E., Saadah, N., Yumni, H., & Abbas, A. (2023). Work from home: A behavioral model of Indonesian education workers' productivity during Covid-19. Heliyon, 9(3), e14082. doi:10.1016/j.heliyon.2023.e14082.

Thorbecke, W. (2023). Sectoral evidence on Indonesian economic performance after the pandemic. Asia and the Global Economy, 3(2), 100069. doi:10.1016/j.aglobe.2023.100069.

Beatty, A., Berkhout, E., Bima, L., Pradhan, M., & Suryadarma, D. (2021). Schooling progress, learning reversal: Indonesia's learning profiles between 2000 and 2014. International Journal of Educational Development, 85. doi:10.1016/j.ijedudev.2021.102436.

Lopus, J. S., Amidjono, D. S., & Grimes, P. W. (2019). Improving financial literacy of the poor and vulnerable in Indonesia: An empirical analysis. International Review of Economics Education, 32. doi:10.1016/j.iree.2019.100168.

Bicer, A., Aleksani, H., Butler, C., Jackson, T., Smith, T. D., & Bostick, M. (2024). Mathematical creativity in upper elementary school mathematics curricula. Thinking Skills and Creativity, 51. doi:10.1016/j.tsc.2024.101462.

Mukuka, A., Balimuttajjo, S., & Mutarutinya, V. (2023). Teacher efforts towards the development of students' mathematical reasoning skills. Heliyon, 9(4), e14789. doi:10.1016/j.heliyon.2023.e14789.

Ding, H., & Homer, M. (2020). Interpreting mathematics performance in PISA: Taking account of reading performance. International Journal of Educational Research, 102, 101566. doi:10.1016/j.ijer.2020.101566.

Pokropek, A., Marks, G. N., Borgonovi, F., Koc, P., & Greiff, S. (2022). General or specific abilities? Evidence from 33 countries participating in the PISA assessments. Intelligence, 92. doi:10.1016/j.intell.2022.101653.

Arsaythamby, V., & Zubainur, C. M. (2014). How a Realistic Mathematics Educational Approach Affect Students' Activities in Primary Schools? Procedia - Social and Behavioral Sciences, 159, 309–313. doi:10.1016/j.sbspro.2014.12.378.

Zulnaidi, H., Mafarja, N., Rahim, S. S. A., & Salleh, U. K. M. (2024). Ethical mediation: The influence of mathematics teachers cooperation on readiness for the industrial revolution era in Indonesia and Malaysia. Acta Psychologica, 243. doi:10.1016/j.actpsy.2024.104151.

Bragelman, J., Amador, J. M., & Superfine, A. C. (2024). The expertise of novices: A framework for prospective teacher's noticing of children's mathematical thinking. Journal of Mathematical Behavior, 74. doi:10.1016/j.jmathb.2024.101151.

McMillan, B. G. (2024). Connecting student development of use of grouping and mathematical properties. Journal of Mathematical Behavior, 74. doi:10.1016/j.jmathb.2024.101154.

Preveraud, T. (2024). The International commission on mathematical instruction, 1908–2008: People, events and challenges in mathematics education. Historia Mathematica, 67, 25–27. doi:10.1016/j.hm.2024.02.007.

Jung, H., & Wickstrom, M. H. (2023). Teachers creating mathematical models to fairly distribute school funding. Journal of Mathematical Behavior, 70. doi:10.1016/j.jmathb.2023.101041.

Struyf, A., De Loof, H., Boeve-de Pauw, J., & Van Petegem, P. (2019). Students' engagement in different STEM learning environments: integrated STEM education as promising practice? International Journal of Science Education, 41(10), 1387–1407. doi:10.1080/09500693.2019.1607983.

Ari, A. A., & Katrancı, Y. (2014). The Opinions of Primary Mathematics Student-teachers on Problem-based Learning Method. Procedia - Social and Behavioral Sciences, 116, 1826–1831. doi:10.1016/j.sbspro.2014.01.478.

Antosz, P., Birks, D., Edmonds, B., Heppenstall, A., Meyer, R., Polhill, J. G., O'Sullivan, D., & Wijermans, N. (2023). What do you want theory for? - A pragmatic analysis of the roles of "theory” in agent-based modelling. Environmental Modelling and Software, 168. doi:10.1016/j.envsoft.2023.105802.

Sulistyowati, F., Hartanti, S., Widodo, S. A., & Putrianti, F. G. (2022). Critical Thinking Skills in Phlegmatic Students Using Learning Videos. Math Educator Nusantara Journal: A Place for Publication of Scientific Papers in the Field of Mathematics Education, 8(2), 119–133. doi:10.29407/jmen.v8i2.18874.

Alac, R., WA Hammad, A., Hadigheh, A., & Opdyke, A. (2023). Optimising egress location in school buildings using mathematical modelling and Agent-Based simulation. Safety Science, 167. doi:10.1016/j.ssci.2023.106265.

Bekker, R. A., Kim, S., Pilon-Thomas, S., & Enderling, H. (2022). Mathematical modeling of radiotherapy and its impact on tumor interactions with the immune system. Neoplasia (United States), 28(C), 1–13. doi:10.1016/j.neo.2022.100796.

Barwell, R., Boylan, M., & Coles, A. (2022). Mathematics education and the living world: A dialogic response to a global crisis. Journal of Mathematical Behavior, 68. doi:10.1016/j.jmathb.2022.101013.

Damyanov, I., & Tsankov, N. (2018). The role of infographics for the development of skills for cognitive modeling in education. International Journal of Emerging Technologies in Learning, 13(1), 82–92. doi:10.3991/ijet.v13i01.7541.

Garfunkel, S., & Montgomery, M. (2019). GAIMME ” Guidelines for Assessment & Instruction in Mathematical Modeling Education. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, United States. doi:10.1137/1.9781611975741

Wilensky, U. (2020). Restructuration Theory and Agent-Based Modeling: Reformulating Knowledge Domains through Computational Representations. Designing Constructionist Futures, MIT Press, 287–300. doi:10.7551/mitpress/12091.003.0037.

McLean, A., McDonald, W., Goodridge, D., & Osgood, N. (2019). Agent-Based Modeling. Nursing Research, 68(6), 473–482. doi:10.1097/nnr.0000000000000390.

Aslan, U., Anton, G., & Wilensky, U. (2019). Bringing Powerful Ideas to Middle School Students' Lives through Agent-Based Modeling. Annual Meeting of the American Educational Research Association (AERA 2019), Toronto, Canada. doi:10.3102/1444571.

Amini, A., & Haughton, M. (2023). A mathematical optimization model for cluster-based single-depot location-routing e-commerce logistics problems. Supply Chain Analytics, 3. doi:10.1016/j.sca.2023.100019.

Elley, W. B. (1976). National assessment of the quality of Indonesian education. Studies in Educational Evaluation, 2(3), 151–166. doi:10.1016/0191-491X(76)90020-1.

Erdogan, F., & Sengul, S. (2014). A Study on the Elementary School Students' Mathematics Self Concept. Procedia - Social and Behavioral Sciences, 152, 596–601. doi:10.1016/j.sbspro.2014.09.249.

Yildiz Durak, H. (2020). The Effects of Using Different Tools in Programming Teaching of Secondary School Students on Engagement, Computational Thinking and Reflective Thinking Skills for Problem Solving. Technology, Knowledge and Learning, 25(1), 179–195. doi:10.1007/s10758-018-9391-y.

Chotimah, S., Wijaya, T. T., Aprianti, E., Akbar, P., & Bernard, M. (2020). Increasing primary school students' reasoning ability on the topic of plane geometry by using hawgent dynamic mathematics software. Journal of Physics: Conference Series, 1657(1), 012009. doi:10.1088/1742-6596/1657/1/012009.

Wilensky, U., & Papert, S. (2010). Restructurations: Reformulations of knowledge disciplines through new representational forms. Constructionism 2010, Paris, France.

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Modelling School Zone Border as Rich Modelling Problem for Secondary School Students

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