Research Article
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Year 2022, Volume: 9 Issue: 2, 118 - 135, 01.03.2022
https://doi.org/10.17275/per.22.32.9.2

Abstract

References

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  • Benchouia, N. E., Elias, H. A., Khochemane, L., & Mahmah, B. (2014). Bond graph modeling approach development for fuel cell PEMFC systems. International Journal of Hydrogen Energy, 15224-15231. https://doi.org/10.1016/j.ijhydene.2014.05.034
  • Bhalerao, P. (2017). Use of graph theory for applications, representations, modeling and problem solving in mathematics and other fields. International Journal of Scientific and Engineering Research. 8(10), 847–851.
  • Bobis, J., Clarke, B., Clarke, D., Thomas, G., Wright, R. B., Young-Loveridge, J., & Gould, P. (2005). Supporting teachers in the development of young children’s mathematical thinking: Three large scale cases. Mathematics Education Research Journal, 16(3), 27-57.
  • Burrus, J., Jackson, T., Xi, N., & Steinberg, J. (2013). Identifying the most important 21st century workforce competencies: An analysis of the Occupational Information Network (O*NET). ETS Research Report Series, 2013(2), i-55. https://doi.org/10.1002/j.2333-8504.2013.tb02328.x
  • Cai, J., Lew, H. C., Morris, A., Moyer, J. C., Ng, S. F., & Schmittau, J. (2005). The development of studients’ algebraic thinking in earlier grades: Zentralblatt Für Didaktik Der Mathematik, 37(1), 5-15. https://doi.org/10.1007/bf02655892
  • Căprioară, D. (2015). Problem solving-purpose and means of learning mathematics in school. Procedia-social and behavioral sciences, 191, 1859-1864. https://doi.org/10.1016/j.sbspro.2015.04.332
  • Castellanos, J. L. V., Castro, E., & Gutiérrez, J. (2009). Representations In Problem Solving: A Case Study With Optimization Problems. Electronic Journal of Research in Educational Psychology.
  • Charlesworth, R., & Leali, S. A. (2012). Using problem solving to assess young children’s mathematics knowledge. Early Childhood Education Journal, 39(6), 373-382. https://doi.org/10.1007/s10643-011-0480-y
  • Creswell, J. W. & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches. California: SAGE Publications, Inc.
  • Csíkos, C., Szitányi, J., & Kelemen, R. (2012). The effects of using drawings in developing young children’s mathematical word problem solving: A design experiment with third-grade Hungarian students. Educational Studies in Mathematics, 81(1), 47-65. https://doi.org/10.1007/s10649-011-9360-z
  • Cvetković, D., & Simić, S. (2011). Graph spectra in computer science. Linear Algebra and its Applications, 434(6), 1545-1562. https://doi.org/10.1016/j.laa.2010.11.035
  • Dafik, Agustin, I. H., Alfarisi, R., & Kurniawati, E. Y. (2020). Integrating a graph theory in a school math curriculum of indonesia under realistic mathematics education. International Journal of Scientific and Technology Research, 9(01), 2437–2445.
  • Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., & Osher, D. (2020). Implications for educational practice of the science of learning and development. Applied Developmental Science, 24(2), 97-140. https://doi.org/10.1080/10888691.2018.1537791
  • Das, B., Mukherjee, V., & Das, D. (2020). Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems. Advances in Engineering Software, 146, 1-17. https://doi.org/10.1016/j.advengsoft.2020.102804
  • Düşek, G., & Ayhan, A. B. (2014). A study on problem solving skills of the children from broken family and full parents family attending regional primary boarding school. Procedia-Social and Behavioral Sciences, 152, 137-142. https://doi.org/10.1016/j.sbspro.2014.09.170
  • Elia, I., van den Heuvel-Panhuizen, M., & Kolovou, A. (2009). Exploring strategy use and strategy flexibility in non-routine problem solving by primary school high achievers in mathematics. ZDM Mathematics Education. 41(5). 605-618. https://doi.org/10.1007/s11858-009-0184-6
  • English, L. D., & Gainsburg, J. (2015). Problem solving in a 21st-century mathematics curriculum. In L. D. English & D. Kirshner (Eds.), Handbook of international research in mathematics education (3rd ed.) (313-335). New York: Routledge. https://doi.org/10.4324/9780203448946
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  • Gao, W., Wu, H., Siddiqui, M. K., & Baig, A. Q. (2018). Study of biological networks using graph theory. Saudi Journal of Biological Sciences, 25(6), 1212-1219. https://doi.org/10.1016/j.sjbs.2017.11.022
  • Garroway, C. J., Bowman, J., Carr, D., & Wilson, P. J. (2008). Applications of graph theory to landscape genetics. Evolutionary Applications, 1(4), 620-630. https://doi.org/10.1111/j.1752-4571.2008.00047.x
  • Goulet-Lyle, M. P., Voyer, D., & Verschaffel, L. (2020). How does imposing a step-by-step solution method impact students’ approach to mathematical word problem solving?. ZDM Mathematics Education, 52, 139–149. https://doi.org/10.1007/s11858-019-01098-w
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  • Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75-86. https://doi.org/10.1207/s15326985ep4102_1
  • Lanjewar, P. B., Rao, R. V., & Kale, A. V. (2015). Assessment of alternative fuels for transportation using a hybrid graph theory and analytic hierarchy process Method. Fuel, 154, 9-16. https://doi.org/10.1016/j.fuel.2015.03.062
  • Lin, C. S., Tzeng, G. H., & Chin, Y. C. (2011). Combined rough set theory and flow network graph to predict customer churn in credit card accounts. Expert Systems with Applications, 38(1), 8-15. https://doi.org/10.1016/j.eswa.2010.05.039
  • Lockwood, E., Asay, A., DeJarnette, A. F., & Thomas, M. (2016). Algorithmic thinking: An initial characterization of computational thinking in mathematics. Proceedings of the 38th Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education. Tucson, AZ: The University of Arizona.
  • Mason, J., Burton, L., & Stacey, K. (2010). Thinking mathematically (2nd ed.). Essex: Pearson Education Limited.
  • Mason, J., & Johnson-Wilder, S. (2004). Designing and using mathematical tasks. UK: Tarquin.
  • Mears, D., & Pollard, H. B. (2016). Network science and the human brain: Using graph theory to understand the brain and one of its hubs, the amygdala, in health and disease. Journal of Neuroscience Research. 94(6), 590-605. https://doi.org/10.1002/jnr.23705
  • Medová, J., Páleníková, K., Rybanský, L., & Naštická, Z. (2019). Undergraduate students’ solutions of modeling problems in algorithmic graph theory. Mathematics, 7(7), 1–16. https://doi.org/10.3390/math7070572
  • Nabiyev, V. V., Çakiroğlu, U., Karal, H., Erümit, A. K., & Çebi, A. (2016). Application of graph theory in an intelligent tutoring system for solving mathematical word problems. Eurasia Journal of Mathematics, Science and Technology Education, 12(4), 687–701. https://doi.org/10.12973/eurasia.2015.1401a
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  • Pérez, A. (2018). A framework for computational thinking dispositions in mathematics education. Journal for Research in Mathematics Education, 49(4), 424-461. https://doi.org/10.5951/jresematheduc.49.4.0424
  • Peters, J., & Metz, D. (2015). Using graph theory to understand first nations connections. The Mathematics Teacher, 109(4), 311-313. https://doi.org/10.5951/mathteacher.109.4.0311
  • Phillips, J. D., Schwanghart, W., & Heckmann, T. (2015). Graph theory in the geosciences. Earth-Science Reviews, 143, 147-160. https://doi.org/10.1016/j.earscirev.2015.02.002
  • Pongsakdi, N., Kajamies, A., Veermans, K., Lertola, K., Vauras, M., & Lehtinen, E. (2020). What makes mathematical word problem solving challenging? Exploring the roles of word problem characteristics, text comprehension, and arithmetic skills. ZDM Mathematics Education, 52(1), 33–44. https://doi.org/10.1007/s11858-019-01118-9
  • Poyias, K., & Tuosto, E. (2015). A design-by-contract approach to recover the architectural style from run-time misbehaviour. Science of Computer Programming, 100, 2-27. https://doi.org/10.1016/j.scico.2014.10.005
  • Puncreobutr, V. (2016). Education 4.0: New challenge of learning. St. Theresa Journal of Humanities and Social Sciences, 2(2), 92-97. Šarga, P., Hroncová, D., Čurillaa, M., & Gmiterko, A. (2012). Simulation of electrical system using Bond Graphs and MATLAB/Simulink. Procedia Engineering, 48, 656–664. https://doi.org/10.1016/j.proeng.2012.09.567
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  • Schoenfeld, A. H. (2017). Uses of video in understanding and improving mathematical thinking and teaching. Journal of Mathematics Teacher Education, 20(5), 415-432. https://doi.org/10.1007/s10857-017-9381-3
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Identification of Graph Thinking in Solving Mathematical Problems Naturally

Year 2022, Volume: 9 Issue: 2, 118 - 135, 01.03.2022
https://doi.org/10.17275/per.22.32.9.2

Abstract

This study attempts to describe characteristics of graph thinking in solving a mathematical problem. Three students at the 10th grade of senior-high schools were involved as the subject. The data was collected from the result of an optimization problem task (OPT), video recording, interviews, and field notes. The results showed two major characteristics of graph thinking were found in solving the problem. First, students used the concept of graph theory to create a problem modelling. They were able to represent the information given in the problem in the form of graph. Second, students also used the concept of graph theory to create a problem modelling and search algorithm. The problem modelling was created as the students interpreted the problem by making connection between the objects in the form of an adjacency matrix and connectivity. In devising a plan, the students referred to the problem modelling to develop search algorithms. However, the algorithms were not entirely efficient. Some of them required the students to initially describe all answer possibilities. The algorithms constructed by the students referred to sequential and conditional algorithms. This study argues that graph-thinking skill can be developed through a learning process which involves students in the solving of open-ended problem to stimulate ideas of problem solving. By developing graph thinking ability, students will be able to analyse and reason information, express mathematical ideas, and have flexibility in solving a problem. These skills are urgently needed in the 21st century where rapid and continuous changes occur.

References

  • Asghari, N., Shahvarani, A., & Haghighi, A. R. (2012). Graph theory as a tool for teaching mathematical processes. International Journal for Cross-Disciplinary Subjects in Education, 3(2), 731–734. https://doi.org/10.20533/ijcdse.2042.6364.2012.0104
  • Benchouia, N. E., Elias, H. A., Khochemane, L., & Mahmah, B. (2014). Bond graph modeling approach development for fuel cell PEMFC systems. International Journal of Hydrogen Energy, 15224-15231. https://doi.org/10.1016/j.ijhydene.2014.05.034
  • Bhalerao, P. (2017). Use of graph theory for applications, representations, modeling and problem solving in mathematics and other fields. International Journal of Scientific and Engineering Research. 8(10), 847–851.
  • Bobis, J., Clarke, B., Clarke, D., Thomas, G., Wright, R. B., Young-Loveridge, J., & Gould, P. (2005). Supporting teachers in the development of young children’s mathematical thinking: Three large scale cases. Mathematics Education Research Journal, 16(3), 27-57.
  • Burrus, J., Jackson, T., Xi, N., & Steinberg, J. (2013). Identifying the most important 21st century workforce competencies: An analysis of the Occupational Information Network (O*NET). ETS Research Report Series, 2013(2), i-55. https://doi.org/10.1002/j.2333-8504.2013.tb02328.x
  • Cai, J., Lew, H. C., Morris, A., Moyer, J. C., Ng, S. F., & Schmittau, J. (2005). The development of studients’ algebraic thinking in earlier grades: Zentralblatt Für Didaktik Der Mathematik, 37(1), 5-15. https://doi.org/10.1007/bf02655892
  • Căprioară, D. (2015). Problem solving-purpose and means of learning mathematics in school. Procedia-social and behavioral sciences, 191, 1859-1864. https://doi.org/10.1016/j.sbspro.2015.04.332
  • Castellanos, J. L. V., Castro, E., & Gutiérrez, J. (2009). Representations In Problem Solving: A Case Study With Optimization Problems. Electronic Journal of Research in Educational Psychology.
  • Charlesworth, R., & Leali, S. A. (2012). Using problem solving to assess young children’s mathematics knowledge. Early Childhood Education Journal, 39(6), 373-382. https://doi.org/10.1007/s10643-011-0480-y
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  • Csíkos, C., Szitányi, J., & Kelemen, R. (2012). The effects of using drawings in developing young children’s mathematical word problem solving: A design experiment with third-grade Hungarian students. Educational Studies in Mathematics, 81(1), 47-65. https://doi.org/10.1007/s10649-011-9360-z
  • Cvetković, D., & Simić, S. (2011). Graph spectra in computer science. Linear Algebra and its Applications, 434(6), 1545-1562. https://doi.org/10.1016/j.laa.2010.11.035
  • Dafik, Agustin, I. H., Alfarisi, R., & Kurniawati, E. Y. (2020). Integrating a graph theory in a school math curriculum of indonesia under realistic mathematics education. International Journal of Scientific and Technology Research, 9(01), 2437–2445.
  • Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., & Osher, D. (2020). Implications for educational practice of the science of learning and development. Applied Developmental Science, 24(2), 97-140. https://doi.org/10.1080/10888691.2018.1537791
  • Das, B., Mukherjee, V., & Das, D. (2020). Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems. Advances in Engineering Software, 146, 1-17. https://doi.org/10.1016/j.advengsoft.2020.102804
  • Düşek, G., & Ayhan, A. B. (2014). A study on problem solving skills of the children from broken family and full parents family attending regional primary boarding school. Procedia-Social and Behavioral Sciences, 152, 137-142. https://doi.org/10.1016/j.sbspro.2014.09.170
  • Elia, I., van den Heuvel-Panhuizen, M., & Kolovou, A. (2009). Exploring strategy use and strategy flexibility in non-routine problem solving by primary school high achievers in mathematics. ZDM Mathematics Education. 41(5). 605-618. https://doi.org/10.1007/s11858-009-0184-6
  • English, L. D., & Gainsburg, J. (2015). Problem solving in a 21st-century mathematics curriculum. In L. D. English & D. Kirshner (Eds.), Handbook of international research in mathematics education (3rd ed.) (313-335). New York: Routledge. https://doi.org/10.4324/9780203448946
  • Foltête, J. C., Girardet, X., & Clauzel, C. (2014). A methodological framework for the use of landscape graphs in land-use planning. Landscape and Urban Planning, 124, 140–150. https://doi.org/10.1016/j.landurbplan.2013.12.012
  • Gao, W., Wu, H., Siddiqui, M. K., & Baig, A. Q. (2018). Study of biological networks using graph theory. Saudi Journal of Biological Sciences, 25(6), 1212-1219. https://doi.org/10.1016/j.sjbs.2017.11.022
  • Garroway, C. J., Bowman, J., Carr, D., & Wilson, P. J. (2008). Applications of graph theory to landscape genetics. Evolutionary Applications, 1(4), 620-630. https://doi.org/10.1111/j.1752-4571.2008.00047.x
  • Goulet-Lyle, M. P., Voyer, D., & Verschaffel, L. (2020). How does imposing a step-by-step solution method impact students’ approach to mathematical word problem solving?. ZDM Mathematics Education, 52, 139–149. https://doi.org/10.1007/s11858-019-01098-w
  • Hutchinson, J. P., Hartsfield, N., & Ringel, G. (1991). Pearls in graph theory: A comprehensive introduction. New York: Dover Publications, Inc. https://doi.org/10.2307/2324291
  • Katagiri, S. (2004). Mathematical thinking and how to teach it. Tokyo: CRICED, University of Tsukuba.
  • Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75-86. https://doi.org/10.1207/s15326985ep4102_1
  • Lanjewar, P. B., Rao, R. V., & Kale, A. V. (2015). Assessment of alternative fuels for transportation using a hybrid graph theory and analytic hierarchy process Method. Fuel, 154, 9-16. https://doi.org/10.1016/j.fuel.2015.03.062
  • Lin, C. S., Tzeng, G. H., & Chin, Y. C. (2011). Combined rough set theory and flow network graph to predict customer churn in credit card accounts. Expert Systems with Applications, 38(1), 8-15. https://doi.org/10.1016/j.eswa.2010.05.039
  • Lockwood, E., Asay, A., DeJarnette, A. F., & Thomas, M. (2016). Algorithmic thinking: An initial characterization of computational thinking in mathematics. Proceedings of the 38th Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education. Tucson, AZ: The University of Arizona.
  • Mason, J., Burton, L., & Stacey, K. (2010). Thinking mathematically (2nd ed.). Essex: Pearson Education Limited.
  • Mason, J., & Johnson-Wilder, S. (2004). Designing and using mathematical tasks. UK: Tarquin.
  • Mears, D., & Pollard, H. B. (2016). Network science and the human brain: Using graph theory to understand the brain and one of its hubs, the amygdala, in health and disease. Journal of Neuroscience Research. 94(6), 590-605. https://doi.org/10.1002/jnr.23705
  • Medová, J., Páleníková, K., Rybanský, L., & Naštická, Z. (2019). Undergraduate students’ solutions of modeling problems in algorithmic graph theory. Mathematics, 7(7), 1–16. https://doi.org/10.3390/math7070572
  • Nabiyev, V. V., Çakiroğlu, U., Karal, H., Erümit, A. K., & Çebi, A. (2016). Application of graph theory in an intelligent tutoring system for solving mathematical word problems. Eurasia Journal of Mathematics, Science and Technology Education, 12(4), 687–701. https://doi.org/10.12973/eurasia.2015.1401a
  • NAEYC & NCTM. (2002). Early childhood mathematics: Promoting good beginnings. Washington, DC: National Association for the Education of Young Children. http://naeyc.org/about/positions/pdf/psmath.pdf.
  • NCTM. (2000). Principles and Standards for School Mathematics Overview. https://www.nctm.org/uploadedFiles/Standards_and_Positions/PSSM_ExecutiveSummary.pdf
  • Nelson, Q., Steffensmeier, D., & Pawaskar, S. (2018). A simple approach for sustainable transportation systems in smart cities: A graph theory model. 2018 IEEE Conference on Technologies for Sustainability (SusTech), 2018, 1-5. https://doi.org/10.1109/SusTech.2018.8671384
  • Niman, J. (1975). Graph theory in the elementary school. Education Studies in Mathematics, 6(3), 351–373. http://www.jstor.org/stable/3481932
  • Pérez, A. (2018). A framework for computational thinking dispositions in mathematics education. Journal for Research in Mathematics Education, 49(4), 424-461. https://doi.org/10.5951/jresematheduc.49.4.0424
  • Peters, J., & Metz, D. (2015). Using graph theory to understand first nations connections. The Mathematics Teacher, 109(4), 311-313. https://doi.org/10.5951/mathteacher.109.4.0311
  • Phillips, J. D., Schwanghart, W., & Heckmann, T. (2015). Graph theory in the geosciences. Earth-Science Reviews, 143, 147-160. https://doi.org/10.1016/j.earscirev.2015.02.002
  • Pongsakdi, N., Kajamies, A., Veermans, K., Lertola, K., Vauras, M., & Lehtinen, E. (2020). What makes mathematical word problem solving challenging? Exploring the roles of word problem characteristics, text comprehension, and arithmetic skills. ZDM Mathematics Education, 52(1), 33–44. https://doi.org/10.1007/s11858-019-01118-9
  • Poyias, K., & Tuosto, E. (2015). A design-by-contract approach to recover the architectural style from run-time misbehaviour. Science of Computer Programming, 100, 2-27. https://doi.org/10.1016/j.scico.2014.10.005
  • Puncreobutr, V. (2016). Education 4.0: New challenge of learning. St. Theresa Journal of Humanities and Social Sciences, 2(2), 92-97. Šarga, P., Hroncová, D., Čurillaa, M., & Gmiterko, A. (2012). Simulation of electrical system using Bond Graphs and MATLAB/Simulink. Procedia Engineering, 48, 656–664. https://doi.org/10.1016/j.proeng.2012.09.567
  • Schoenfeld, A. H. (2016). Learning to think mathematically: Problem solving, metacognition, and sense making in mathematics (Reprint). Journal of Education, 196(2), 1-38. https://doi.org/10.1177/002205741619600202
  • Schoenfeld, A. H. (2017). Uses of video in understanding and improving mathematical thinking and teaching. Journal of Mathematics Teacher Education, 20(5), 415-432. https://doi.org/10.1007/s10857-017-9381-3
  • Singh, R. P., & Vandana, V. (2014). Application of graph theory in computer science and engineering. International Journal of Computer Applications, 104(1), 10-13. https://doi.org/10.5120/18165-9025
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There are 65 citations in total.

Details

Primary Language English
Subjects Other Fields of Education
Journal Section Research Articles
Authors

Anggar Titis Prayitno 0000-0002-3444-8917

Toto Nusantara 0000-0003-1116-9023

Erry Hidayanto 0000-0001-9412-0799

Swasono Rahardjo 0000-0002-3277-3935

Publication Date March 1, 2022
Acceptance Date July 18, 2021
Published in Issue Year 2022 Volume: 9 Issue: 2

Cite

APA Prayitno, A. T., Nusantara, T., Hidayanto, E., Rahardjo, S. (2022). Identification of Graph Thinking in Solving Mathematical Problems Naturally. Participatory Educational Research, 9(2), 118-135. https://doi.org/10.17275/per.22.32.9.2