Describing the phenomena of students’ representation in solving ill-posed and well-posed problems

Imam Rofiki, Ika Santia

Abstract


Mathematical representation is an essential aspect of mathematical problem-solving. But students’ ability of an accurate representation in ill-posed problem-solving is still very minimal compared to that in well-posed problem-solving. However, ill-posed problem supported mathematical abstraction used in mathematical concept understanding. This study described the representations used by mathematics education students in solving ill-posed and well-posed problems. Thirty Indonesian matematics education students have solved ill-well posed problems by using think-aloud. Researchers also collected data using a video recorder and a field note. Data were analyzed by a constant comparative method so that it was obtained the different characteristics of representations between solving ill-posed and well-posed problems. The finding of the study showed that verbal and symbolic representations were used by subjects to compute, detect, and correct error. They also justified their answers in ill-posed problem-solving. However, the visual representation was only used by first subject to identify and correct error. The subjects lacked to expose necessary information to solve the ill-posed problem compared to the well-posed problem.


Keywords


Representation; Problem-Solving; Ill-Posed Problem; Well-Posed Problem; Mathematical Abstraction

Full Text:

PDF

References


Adu‐Gyamfi, K., Stiff, L. V, & Bossé, M. J. (2012). Lost in translation: Examining translation errors associated with mathematical representations. School Science and Mathematics, 112(3), 159–170.

Bal, A. P. (2014). The examination of representations used by classroom teacher candidates in solving mathematical problems. Educational Sciences: Theory and Practice, 14(6), 2349–2365.

Birgin, O. (2012). Investigation of eighth-grade students’ understanding of the slope of the linear function. Bolema: Boletim de Educação Matemática, 26(42A), 139–162.

Caglayan, G., & Olive, J. (2010). Eighth grade students’ representations of linear equations based on a cups and tiles model. Educational Studies in Mathematics, 74(2), 143–162.

Chi, M. T. H., & Glaser, R. (1985). Problem-solving ability. In R. J. Sternberg (Ed.), Human abilities: An information-processing approach (pp. 227–250). Freeman.

David, M. M., Tomaz, V. S., & Ferreira, M. C. C. (2014). How visual representations participate in algebra classes’ mathematical activity. ZDM, 46(1), 95–107.

De Bock, D., Van Dooren, W., & Verschaffel, L. (2015). Students’ understanding of proportional, inverse proportional, and affine functions: Two studies on the role of external representations. International Journal of Science and Mathematics Education, 13(1), 47–69. https://doi.org/10.1007/s10763-013-9475-z

Dreyfus, T. (1991). Advanced mathematical thinking processes. In D. Tall (Ed.), Advanced mathematical thinking (pp. 25–41). Kluwer Academic Publishers.

Feltovich, P. I., Coulson, R. L., & Peltovich, J. (1996). Complexity, indivitually and in groups. CSCL, Theory and Practice of an Emerging Paradigm, 25, 25–44.

Ge, X., & Land, S. M. (2003). Scaffolding students’ problem-solving processes in an ill-structured task using question prompts and peer interactions. Educational Technology Research and Development, 51(1), 21–38.

Goldin-Meadow, S., & Beilock, S. L. (2010). Action’s influence on thought: The case of gesture. Perspectives on Psychological Science, 5(6), 664–674.

Grégoire, J. (2016). Understanding creativity in mathematics for improving mathematical education. Journal of Cognitive Education and Psychology, 15(1), 24–36.

Hadamard, J. (1923). Lectures on Cauchy’s problems in linear partial differential equations. Yale University Press.

Hwang, W.-Y., Su, J.-H., Huang, Y.-M., & Dung, J.-J. (2009). A study of multi-representation of geometry problem solving with virtual manipulatives and whiteboard system. Educational Technology & Society, 12(3), 229–247.

Jonassen, D. H. (1997). Instructional design models for well-structured and ill-structured problem-solving learning outcomes. Educational Technology Research and Development, 45(1), 65–94.

Jonassen, D. H. (2011). Learning to solve problems: A handbook for designing problem-solving learning environments. Routledge. https://doi.org/10.4324/9780203847527

Kabanikhin, S. I. (2008). Definitions and examples of inverse and ill-posed problems. Journal of Inverse and Ill-Posed Problems, 16(4), 317–357.

Merritt, E. G., Palacios, N., Banse, H., Rimm-Kaufman, S. E., & Leis, M. (2017). Teaching practices in Grade 5 mathematics classrooms with high-achieving English learner students. The Journal of Educational Research, 110(1), 17–31.

NCTM. (2000). Principles and standards for school mathematics. The National Council of Teachers of Mathematics.

Rau, M. A. (2017). Conditions for the effectiveness of multiple visual representations in enhancing STEM learning. Educational Psychology Review, 29(4), 717–761.

Reed, S. K. (2016). The structure of ill-structured (and well-structured) problems revisited. Educational Psychology Review, 28(4), 691–716.

Roche, A., & Clarke, D. M. (2013). Primary teachers’ representations of division: Assessing mathematical knowledge that has pedagogical potential. Mathematics Education Research Journal, 25(2), 257–278.

Rofiki, I. (2015). Penalaran kreatif versus penalaran imitatif. Prosiding SeminarNasional Matematika, 1, 57–62.

Rofiki, I., Nusantara, T., Subanji, & Chandra, T. D. (2017a). Exploring local plausible reasoning: The case of inequality tasks. Journal of Physics: Conference Series, 943(1), 012002. https://doi.org/10.1088/1742-6596/943/1/012002

Rofiki, I., Nusantara, T., Subanji, S., & Chandra, T. D. (2017b). Reflective plausible reasoning in solving inequality problem. IOSR Journal of Research & Method in Education (IOSRJRME), 7(1), 101–112. https://doi.org/10.9790/7388-070101101112

Roschelle, J., Rafanan, K., Bhanot, R., Estrella, G., Penuel, B., Nussbaum, M., & Claro, S. (2010). Scaffolding group explanation and feedback with handheld technology: Impact on students’ mathematics learning. Educational Technology Research and Development, 58(4), 399–419.

Shin, S., & Song, H.-D. (2016). Finding the optimal scaffoldings for learners’ epistemological beliefs during ill-structured problem solving. Interactive Learning Environments, 24(8), 2032–2047.

Shriki, A. (2010). Working like real mathematicians: Developing prospective teachers’ awareness of mathematical creativity through generating new concepts. Educational Studies in Mathematics, 73(2), 159–179.

Sinnott, J. D. (1989). A model for solution of ill-structured problems: Implications for everyday and abstract problem solving. In J. D. Sinnott (Ed.), Everyday problem solving: Theory and application (pp. 72–99). Praeger Publishers.

Stylianou, D. A., & Silver, E. A. (2004). The role of visual representations in advanced mathematical problem solving: An examination of expert-novice similarities and differences. Mathematical Thinking and Learning, 6(4), 353–387.

Villegas, J. L., Castro, E., & Gutiérrez, J. (2009). Representations in problem solving: A case study with optimization problems. Electronic Journal of Research in Educational Psychology, 7(1), 279–308.

Voss, J. F., & Post, T. A. (1988). On the solving of ill-structured problems. In M. H. Chi, R. Glaser, & M. J. Farr (Eds.), The nature of expertise (pp. 261–285). Lawrence Erlbaum Associates, Inc.

Wischgoll, A., Pauli, C., & Reusser, K. (2015). Scaffolding—How can contingency lead to successful learning when dealing with errors? ZDM, 47(7), 1147–1159.

Xun, G. E., & Land, S. M. (2004). A conceptual framework for scaffolding ill-structured problem-solving processes using question prompts and peer interactions. Educational Technology Research and Development, 52(2), 5–22.




DOI: https://doi.org/10.18860/ijtlm.v1i1.5713

Refbacks

  • There are currently no refbacks.




Copyright (c) 2020 International Journal on Teaching and Learning Mathematics

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.


International Journal on Teaching and Learning Mathematics is powered by Open Journal Systems

Department of Mathematics Education
Universitas Islam Negeri Maulana Malik Ibrahim Malang

Information:

Address: Jalan Gajayana 50 Malang, Jawa Timur, Indonesia 65144 
Phone/Fax: (0341) 552398
Website: ejournal.uin-malang.ac.id/index.php/ijtlm
View Stats: Web Analytics View IJTLM Stats
Email: ijtlm@uin-malang.ac.id
p-ISSN: 2621-2188
e-ISSN: 2621-2196

Indexed by : 

 
 

 

International Journal on Teaching and Learning Mathematics is licensed under
a Creative Commons Attribution-ShareAlike 4.0 International License.


To ensure the language quality of the manuscript, the authors have to use a professional proofreading service.