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Jun 27, 2023
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Abstract

Applying a graphing quadratic worksheet as a medium for learning the concept of a Quadratic Function clearer is an alternative instrument to accommodate the needs of developing students' mathematical visual thinking. In implementing graphing quadratic worksheet should show details of the dominant and recessive visual thinking classification aspects that develop in students. Classification of dominant and recessive aspects of visual thinking needs to be completed to determine stages in improving the worksheet and learning instructions that are applied especially to recessive aspects. Therefore, there is a need to evaluate the factors and trace the classification aspects of visual thinking that developed in students after practicing the graphing quadratic worksheet. The purpose of this research is to determine the categorization aspects of visual thinking in graphing quadratic worksheet items that develop and do not develop in students. Confirmatory factor analysis was employed as a research method on 12 sub-variables from the three classifications of visual thinking. As research data, 93 student records were used. Four main factors were formed as a result of the confirmatory factor analysis procedure, with the top two factors, namely factors 1 and 2, completely representing each aspect of the visual thinking classification and achieving the factor loading significance criteria. The implication is that the variables developed in the graphing quadratic worksheet for each aspect of the visual thinking classification have a strong relationship with the visual thinking ability overall. Enhanced by a cumulative variance value for factors 1 and 2 specifically 56.88% of the total 81.78% for all factors. Thereby it can be said that the categorization aspect of visual thinking that develops in students after implementing a graphing quadratic worksheet is achieved sensibly.

Keywords

Confirmatory Factor Analysis Graphing Quadratics Visual Thinking Worksheet Mathematics

Article Details

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

Author Biographies

Rina Oktaviyanthi, Universitas Serang Raya

Department of Mathematics Education

Ria Noviana Agus, Universitas Serang Raya

Department of Mathematics Education

References

  1. Agus, R. N., & Oktaviyanthi, R. (2023). Worksheet graphing quadratics berbantuan PhET simulation untuk optimalisasi mathematical visual thinking mahasiswa [Worksheet graphing quadratics assisted by PhET simulation for optimizing students' mathematical visual thinking]. AKSIOMA: Jurnal Program Studi Pendidikan Matematika, 12(2), 2166-2180.

  2. Alsina, A., Maurandi, A., Ferre, E., & Coronata, C. (2021). Validating an instrument to evaluate the teaching of mathematics through processes. International Journal of Science and Mathematics Education, 19(3), 559-577. https://doi.org/10.1007/s10763-020-10064-y

  3. Alwast, A., & Vorhölter, K. (2022). Measuring pre-service teachers’ noticing competencies within a mathematical modeling context – an analysis of an instrument. Educational Studies in Mathematics, 109(2), 263-285. https://doi.org/10.1007/s10649-021-10102-8

  4. Anmarkrud, Ø., Andresen, A., & Bråten, I. (2019). Cognitive load and working memory in multimedia learning: Conceptual and measurement issues. Educational Psychologist, 54(2), 61-83. https://doi.org/10.1080/00461520.2018.1554484

  5. Arpaci, I. (2019). A hybrid modeling approach for predicting the educational use of mobile cloud computing services in higher education. Computers in human Behavior, 90, 181-187. https://doi.org/10.1016/j.chb.2018.09.005

  6. Asempapa, R. S., & Brooks, G. P. (2022). Factor analysis and psychometric evaluation of the mathematical modeling attitude scale for teachers of mathematics. Journal of Mathematics Teacher Education, 25(2), 131-161. https://doi.org/10.1007/s10857-020-09482-0

  7. Ayebo, A., Bright, J., & Ballam, C. (2019). Examining the factor structure of the survey of attitudes towards statistics among undergraduate health science students. International Electronic Journal of Mathematics Education, 15(1), em0560. https://doi.org/10.29333/iejme/5942

  8. Bjorklund, D. F. (2022). Children′ s thinking: Cognitive development and individual differences. Sage publications.

  9. Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford publications.

  10. Cain, A. J. (2019). Visual thinking and simplicity of proof. Philosophical Transactions of the Royal Society A, 377(2140), 20180032. https://doi.org/10.1098/rsta.2018.0032

  11. Chatfield, C. (2018). Introduction to multivariate analysis. Routledge. https://doi.org/10.1201/9780203749999

  12. Crede, M., & Harms, P. (2019). Questionable research practices when using confirmatory factor analysis. Journal of Managerial Psychology, 34(1), 18-30. https://doi.org/10.1108/JMP-06-2018-0272

  13. Deutsch, H.-P., & Beinker, M. W. (2019). Principal Component Analysis. In H.-P. Deutsch & M. W. Beinker (Eds.), Derivatives and Internal Models: Modern Risk Management (pp. 793-804). Springer International Publishing. https://doi.org/10.1007/978-3-030-22899-6_34

  14. Elsayed, S. A., & Al-Najrani, H. I. (2021). Effectiveness of the augmented reality on improving the visual thinking in mathematics and academic motivation for middle school students. Eurasia Journal of Mathematics, Science and Technology Education, 17(8), em1991. https://doi.org/10.29333/ejmste/11069

  15. Fernández-Fontecha, A., O’Halloran, K. L., Tan, S., & Wignell, P. (2019). A multimodal approach to visual thinking: The scientific sketchnote. Visual Communication, 18(1), 5-29. https://doi.org/10.1177/1470357218759808

  16. Frick, A. (2019). Spatial transformation abilities and their relation to later mathematics performance. Psychological Research, 83(7), 1465-1484. https://doi.org/10.1007/s00426-018-1008-5

  17. Ghazali, N., & Nordin, M. (2019). Measuring meaningful learning experience: Confirmatory factor analysis. International Journal of Innovation, Creativity and Change, 9(12), 283-296.

  18. González-Ramírez, T., & García-Hernández, A. (2021). Design and validation of a questionnaire to assess student satisfaction with mathematics study materials. International Journal of Instruction, 15(1), 1-20. https://doi.org/10.29333/iji.2022.1511a

  19. Hair, J. F., Gabriel, M., & Patel, V. (2014). AMOS covariance-based structural equation modeling (CB-SEM): Guidelines on its application as a marketing research tool. Brazilian Journal of Marketing, 13(2), 44-55. https://doi.org/10.5585/remark.v13i2.2718

  20. Hawes, Z., & Ansari, D. (2020). What explains the relationship between spatial and mathematical skills? A review of evidence from brain and behavior. Psychonomic Bulletin & Review, 27(3), 465-482. https://doi.org/10.3758/s13423-019-01694-7

  21. Heng, L. C., & Said, M. N. H. M. (2020). Effects of digital game-based learning apps based on Mayer’s cognitive theory of multimedia learning in mathematics for primary school students. Innovative Teaching and Learning Journal (ITLJ), 4(1), 65-78.

  22. Hermann, M., & Klein, R. (2015). A visual analytics perspective on shape analysis: State of the art and future prospects. Computers & Graphics, 53, 63-71. https://doi.org/10.1016/j.cag.2015.08.008

  23. Hox, J. J. (2021). Confirmatory Factor Analysis. In J. C. Barnes & D. R. Forde (Eds.), The encyclopedia of research methods in criminology and criminal justice (pp. 830-832). John Wiley & Sons, Inc. https://doi.org/10.1002/9781119111931.ch158

  24. Jiang, D., & Kalyuga, S. (2020). Confirmatory factor analysis of cognitive load ratings supports a two-factor model. The Quantitative Methods for Psychology, 16(3), 216-225. https://doi.org/10.20982/tqmp.16.3.p216 (IN FILE)

  25. Kaplon-Schilis, A., & Lyublinskaya, I. (2020). Analysis of relationship between five domains of TPACK framework: TK, PK, CK math, CK science, and TPACK of pre-service special education teachers. Technology, Knowledge and Learning, 25(1), 25-43. https://doi.org/10.1007/s10758-019-09404-x

  26. Lin, L.-Y. (2019). Differences between preschool children using tablets and non-tablets in visual perception and fine motor skills. Hong Kong Journal of Occupational Therapy, 32(2), 118-126. https://doi.org/10.1177/1569186119888698

  27. Matteson, D. S., & James, N. A. (2014). A nonparametric approach for multiple change point analysis of multivariate data. Journal of the American Statistical Association, 109(505), 334-345. https://doi.org/10.1080/01621459.2013.849605

  28. Mustafa, M. Z. B., Nordin, M. N. B., & Razzaq, A. R. B. A. (2020). Structural equation modelling using AMOS: Confirmatory factor analysis for taskload of special education integration program teachers. Universal Journal of Educational Research, 8(1), 127-133. https://doi.org/10.13189/ujer.2020.080115

  29. Oktaviyanthi, R., & Agus, R. N. (2021). Guided worksheet formal definition of limit: An instrument development process. AL-ISHLAH: Jurnal Pendidikan, 13(1), 449-461. https://doi.org/10.35445/alishlah.v13i1.483

  30. Presmeg, N. (2020). Visualization and learning in mathematics education. In S. Lerman (Ed.), Encyclopedia of mathematics education (pp. 900-904). Springer International Publishing. https://doi.org/10.1007/978-3-030-15789-0_161

  31. Sari, Y. M., Retnawati, H., & Fiangga, S. (2022). The construct validity of mathematical reasoning and proof test instrument integrated with GeoGebra: Second-order confirmatory factor analysis. Beta: Jurnal Tadris Matematika, 15(2), 104-117. https://doi.org/10.20414/betajtm.v15i2.549

  32. Semeraro, C., Giofrè, D., Coppola, G., Lucangeli, D., & Cassibba, R. (2020). The role of cognitive and non-cognitive factors in mathematics achievement: The importance of the quality of the student-teacher relationship in middle school. PLoS One, 15(4), e0231381. https://doi.org/10.1371/journal.pone.0231381

  33. Shanta, S., & Wells, J. G. (2022). T/E design based learning: assessing student critical thinking and problem solving abilities. International Journal of Technology and Design Education, 32(1), 267-285. https://doi.org/10.1007/s10798-020-09608-8

  34. von Thienen, J. P. A., Clancey, W. J., & Meinel, C. (2021). Theoretical foundations of design thinking. Part III: Robert H. McKim’s visual thinking theories. In C. Meinel & L. Leifer (Eds.), Design thinking research : Interrogating the doing (pp. 9-72). Springer International Publishing. https://doi.org/10.1007/978-3-030-62037-0_2

  35. Vucaj, I. (2022). Development and initial validation of digital age teaching scale (DATS) to assess application of ISTE standards for educators in K–12 education classrooms. Journal of Research on Technology in Education, 54(2), 226-248. https://doi.org/10.1080/15391523.2020.1840461

  36. Wan, Z. H., So, W. M. W., & Zhan, Y. (2022). Developing and validating a scale of STEM project-based learning experience. Research in Science Education, 52(2), 599-615. https://doi.org/10.1007/s11165-020-09965-3

  37. Zainudin, M., Subali, B., & Jailani, J. (2019). Construct validity of mathematical creativity instrument: First-order and second-order confirmatory factor analysis. International Journal of Instruction, 12(3), 595-614. https://doi.org/10.29333/iji.2019.12336a