Student-Teacher Level of Statistical Reasoning Ability Reviewed by Apos Theory (Action, Process, Object, Schema)

Nayla Nafisa Ghassaniy, Aan Hasanah, Wahyudin Wahyudin

Abstract


In learning statistics, there is the term statistical reasoning. Statistical reasoning arises because of the opinion that learning statistics using a traditional approach does not lead students to have statistical reasoning abilities or to think statistically. There are four statistical reasoning levels: idiosyncratic, transitional, quantitative, and analytical. Student-teachers must understand fundamental statistical concepts more deeply to develop statistical reasoning abilities. The mental construction of a statistical concept can be analyzed using APOS (Action, Process, Object, Schema) theory. Through the APOS theory, researchers can determine individual understanding of statistical concepts that cause individuals to reason or draw reasonable conclusions based on the statistical information obtained. This study aims to analyze the interrelationships and make conjectures regarding the level of statistical reasoning ability based on the APOS theory. The research subjects are. 19 informants. The research results obtained four conjectures, namely. Students at the idiosyncratic level are in the action stage; students at the transitional stage are in the action, process, or object stage; students at the quantitative stage are in the process or object stage; lastly, students at the analytical are in the schema stage.

Keywords


APOS theory; mathematics education; statistical reasoning

References


Arnon, I., Cottrill, J., Dubinsky, E., Oktaç, A., Fuentes, R. S., Trigueros, M., & Weller, K. (2014). APOS Theory A Framework for Research and Curriculum Development in Mathematics Education. Springer. https://doi.org/10.1007/978-1-4614-7966-6

Ben-Zvi, D., & Garfield, J. (2004). The Challenge Of Developing Statistical Literacy, Reasoning And Thinking. Kluwer Academic.

Burrill, G., & Ben-Zvi, D. (2019). Topics and Trends in Current Statistics Education Research. Springer. http://www.springer.com/series/15585

Corbin, J., & Strauss, A. (2008). Basic of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (3rd ed.). Sage.

Dubinsky, E., Weller, K., McDonald, M. A., & Brown, A. (2005). Some historical issues and paradoxes regarding the concept of infinity: An APOS-based analysis: Part 1. Educational Studies in Mathematics, 58(3), 335–359. https://doi.org/10.1007/s10649-005-2531-z

Garfield, J., & Gal, I. (1999). Teaching and Assessing Statistical

Reasoning. Developing Mathematical Reasoning in Grades K-12: National Council Teachers of Mathematics 1999 Year, 207–219.

Idris, K. (2018). Teaching and learning statistics in college: How learning materials should be designed. Journal of Physics: Conference Series, 1088. https://doi.org/10.1088/1742-6596/1088/1/012032

Jones, G. A., Langrall, C. W., Mooney, E. S., & Thornton, C. A. (2004). Models Of Development In Statistical Reasoning.

Jones, G. A., Langrall, C. W., Thornton, C. A., Mooney, E. S., Wares, A., Jones, M. R., Perry, B., Putt, I. J., & Nisbet, S. (2001). Using students’ statistical thinking to inform instruction. Journal of Mathematical Behavior, 20, 109–144.

Jones, G. A., Thornton, C. A., Langrall, C. W., Mooney, E. S., Perry, B., & Putt, I. J. (2000). A Framework for Characterizing Children’s Statistical Thinking. Mathematical Thinking and Learning, 2(4), 269–307. https://doi.org/10.1207/s15327833mtl0204_3

Lavigne, N. C., & Lajoie, S. P. (2007). Statistical reasoning of middle school children engaged in survey inquiry. Contemporary Educational Psychology, 32(4), 630–666. https://doi.org/10.1016/j.cedpsych.2006.09.001

Lee, S., & Kim, G. (2019). How middle-school mathematics textbooks of Korea and the US support to develop students’ statistical reasoning. The Mathematical Education, 58(1), 139–160. https://doi.org/10.7468/mathedu.2019.58.1.139

Mooney, E. S. (2002). A Framework for Characterizing Middle School Students’ Statistical Thinking. Mathematical Thinking and Learning, 4(1), 23–63. https://doi.org/10.1207/s15327833mtl0401_2

Oktay, J. S. (2012). Grounded Theory. Oxford University Press.

Olani, A., Hoekstra, R., Harskamp, E., & Van Der Werf, G. (2011). Statistical Reasoning Ability, Self-Efficacy, and Value Beliefs in a Reform Based University Statistics Course. Electronic Journal of Research in Educational Psychology, 9(1), 1696–2095.

Onwuegbuzie, A. J., & Wilson, V. A. (2003). Statistics Anxiety: Nature, etiology, antecedents, effects, and treatments--a comprehensive review of the literature. Teaching in Higher Education, 8(2), 195–209. https://doi.org/10.1080/1356251032000052447

Rohana, R., & Ningsih, Y. L. (2020). Students’ Statistical Reasoning In Statistics Method Course. Jurnal Pendidikan Matematika, 14(1), 81–90. https://doi.org/10.22342/jpm.14.1.6732.81-90

Rosidah, Ketut Budayasa, I., & Juniati, D. (2018). An Analysis of Statistical Reasoning Process of High School Students in Solving the Statistical Problem. Journal of Physics: Conference Series, 1028(1). https://doi.org/10.1088/1742-6596/1028/1/012125




DOI: https://doi.org/10.35194/jp.v12i2.3139

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 PRISMA

Prisma Indexing : 

   

Creative Commons License
PRISMA by UNIVERSITAS SURYAKANCANA is licensed under a This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.Based on a work at https://jurnal.unsur.ac.id/prisma.