STATISTICAL LITERACY KNOWLEDGE DIFFUSION: DIDACTICAL TRANSPOSITION FROM SCHOLARLY KNOWLEDGE TO KNOWLEDGE TO BE TAUGHT ON LINEAR REGRESSION
Keywords:
Didactic transposition, linear regression, knowledge to be taught, scholarly knowledge, statistical literacyAbstract
This study examines the process of external didactic transposition in linear regression material, namely the transformation of scholarly knowledge into knowledge to be taught. This transposition is important to ensure that knowledge that is complex and tends to be abstract for students can be accessed and easily understood, especially in the context of linear regression so that it can develop students' statistical literacy. The research method was conducted with a document study by analysing primary books on statistics, reference guidelines for the preparation of mathematics textbooks, and educational curriculum documents in Indonesia. The results of the study showed that the knowledge adaptations that have been identified are the meaning of dependent and independent variables, the concept of linear regression, the concept of best fit line, linear regression equation model, and random error. Although there are knowledge adaptations that facilitate understanding for students, there are also limitations in the presentation of random error distribution assumptions in the knowledge to be taught. The contribution of this research is an understanding of the construction of statistical knowledge presented in the educational context and the strengthening of materials that can develop students' statistical literacy.
Â
References
Achiam, M. (2014). Didactic Transposition: From theoretical notion to research programme. Paper Presented at ESERA (European Science Education Research Association), 1(1), 1–8.
Aityan, S. K. (2022). Linear Regression. In Linear Regression (pp. 915-978). https://doi.org/https://doi.org/10.1007/978-1-4842-6797-4_14
Anandhi, P., & Nathiya, D. E. (2023). Application of linear regression with their advantages, disadvantages, assumptions and limitations. International Journal of Statistics and Applied Mathematics. https://doi.org/https://doi.org/10.22271/maths.2023.v8.i6b.1463
Andriatna, R., & Sujadi, I. (2024). Didactic Transposition from Scholarly Mathematics to School Mathematics: The Case of Function Concept Mosharafa: Journal of Mathematics Education Mosharafa: Journal of Mathematics Education. Mosharafa: Journal of Mathematics Education, 13(1), 215–230.
Aziz, A. M., & Rosli, R. (2021). A systematic literature review on developing students' statistical literacy skills. Journal of Physics: Conference Series,1806 (1). https://doi.org/10.1088/1742-6596/1806/1/012102
Callingham, R., & Watson, J. M. (2017). The development of statistical literacy at school. Statistics Education Research Journal,16 (1), 181-201. https://doi.org/10.52041/serj.v16i1.223
Chevallard, Y., & Bosch, M. (2020). Didactic Transposition in Mathematics Education. Springer Science and Business Media LLC, 214-218. https://doi.org/https://doi.org/10.1007/978-3-030-15789-0_48
Chevallard, Y. (1989). On didactic transposition theory: some introductory notes. International Symposium on Selected Domains of Research and Development in Mathematics Education, 1-9. http://yves.chevallard.free.fr/spip/spip/article.php3?id_article=122
Chevallard, Y. (2005). In M. Bosch (Ed.), Proceedings of the Fourth Congress of the European Society for Research in Mathematics Education (Vol. 4, Issue 2005, pp. 19-64). FUNDEMI IQS - Universitat Ramon Llull.
Cooper, L. L., RodrÃguez Vásquez, M. P., & Moyer, T. O. (2024). A Student-Centered Exploration of Influential Points in Linear Regression Using Desmos. Journal of Statistics and Data Science Education, 1-16. https://doi.org/https://doi.org/10.1080/26939169.2024.2335365
Cope, C., & Prosser, M. (2005). Identifying didactic knowledge: An empirical study of the educationally critical aspects of learning about information systems. Higher Education, 49(3), 345–372.
Droogers, M. J. S., & Drijvers, P. H. M. (2017). Enhancing statistical literacy. 860-867. https://doi.org/https://dspace.library.uu.nl/handle/1874/357816
El Fadel, H. (2024). The Didactic Transposition: Practices and Pedagogical Issues. International Journal of Innovative Research in Multidisciplinary Education,03 (09), 1569-1581. https://doi.org/10.58806/ijirme.2024.v3i9n18
Fitriani, N., Kadarisma, G., & Amelia, R. (2020). Development of Didactical Design to Overcome Learning Obstacle in Third Dimension Material. AKSIOMA: Journal of Mathematics Education Study Programme,9 (2), 231. https://doi.org/10.24127/ajpm.v9i2.2686
Gal, I. (2002). Adults' statistical literacy: meaning, components, responsibilities. Int Stat Rev, .70
Healey, J. F., & Prus, S. G. (2015). Statistics: A Tool for Social Research. Nelson College Indigenous, Nelson Education.
Jacobs, J. (2023). Linear regression analysis. In Linear regression analysis (pp. 548-557). Elsevier eBooks. https://doi.org/https://doi.org/10.1016/b978-0-12-818630-5.10067-3
Krogh, E., Qvortrup, A., & Graf, S. T. (2021). Didactics and curriculum in ongoing dialogue. In Didactics and Curriculum in Ongoing Dialogue. Routledge. https://doi.org/10.4324/9781003099390
Makkawy, A. (2023). Linear Regression. In Linear Regression (pp. 113-119). Routledge eBooks. https://doi.org/https://doi.org/10.4324/b23320-21
Mann, P. S. (2011). Introductory Statistics, 7th Ed. John Wiley and Sons, Incorporated, 8(11), 736.
Mensah, R. O. (2023). Regression Fundamentals. International Series in Management Science/Operations Research, 33-55. https://doi.org/https://doi.org/10.1007/978-3-031-21480-6_3
Mezaini, D., Khemis, B., Algeria, M., & Khemmad, M. (2022). Didactics: an overview on the key concepts. Journal of El Hikma for Philosophical Studies,2 (November), 1041-1049. https://www.researchgate.net/publication/365608436
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis (6th ed.). John Wiley & Sons.
Nurhayati, L., Priatna, N., Herman, T., & Dasari, D. (2023). Learning Obstacle on Integral (Antiderivative) Material in Didactic Situation Theory. AKSIOMA: Journal of Mathematics Education Study Programme, 12(1), 984–993.
Pal, M., & Bharati, P. (2019). Introduction to Correlation and Linear Regression Analysis. Springer, Singapore, 1-18. https://doi.org/https://doi.org/10.1007/978-981-13-9314-3_1
Peck, R., Olsen, C., & Decore, J. (2016). Introduction to Statistics & Data Analysis (Vol. 4, Issue 1).
Prasad, S. (2024). Regression (pp. 1-45). https://doi.org/https://doi.org/10.1007/978-981-99-7257-9_1
Puspita, E., & Kustiawan, C. (2024). A Didactical Design Research: Knowledge Acquisition of Mathematics Teacher Prospective Students on Multiple Integral Concepts. Kreano, Journal of Creative-Innovative Mathematics,15 (1), 199-217. https://doi.org/10.15294/h3v97z60
Sadiah, L. H., Suhendra, S., & Herman, T. (2024). Learning Obstacle in Learning Three Variable Linear Equation System Based on Praxeology. AKSIOMA: Journal of Mathematics Education Study Programme,13 (2), 633. https://doi.org/10.24127/ajpm.v13i2.8352
Suarsana, I. M., Suryadi, D., Nurlaelah, E., Jupri, A., & Pacis, E. R. (2024). Didactic Transposition of Straight-Line Equations: from Scholarly Knowledge to Knowledge to be Taught. Plusminus: Journal of Mathematics Education,4 (2), 287-308. https://doi.org/10.31980/plusminus.v4i2.1528
Sulastri, R. (2023). Didactic transposition study: Exploration of knowledge to be taught on function limits. Journal of Didactic Mathematics,4 (2), 106-117. https://doi.org/10.34007/jdm.v4i2.1903
Suryadi, D. (2019). Didactic design research (DDR) and its implementation. Gapura Press.
Susanto, D., Sihombing, S. K., Radjawane, M. M., Candra, Y., Sinambela, D., & Reviewer. (2021). Teacher's book for high school/vocational school mathematics class XI.
Susetyo, B. (2012). Statistics. Directorate General of Islamic Education, Ministry of Religious Affairs Republic of Indonesia. http://www.lechtmanresearch.com/mages/research_
Walpole, R. E., Myers, R. H., Myers, S. L., & Ye, K. (2012). Probability & Statistics for Engineers & Scientists Ninth Edition. In Pearson Education Inc.
Weiss, N. A. (2012). Introductory Statistics.
Wu, Z., & Ye, L. (2015). Mathematical Modelling in Connecting Concepts to Real World Application. In The Proceedings of the 12th International Congress on Mathematical Education. https://doi.org/10.1007/978-3-319-12688-3_76
Yan, W. (2022). Correlation and Regression (pp. 241-279). https://doi.org/https://doi.org/10.1007/978-981-19-0596-4_5
Published
Issue
Section
License
Authors who publish with AKSIOMA: Jurnal Program Studi Pendidikan Matematika agree to the following terms:
Creative Commons License
AKSIOMA:Â Jurnal Program Studi Pendidikan Matematika is licensed under a Creative Commons Attribution 4.0 International License.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. AKSIOMA offers all authors of journal articles allows their research openly available, free access and time restrictions.
All articles published Open Access will be immediately and permanently free for everyone to read and download. Under the CC-BY license, authors retain ownership of the copyright for their article, but authors grant others permission to use the content of publications in AKSIOMA in whole or in part provided that the original work is properly cited. Users (redistributors) of AKSIOMA are required to cite the original source, including the author's names, AKSIOMA as the initial source of publication, year of publication, volume number and DOI (if available).
