STATISTICAL LITERACY KNOWLEDGE DIFFUSION: DIDACTICAL TRANSPOSITION FROM SCHOLARLY KNOWLEDGE TO KNOWLEDGE TO BE TAUGHT ON LINEAR REGRESSION

Authors

DOI:

https://doi.org/10.24127/ajpm.v15i2.13792

Keywords:

Didactic transposition, linear regression, knowledge to be taught, scholarly knowledge, statistical literacy

Abstract

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.

Penelitian ini mengkaji proses transposisi didaktis eksternal pada materi regresi linear, yaitu transformasi pengetahuan ilmiah (scholarly knowledge) menjadi pengetahuan yang akan diajarkan (knowledge to be taught). Transposisi ini penting untuk memastikan pengetahuan yang kompleks dan cenderung abstrak bagi siswa dapat diakses dan mudah dipahami, khususnya dalam konteks regresi linear sehinga dapat mengembangkan literasi statistik siswa. Metode penelitian dilakukan dengan studi dokumen melalui analisis buku primer materi statistika, pedoman acuan penyusunan buku teks matematika, dan dokumen kurikulum pendidikan di Indonesia. Hasil studi menunjukkan adaptasi pengetahuan yang telah teridentifikasi yaitu pemaknaan variabel dependen dan independen, konsep regresi linear, konsep garis best fit, model persamaan regresi linear, serta random error. Walaupun terdapat adaptasi pengetahuan yang memfasilitasi pemahaman untuk siswa, tetapi ditemukan pula adanya keterbatasan dalam penyajian asumsi distribusi random eror dalam knowledge to be taught. Kontribusi penelitian ini yaitu pemahaman terhadap konstruksi pengetahuan statistika yang disajikan pada konteks pendidikan serta penguatan materi yang dapat mengembangkan literasi statistik siswa.

References

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

Amna Saleem, Huma Kausar, & Farah Deeba. (2021). Social Constructivism: A New Paradigm in Teaching and Learning Environment. Perennial Journal of History, 2(2), 403–421. https://doi.org/10.52700/pjh.v2i2.86

Anandhi, P., & Nathiya, D. E. (2023). Application of linear regression with their advantages, disadvantages, assumption 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 : Jurnal Pendidikan Matematika Mosharafa : Jurnal Pendidikan Matematika. Mosharafa: Jurnal Pendidikan Matematika, 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

Cooper, L. L., Rodríguez 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.

Do, T. H., & Nguyen, V. T. T. (2020). The structure of didactic transposition capability - analysis of an example of didactic transposition of physical knowledge in the training of pedagogical students. Vietnam Journal of Education, 4(1), 44–52. https://doi.org/10.52296/vje.2020.7

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). Pengembangan Desain Didaktis Untuk Mengatasi Learning Obstacle Pada Materi Dimensi Tiga. AKSIOMA: Jurnal Program Studi Pendidikan Matematika, 9(2), 231. https://doi.org/10.24127/ajpm.v9i2.2686

Gök, M., Erdoğan, A., & Erdoğan, E. Ö. (2019). Transpositions of function concept in mathematics curricula and textbooks from the historical development perspective. International Journal of Instruction, 12(1), 1189–1206. https://doi.org/10.29333/iji.2019.12176a

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). Didaktik and curriculum in ongoing dialogue. In Didaktik 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/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.

Morales-López, Y., Páez, A. B., Vega, D. A., & Breda, A. (2023). Identification of characteristics of didactic and meta-didactic mathematical knowledge of novice and expert teachers when reflecting on class episodes. Journal on Mathematics Education, 14(1), 149–168. https://doi.org/10.22342/jme.v14i1.pp149-168

Nurhayati, L., Priatna, N., Herman, T., & Dasari, D. (2023). Learning Obstacle pada Materi Integral (Antiderivative) dalam Teori Situasi Didaktis. AKSIOMA: Jurnal Program Studi Pendidikan Matematika, 12(1), 984–993.

Pal, M., & Bharati, P. (2019). Introduction to Correlation and Linear Regression Analysis. Springer, Singapore, 1–18. 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).

Phuong, V. D., & Quan, N. H. (2023). The Didactic Transposition Competence of Mathematics Preservice Teachers. Hnue Journal of Science, 68(3), 151–165. https://doi.org/10.18173/2354-1075.2023-0071

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, Jurnal Matematika Kreatif-Inovatif, 15(1), 199–217. https://doi.org/10.15294/h3v97z60

Rudi, R., Suryadi, D., & Rosjanuardi, R. (2022). Didactical Transposition within Reflective Practice of an Indonesian Mathematics Teacher Community: A Case in Proving the Pythagorean Theorem Topic. Southeast Asian Mathematics Education Journal, 12(1), 65–80. https://doi.org/https://doi.org/10.46517/seamej.v12i1.132

Sadiah, L. H., Suhendra, S., & Herman, T. (2024). Learning Obstacle Pada Pembelajaran Sistem Persamaan Linear Tiga Variabel Berdasarkan Praxeology. AKSIOMA: Jurnal Program Studi Pendidikan Matematika, 13(2), 633. https://doi.org/10.24127/ajpm.v13i2.8352

Son, A. L., Darhim, & Fatimah, S. (2020). Students’ mathematical problem-solving ability based on teaching models intervention and cognitive style. Journal on Mathematics Education, 11(2), 209–222. https://doi.org/10.22342/jme.11.2.10744.209-222

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: Jurnal Pendidikan Matematika, 4(2), 287–308. https://doi.org/10.31980/plusminus.v4i2.1528

Sulastri, R. (2023). Studi didactic transposition: Eksplorasi knowledge to be taught pada limit fungsi. Journal of Didactic Mathematics, 4(2), 106–117. https://doi.org/10.34007/jdm.v4i2.1903

Suryadi, D. (2019). Penelitian desain didaktis (DDR) dan implementasinya. Gapura Press.

Susanto, D., Sihombing, S. K., Radjawane, M. M., Candra, Y., Sinambela, D., & Penelaah. (2021). Buku Guru Matematika SMA/SMK Kelas XI.

Susetyo, B. (2012). Statistika. Direktorat Jenderal Pendidikan Islam, Kementerian Agama Republik Indonesia. http://www.lechtmanresearch.com/mages/research_

Tran, D., Nguyen, T. T. A., Nguyen, T. D., Ta, T. M. P., Huynh, T. B., Phan, T. P., Pham, T. N., & Nguyen, T. H. N. (2023). Planning for Developing Students’ Statistical Literacy: A Research-Informed Framework Development. Vietnam Journal of Education, 7(2), 74–81. https://doi.org/10.52296/vje.2023.227

Walpole, R. E., Myers, R. H., Myers, S. L., & Ye, K. (2012). Probability & Statistics for Engineers & Scientists Ninth Edition. In Pearson Education Inc.

Wang, C., Shinno, Y., Xu, B., & Miyakawa, T. (2023). An anthropological point of view: exploring the Chinese and Japanese issues of translation about teaching resources. ZDM - Mathematics Education, 55(3), 705–717. https://doi.org/10.1007/s11858-023-01477-4

Weiland, T., & Sundrani, A. (2022). Opportunities for K-8 Students to Learn Statistics Created by States’ Standards in the United States. Journal of Statistics and Data Science Education, 30(2), 165–178. https://doi.org/10.1080/26939169.2022.2075814

Weiss, N. A. (2012). Introductory Statistics.

Wu, Z., & Ye, L. (2015). Mathematical Modeling 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

Yin, A., Chan, W., Cheung, C., & Sung, M. (2025). Enhancing students ’ digital literacy skills through their technology use in a course ‑ based research project : a Hong Kong case study. Asia Pacific Education Review, 0123456789. https://doi.org/10.1007/s12564-025-10038-1

Downloads

Published

25-06-2026

Issue

Section

Articles