Before dan After: Kemampuan Siswa Kelas V Setelah Belajar dengan Menggunakan Pendekatan Realistik Matematika
Article Sidebar
Published
Jan 30, 2023
Main Article Content
Abstract
Data has been an integrated part of daily life and it is important for students to be able to read data in its many representations. Therefore, there is a need for suitable teaching and learning approach to support students in learning how to read data, such as Realistic Mathematics Education (RME). The aim of this study is to discover whether RME is effective enough in supporting students by comparing students’ ability in reading data before and after the learning process. The data in this study was analyzed from the pretest and posttest result of 28 fifth grader of Jebed 3 Primary School in Pemalang, Central java. The data was analyzed using descriptive statistics, paired-sample t test, Cohen’s d effect size, and also normalized change. The study found that RME lesson can support students to be able to read data. Most students have the ability to read the data, but several students still have difficulties in making comparison and making further calculation to read between the data. Additionally, some students also had difficulty in perceiving the symbols representation in pictogram, and also in calculate numbers.
Article Details
How to Cite
Windria, H., Waluya, S., & Mariani, S. (2023). Before dan After: Kemampuan Siswa Kelas V Setelah Belajar dengan Menggunakan Pendekatan Realistik Matematika. Journal on Education, 5(3), 5509-5521. https://doi.org/10.31004/joe.v5i3.1304
Section
Articles
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Arteaga, P., Díaz-Levicoy, D., & Batanero, C. (2021). Primary School Students’ Reading Levels of Line Graphs. Statistics Education Research Journal, 20(2). https://doi.org/10.52041/SERJ.V20I2.339
Auliya, R. N. (2019). Can Mathematics and Statistics Perception Explain Students’ Statistical Literacy? JRAMathEdu (Journal of Research and Advances in Mathematics Education), 3(2), 86. https://doi.org/10.23917/jramathedu.v3i2.5983
Ben-Zvi, D., & Makar, K. (2016). International Perspectibes on the Teaching and Learning of Statistics. In D. Ben-Zvi & K. Makar (Eds.), The Teaching and Learning of Statistics: International Perspective (pp. 1–10). Dordrecht, Heidelberg, London, New York: Springer.
Borkin, M. A., Vo, A. A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., & Pfiste, H. (2013). What makes a data visualization memorable ? IEEE Transactions on Visualization and Computer Graphics, 19(12), 1–6.
Branch, R. M., & Kopcha, T. J. (2014). Instructional Design Models. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of Research on Educational Communications and Technology: Fourth Edition (fourth, pp. 1–1005). https://doi.org/10.1007/978-1-4614-3185-5
Crusoe, D. (2016). Data Literacy defined pro populo: To read this article, please provide a little information. The Journal of Community Informatics, 12(3), 27–46. https://doi.org/10.15353/joci.v12i3.3276
Data.(n.d). dalam Macmillan Dictionary. Diambil dari https://macmillandictionary.com/dictionary/british/data
Díaz-Levicoy, D., Batanero, C., Arteaga, P., & Gea, M. M. (2019). Chilean Children’s Reading Levels of Statistical Graphs. International Electronic Journal of Mathematics Education, 15(1), 689–700. https://doi.org/10.29333/iejme/5786
Estrella, S., Mena-Lorca, A., & Olfos, R. (2016). Tasks Associated to The Treatment of Tables at Elementary Schiil and Its Level Difficulty. In D. Ben-Zvi & K. Makar (Eds.), The Teaching and Learning of Statistics: International Perspective (pp. 95–96). Springer.
Fauzan, A., Musdi, E., & Afriadi, J. (2018). Developing learning trajectory for teaching statistics at junior high school using RME approach. Journal of Physics: Conference Series, 1088. https://doi.org/10.1088/1742-6596/1088/1/012040
Frank, M., Walker, J., Julie Attard, sotonacuk, & Tygel, A. (2012). Special Issue on Data Literacy: Editorial. The Journal of Community Informatics, 12(3), 4–8. www.ci-journal.net/index.php/ciej/article/view/1347
Garcia-Mila, M., Marti, E., Gilabert, S., & Castells, M. (2014). Fifth Through Eighth Grade Students’ Difficulties in Constructing Bar Graphs: Data Organization, Data Aggregation, and Integration of a Second Variable. Mathematical Thinking and Learning, 16(3), 201–233. https://doi.org/10.1080/10986065.2014.921132
Koltay, T. (2017). Data literacy for researchers and data librarians. Journal of Librarianship and Information Science, 49(1), 3–14. https://doi.org/10.1177/0961000615616450
Laurens, T., Batlolona, F. A., Batlolona, J. R., & Leasa, M. (2018). How does realistic mathematics education (RME) improve students’ mathematics cognitive achievement? Eurasia Journal of Mathematics, Science and Technology Education, 14(2), 569–578. https://doi.org/10.12973/ejmste/76959
Marx, J. D., & Cummings, K. (2007). Normalized change. American Journal of Physics, 75(1), 87–91. doi:10.1119/1.2372468
Mulya, N., Nurlaelah, E., & Prabawanto, S. (2018). Students’ statistical literacy on junior high school. International Conference on Mathematics and Science Education of Universitas Pendidikan Indonesia, 3, 710–714. http://science.conference.upi.edu/proceeding/index.php/ICMScE/issue/view/3
Paroqi, L. L., Mursalin, M., & Marhami, M. (2020). The Implementation of Realistic Mathematics Education Approach to Improve Students’ Mathematical Communication Ability in Statistics Course. International Journal for Educational and Vocational Studies, 2(10), 879–889. https://doi.org/10.29103/ijevs.v2i10.3311
Prado, J. C., & Marzal, M. Á. (2013). Incorporating data literacy into information literacy programs: Core competencies and contents. Libri, 63(2), 123–134. https://doi.org/10.1515/libri-2013-0010
Pratama, M. A., Supahar, Lestari, D. P., Sari, W. K., Putri, T. S. Y., & Adiatmah, V. A. K. (2020). Data literacy assessment instrument for preparing 21 Cs literacy: Preliminary study. Journal of Physics: Conference Series, 1440(1). https://doi.org/10.1088/1742-6596/1440/1/012085
Ridsdale, C., Bliemel, M., Kelley, D. E., & Matwin, S. S. (2015). Strategies and Best Practices for Data Literacy Education Knowledge Synthesis Report Strategies and Best Practices for Data Literacy Education Knowledge Synthesis Report View project Mentored Undergraduate Research and Identity Development View project. https://www.researchgate.net/publication/284029915
Ridgway, J. (2016). Implications of the Data Revolution for Statistics Education. International Statistical Review, 84(3), 528–549. https://doi.org/10.1111/insr.12110
Sriyansyah, S. P., & Azhari, D. (2017). Addressing an undergraduate research issue about normalized change for critical thinking test. Jurnal Pendidikan IPA Indonesia, 6(1), 138–144. https://doi.org/10.15294/jpii.v6i1.9602
Susac, A., Bubic, A., Martinjak, P., Planinic, M., & Palmovic, M. (2017). Graphical representations of data improve student understanding of measurement and uncertainty: An eye-tracking study. Physical Review Physics Education Research, 13(2). https://doi.org/10.1103/PhysRevPhysEducRes.13.020125
Sharma, S. (2017). Definitions and models of statistical literacy: a literature review. Open Review of Educational Research, 4(1), 118–133. https://doi.org/10.1080/23265507.2017.1354313
Taufina, T., Chandra, C., Fauzan, A., & Ilham Syarif, M. (2019). Development of Statistics in Elementary School Based RME Approach with Problem Solving for Revolution Industry 4.0. 382(Icet), 716–721. https://doi.org/10.2991/icet-19.2019.172
Utomo, D. P. (2021). An analysis of the statistical literacy of middle school students in solving timss problems. International Journal of Education in Mathematics, Science and Technology, 9(2), 181–197. https://doi.org/10.46328/IJEMST.1552
Wijaya, A., van den Heuvel-Panhuizen, M., Doorman, M., & Robitzsch, A. (2014). Difficulties in solving context-based PISA mathematics tasks: An analysis of students’ errors. Mathematics Enthusiast, 11(3), 555–584. https://doi.org/10.54870/1551-3440.1317
Wolff, A., Gooch, D., Cavero Montaner, J. J., Rashid, U., & Kortuem, G. (2016). Creating an Understanding of Data Literacy for a Data-driven Society. The Journal of Community Informatics, 12(3), 9–26. https://doi.org/10.15353/joci.v12i3.3275
Van den Heuvel-Panhuizen, M., & Drijvers, P. (2014). Realistic Mathematics Education. Dalam Encyclopedia of Mathematics Education (pp. 521–525). https://doi.org/10.1007/978-94-007-4978-8_170
Auliya, R. N. (2019). Can Mathematics and Statistics Perception Explain Students’ Statistical Literacy? JRAMathEdu (Journal of Research and Advances in Mathematics Education), 3(2), 86. https://doi.org/10.23917/jramathedu.v3i2.5983
Ben-Zvi, D., & Makar, K. (2016). International Perspectibes on the Teaching and Learning of Statistics. In D. Ben-Zvi & K. Makar (Eds.), The Teaching and Learning of Statistics: International Perspective (pp. 1–10). Dordrecht, Heidelberg, London, New York: Springer.
Borkin, M. A., Vo, A. A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., & Pfiste, H. (2013). What makes a data visualization memorable ? IEEE Transactions on Visualization and Computer Graphics, 19(12), 1–6.
Branch, R. M., & Kopcha, T. J. (2014). Instructional Design Models. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of Research on Educational Communications and Technology: Fourth Edition (fourth, pp. 1–1005). https://doi.org/10.1007/978-1-4614-3185-5
Crusoe, D. (2016). Data Literacy defined pro populo: To read this article, please provide a little information. The Journal of Community Informatics, 12(3), 27–46. https://doi.org/10.15353/joci.v12i3.3276
Data.(n.d). dalam Macmillan Dictionary. Diambil dari https://macmillandictionary.com/dictionary/british/data
Díaz-Levicoy, D., Batanero, C., Arteaga, P., & Gea, M. M. (2019). Chilean Children’s Reading Levels of Statistical Graphs. International Electronic Journal of Mathematics Education, 15(1), 689–700. https://doi.org/10.29333/iejme/5786
Estrella, S., Mena-Lorca, A., & Olfos, R. (2016). Tasks Associated to The Treatment of Tables at Elementary Schiil and Its Level Difficulty. In D. Ben-Zvi & K. Makar (Eds.), The Teaching and Learning of Statistics: International Perspective (pp. 95–96). Springer.
Fauzan, A., Musdi, E., & Afriadi, J. (2018). Developing learning trajectory for teaching statistics at junior high school using RME approach. Journal of Physics: Conference Series, 1088. https://doi.org/10.1088/1742-6596/1088/1/012040
Frank, M., Walker, J., Julie Attard, sotonacuk, & Tygel, A. (2012). Special Issue on Data Literacy: Editorial. The Journal of Community Informatics, 12(3), 4–8. www.ci-journal.net/index.php/ciej/article/view/1347
Garcia-Mila, M., Marti, E., Gilabert, S., & Castells, M. (2014). Fifth Through Eighth Grade Students’ Difficulties in Constructing Bar Graphs: Data Organization, Data Aggregation, and Integration of a Second Variable. Mathematical Thinking and Learning, 16(3), 201–233. https://doi.org/10.1080/10986065.2014.921132
Koltay, T. (2017). Data literacy for researchers and data librarians. Journal of Librarianship and Information Science, 49(1), 3–14. https://doi.org/10.1177/0961000615616450
Laurens, T., Batlolona, F. A., Batlolona, J. R., & Leasa, M. (2018). How does realistic mathematics education (RME) improve students’ mathematics cognitive achievement? Eurasia Journal of Mathematics, Science and Technology Education, 14(2), 569–578. https://doi.org/10.12973/ejmste/76959
Marx, J. D., & Cummings, K. (2007). Normalized change. American Journal of Physics, 75(1), 87–91. doi:10.1119/1.2372468
Mulya, N., Nurlaelah, E., & Prabawanto, S. (2018). Students’ statistical literacy on junior high school. International Conference on Mathematics and Science Education of Universitas Pendidikan Indonesia, 3, 710–714. http://science.conference.upi.edu/proceeding/index.php/ICMScE/issue/view/3
Paroqi, L. L., Mursalin, M., & Marhami, M. (2020). The Implementation of Realistic Mathematics Education Approach to Improve Students’ Mathematical Communication Ability in Statistics Course. International Journal for Educational and Vocational Studies, 2(10), 879–889. https://doi.org/10.29103/ijevs.v2i10.3311
Prado, J. C., & Marzal, M. Á. (2013). Incorporating data literacy into information literacy programs: Core competencies and contents. Libri, 63(2), 123–134. https://doi.org/10.1515/libri-2013-0010
Pratama, M. A., Supahar, Lestari, D. P., Sari, W. K., Putri, T. S. Y., & Adiatmah, V. A. K. (2020). Data literacy assessment instrument for preparing 21 Cs literacy: Preliminary study. Journal of Physics: Conference Series, 1440(1). https://doi.org/10.1088/1742-6596/1440/1/012085
Ridsdale, C., Bliemel, M., Kelley, D. E., & Matwin, S. S. (2015). Strategies and Best Practices for Data Literacy Education Knowledge Synthesis Report Strategies and Best Practices for Data Literacy Education Knowledge Synthesis Report View project Mentored Undergraduate Research and Identity Development View project. https://www.researchgate.net/publication/284029915
Ridgway, J. (2016). Implications of the Data Revolution for Statistics Education. International Statistical Review, 84(3), 528–549. https://doi.org/10.1111/insr.12110
Sriyansyah, S. P., & Azhari, D. (2017). Addressing an undergraduate research issue about normalized change for critical thinking test. Jurnal Pendidikan IPA Indonesia, 6(1), 138–144. https://doi.org/10.15294/jpii.v6i1.9602
Susac, A., Bubic, A., Martinjak, P., Planinic, M., & Palmovic, M. (2017). Graphical representations of data improve student understanding of measurement and uncertainty: An eye-tracking study. Physical Review Physics Education Research, 13(2). https://doi.org/10.1103/PhysRevPhysEducRes.13.020125
Sharma, S. (2017). Definitions and models of statistical literacy: a literature review. Open Review of Educational Research, 4(1), 118–133. https://doi.org/10.1080/23265507.2017.1354313
Taufina, T., Chandra, C., Fauzan, A., & Ilham Syarif, M. (2019). Development of Statistics in Elementary School Based RME Approach with Problem Solving for Revolution Industry 4.0. 382(Icet), 716–721. https://doi.org/10.2991/icet-19.2019.172
Utomo, D. P. (2021). An analysis of the statistical literacy of middle school students in solving timss problems. International Journal of Education in Mathematics, Science and Technology, 9(2), 181–197. https://doi.org/10.46328/IJEMST.1552
Wijaya, A., van den Heuvel-Panhuizen, M., Doorman, M., & Robitzsch, A. (2014). Difficulties in solving context-based PISA mathematics tasks: An analysis of students’ errors. Mathematics Enthusiast, 11(3), 555–584. https://doi.org/10.54870/1551-3440.1317
Wolff, A., Gooch, D., Cavero Montaner, J. J., Rashid, U., & Kortuem, G. (2016). Creating an Understanding of Data Literacy for a Data-driven Society. The Journal of Community Informatics, 12(3), 9–26. https://doi.org/10.15353/joci.v12i3.3275
Van den Heuvel-Panhuizen, M., & Drijvers, P. (2014). Realistic Mathematics Education. Dalam Encyclopedia of Mathematics Education (pp. 521–525). https://doi.org/10.1007/978-94-007-4978-8_170