Why is the mastery of key competencies in mathematics for Indonesian students low? : Re-analysis of PISA 2012
DOI : 10.30863/ekspose.v21i1.3403
This study aims to determine the level of mastery of the key competency attributes of Indonesian students in mathematics. This study was approached quantitatively by adopting approach retrofitting (posthoc analysis). The data sources for this study were Indonesian students aged 15 years who took part in PISA 2012, as many as 5,622 students. The data of this research are ex post facto data obtained by documentation technique, as for what will be documented in the form of response data from Indonesian students based on the results of PISA 2012 and PISA 2012 instruments (item release PISA2012). The data analysis technique used is descriptive statistics using the DINA package R application. The results of this study indicate that Indonesian students are low in mastering the key competency attributes of mathematics related to mathematical operation (MO) and data analysis (DA); high in the mastery of key mathematical competency attributes related to mathematical abstraction (MA), logical reasoning (LR), mathematical modeling (MM), and intuitive imagination (II).
Keywords
Diagnosis; DINA Model; Key Competencies of Mathematics; PISA
- Boesen, J., Lithner, J., & Palm, T. (2018). Assessing mathematical competencies: an analysis of Swedish national mathematics tests. Scandinavian Journal of Educational Research, 62(1), 109–124. https://doi.org/10.1080/00313831.2016.1212256
- Chen, J., de la Torre, J., & Zhang, Z. (2013). Relative and Absolute Fit Evaluation in Cognitive Diagnosis Modeling. Journal of Educational Measurement, 50(2), 123–140. https://doi.org/10.1111/j.1745-3984.2012.00185.x
- De La Torre, J., & Minchen, N. (2014). Cognitively diagnostic assessments and the cognitive diagnosis model framework. Psicologia Educativa, 20(2), 89–97. https://doi.org/10.1016/j.pse.2014.11.001
- Duschl, R., Maeng, S., & Sezen, A. (2011). Learning progressions and teaching sequences: a review and analysis. Studies in Science Education, 47(2), 123–182. https://doi.org/10.1080/03057267.2011.604476
- Jacobs, M., Mhakure, D., Fray, R. L., Holtman, L., & Julie, C. (2014). Item difficulty analysis of a high-stakes mathematics examination using Rasch analysis: The case of sequences and series. Pythagoras, 35(1). https://doi.org/10.4102/pythagoras.v35i1.220
- Kartianom, K., & Ndayizeye, O. (2017). What‘s wrong with the Asian and African Students’ mathematics learning achievement? The multilevel PISA 2015 data analysis for Indonesia, Japan, and Algeria. Jurnal Riset Pendidikan Matematika, 4(2), 200–210.
- Kilpatrick, J. (2020). Competency Frameworks in Mathematics Education. In Encyclopedia of Mathematics Education (pp. 110–113). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-15789-0_27
- Niss, M. (2015). Mathematical competencies and PISA. In Assessing Mathematical Literacy (pp. 35–55). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-10121-7_2
- Niss, M., & Jablonka, E. (2020). Mathematical Literacy. In Encyclopedia of Mathematics Education (pp. 548–553). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-15789-0_100
- OECD. (2004). Learning for tomorrow’s world. OECD. https://doi.org/10.1787/9789264006416-en
- OECD. (2019). PISA 2018 Results. In OECD Publishing.
- Pettersen, A., & Braeken, J. (2019). Mathematical competency demands of assessment items: a search for empirical evidence. International Journal of Science and Mathematics Education, 17(2), 405–425. https://doi.org/10.1007/s10763-017-9870-y
- Ravand, H., & Robitzsch, A. (2015). Cognitive Diagnostic Modeling Using R. Practical Assessment, Research, and Evaluation, 20, 11. https://doi.org/10.7275/5g6f-ak15
- Rezky, R., & Wijaya, A. (2018). Designing hypothetical learning trajectory based on van hiele theory: a case of geometry. Journal of Physics: Conference Series, 1097(1), 12129. IOP Publishing.
- Rupp, A. A., Templin, J., & Henson, R. A. (2010). Diagnostic measurement: Theory, methods, and applications. In Diagnostic measurement: Theory, methods, and applications. New York, NY, US: Guilford Press.
- Tatsuoka, K. K. (2009). Cognitive Assessment. In Cognitive Assessment: An Introduction to the Rule Space Method. Routledge. https://doi.org/10.4324/9780203883372
- Tomul, E., Önder, E., & Taslidere, E. (2021). The relative effect of student, family and school-related factors on math achievement by location of the school. Large-Scale Assessments in Education, 9(1), 22. https://doi.org/10.1186/s40536-021-00117-1
- Wu, X., Wu, R., Chang, H.-H., Kong, Q., & Zhang, Y. (2020). International comparative study on PISA mathematics achievement test based on cognitive diagnostic models. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.02230
- Wu, X., Zhang, Y., Wu, R., & Chang, H.-H. (2021). A comparative study on cognitive diagnostic assessment of mathematical key competencies and learning trajectories. Current Psychology. https://doi.org/10.1007/s12144-020-01230-0
Copyright (c) 2022 Kartianom Kartianom, Rika Damayanti, Musdalifah Musdalifah
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Article Info
Submitted: 2022-12-12
Published: 2022-12-12
Section: Articles