Why is the mastery of key competencies in mathematics for Indonesian students low? : Re-analysis of PISA 2012

Authors

  • Kartianom Kartianom Institut Agama Islam Negeri Bone, Indonesia
  • Rika Damayanti Institut Agama Islam Negeri Bone, Indonesia
  • Musdalifah Musdalifah Institut Agama Islam Negeri Bone, Indonesia

DOI:

https://doi.org/10.30863/ekspose.v21i1.3403

Keywords:

Diagnosis, DINA Model, Key Competencies of Mathematics, PISA

Abstract

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).

References

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

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Published

2022-12-12

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