MENINGKATKAN LITERASI MATEMATIS MELALUI PENDEKATAN MATEMATIKA REALISTIK PADA SISWA KELAS 11 SMA YPK

Glori Cahaya  Gultom; Mutia  Hafiza; Rehana Rehana; Rifka Elianti br  Kaban; Samuel Yordan  Ambarita; Budi Halomoan  Siregar; Saripah Erguna br  Saragih

doi:10.51878/edutech.v5i1.4610

Authors

Glori Cahaya Gultom Program Studi Pendidikan Matematika, Universitas Negeri Medan
Mutia Hafiza Program Studi Pendidikan Matematika, Universitas Negeri Medan
Rehana Rehana Program Studi Pendidikan Matematika, Universitas Negeri Medan
Rifka Elianti br Kaban Program Studi Pendidikan Matematika, Universitas Negeri Medan
Samuel Yordan Ambarita Program Studi Pendidikan Matematika, Universitas Negeri Medan
Budi Halomoan Siregar Program Studi Pendidikan Matematika, Universitas Negeri Medan
Saripah Erguna br Saragih Program Studi Pendidikan Matematika, Universitas Negeri Medan

DOI:

https://doi.org/10.51878/edutech.v5i1.4610

Keywords:

Literasi Matematika, Pendidikan Matematika Realistis (PMR), Pembelajaran Kontekstual., Teknologi Pendidikan

Abstract

The purpose of this study was to improve students' mathematical literacy competence in class XI at SMA YPK Medan by implementing the Realistic Mathematics Education (RME) approach supported by educational technology. Mathematical literacy includes students' ability to understand, interpret, and apply mathematical concepts in real-world contexts. In this study, an experimental approach with pre-tests and post-tests was used to compare the results of the control and experimental classes. The experimental class was taught using the RME approach supported by technology such as mathematics learning applications and interactive media, while the control class used traditional teaching methods without technological support. Data were analyzed using a t-test to analyze differences in mathematical competence between the two classes. The results of this study indicate that the RME approach supported by technology is significantly more effective in improving students' mathematical literacy compared to traditional methods, especially in terms of formulating, interpreting, and applying mathematics to solve real-world problems. This research also shows that the integration of technology in mathematics learning can enrich students' learning experiences, increase engagement, and support the development of better mathematical skills.

Abstrak

Tujuan dari penelitian ini adalah untuk meningkatkan kompetensi literasi matematika siswa pada kelas XI di SMA YPK Medan dengan menerapkan Pendekatan Matematika Realistik (PMR) yang didukung oleh teknologi pendidikan. Literasi matematika mencakup kemampuan siswa dalam memahami, menginterpretasikan, dan mengaplikasikan konsep-konsep matematika dalam konteks dunia nyata. Dalam penelitian ini, pendekatan eksperimental dengan pre-test dan post-test digunakan untuk membandingkan hasil dari kelas kontrol dan kelas eksperimen. Kelas eksperimen diajar menggunakan pendekatan PMR yang didukung oleh teknologi seperti aplikasi pembelajaran matematika dan media interaktif, sedangkan kelas kontrol menggunakan metode pengajaran tradisional tanpa dukungan teknologi. Data dianalisis dengan menggunakan uji-t untuk menganalisis perbedaan kompetensi matematika antara kedua kelas. Hasil penelitian ini menunjukkan bahwa pendekatan PMR yang didukung oleh teknologi secara signifikan lebih efektif dalam meningkatkan literasi matematika siswa dibandingkan dengan metode tradisional, terutama dalam hal merumuskan, menafsirkan, dan mengaplikasikan matematika untuk memecahkan masalah dunia nyata. Penelitian ini juga menunjukkan bahwa integrasi teknologi dalam pembelajaran matematika dapat memperkaya pengalaman belajar siswa, meningkatkan keterlibatan, dan mendukung pengembangan keterampilan matematika yang lebih baik.

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References

Cohen, J. (2018). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.

Fauzana, R., dkk. (2020). Efektivitas Pendekatan Matematika Realistik (PMR) dalam meningkatkan kemampuan pemecahan masalah matematika siswa. Jurnal Pendidikan Matematika, 8(2), 123-135.

Ferguson, G. A. (2018). Statistical analysis in psychology and education. McGraw-Hill.

Field, A. (2020). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.

Fox, J. (2020). Applied regression analysis and generalized linear models (3rd ed.). SAGE Publications.

Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486–489. https://doi.org/10.5812/ijem.3505

Glass, G. V., & Hopkins, K. D. (2016). Statistical methods in education and psychology (3rd ed.). Pearson Education.

Kim, H. Y. (2013). Statistical notes for clinical researchers: Assessing normal distribution using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52–54. https://doi.org/10.5395/rde.2013.38.1.52

Komala, E., & Monariska, E. (2023). Analisis literasi matematika siswa Indonesia berdasarkan hasil PISA. Jurnal Penelitian Pendidikan, 10(1), 45-58.

Maysarah, S., Saragih, S., Armanto, D., & Siregar, H. (2024). Kemampuan literasi matematis dan keterampilan sosial melalui model project-based learning. Delta-Pi: Jurnal Matematika dan Pendidikan Matematika, 13(1), 68–81. https://doi.org/10.33387/dpi.v13i1.7076

Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. U.S. Department of Education.

Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of Cardiac Anaesthesia, 22(1), 67–72. https://doi.org/10.4103/aca.ACA_157_18

Nordstokke, D. W., & Zumbo, B. D. (2014). A new nonparametric Levene test for equal variances. Psicologica, 35(2), 235–249.

Razali, N. M., & Wah, Y. B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors, and Anderson-Darling tests. Journal of Statistical Modeling and Analytics, 2(1), 21–33.

Ruxton, G. D. (2019). The unequal variance t-test is an underused alternative to Student’s t-test and the Mann-Whitney U test. Behavioral Ecology, 30(4), 962–968. https://doi.org/10.1093/beheco/arz020

Schmid, R. F., Bernard, R. M., Borokhovski, E., Tamim, R. M., Abrami, P. C., Wade, C. A., Surkes, M. A., & Woods, J. (2014). The effects of technology use in postsecondary education: A meta-analysis of classroom applications. Computers & Education, 72, 271–291. https://doi.org/10.1016/j.compedu.2013.11.002

Sheskin, D. J. (2019). Handbook of parametric and nonparametric statistical procedures (5th ed.). Chapman & Hall/CRC.

Stevens, J. P. (2021). Applied multivariate statistics for the social sciences (6th ed.). Routledge.

Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.

Wilcox, R. R. (2017). Introduction to robust estimation and hypothesis testing (4th ed.). Academic Press.

Yap, B. W., & Sim, C. H. (2018). Comparisons of various types of normality tests. Journal of Statistical Computation and Simulation, 88(16), 3033–3051. https://doi.org/10.1080/00949655.2018.1468993

Zucker, A. A., & Light, D. (2013). Laptop programs for students. Science, 323(5910), 82-85.