OPTIMALISASI LITERASI MATEMATIS MAHASISWA CALON GURU BERDASARKAN NILAI UJIAN MASUK: PENDEKATAN PREDIKTIF DAN KLASIFIKASI

Kurnia Putri Sepdikasari Dirgantoro
Dadan Dasari
Al Jupri


DOI: https://doi.org/10.29100/jp2m.v11i2.7646

Abstract


Literasi matematis merupakan keterampilan penting bagi mahasiswa calon guru dalam membangun pemahaman matematika yang mendalam dan aplikatif bagi siswa di masa depan. Oleh karena itu, penting untuk menelusuri faktor-faktor awal yang berkontribusi terhadap kemampuan literasi matematis mahasiswa, salah satunya melalui analisis nilai ujian masuk. Penelitian ini bertujuan untuk mengembangkan model prediksi literasi matematis mahasiswa berdasarkan nilai ujian saringan masuk serta melakukan klasifikasi mahasiswa berdasarkan kesamaan kemampuan akademik mereka. Data yang digunakan meliputi nilai ujian masuk pada mata pelajaran matematika, bahasa Indonesia, bahasa Inggris, serta APM (Analisis Potensi Mental) dan skor literasi matematis. Metode regresi linear dipilih untuk mengembangkan model prediksi literasi matematis. Selain itu, metode clustering K-means, diterapkan untuk mengelompokkan mahasiswa berdasarkan nilai ujian masuk. Hasil penelitian menunjukkan bahwa skor matematika dan APM memberikan kontribusi signifikan dalam memprediksi literasi matematis. Selanjutnya, analisis clustering menghasilkan beberapa kelompok mahasiswa dengan karakteristik akademik yang berbeda, yang memungkinkan pendekatan pembelajaran yang lebih spesifik dan efektif. Kesimpulan dari penelitian ini menunjukkan bahwa model prediktif dan klasifikasi berdasarkan nilai ujian masuk dapat menjadi alat bantu yang bermanfaat untuk mengidentifikasi kebutuhan belajar mahasiswa secara lebih tepat dalam mengembangkan literasi matematis calon guru.

Keywords


clustering; literasi matematis; mahasiswa calon guru; orange data mining; ujian saringan masuk

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Azmi, B. N., Hermawan, A., & Avianto, D. (2023). Analisis Pengaruh Komposisi Data Training dan Data Testing pada Penggunaan PCA dan Algoritma Decision Tree untuk Klasifikasi Penderita Penyakit Liver. JTIM : Jurnal Teknologi Informasi Dan Multimedia, 4(4), 281–290. https://doi.org/10.35746/jtim.v4i4.298

Bhurre, S., Raikwar, S., Prajapat, S., & Pathak, D. (2024). Analyzing and Comparing Clustering Algorithms for Student Academic Data BT - Advances in Computational Intelligence Systems. In N. Naik, P. Jenkins, P. Grace, L. Yang, & S. Prajapat (Eds.), UK Workshop on Computational Intelligence (pp. 640–651). Springer Nature Switzerland.

Bolstad, O. H. (2023). Lower secondary students’ encounters with mathematical literacy. Mathematics Education Research Journal, 35(1), 237–253. https://doi.org/10.1007/s13394-021-00386-7

Buchari, A. (2018). Peran Guru Dalam Pengelolaan Pembelajaran. Jurnal Ilmiah Iqra, 12, 1693–5705.

Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, 1–24. https://doi.org/10.7717/PEERJ-CS.623

Dirgantoro, K. P. S. (2018). Kompetensi Guru Matematika Dalam Mengembangkan Kompetensi Matematis Siswa. Scholaria: Jurnal Pendidikan Dan Kebudayaan, 8(2), 157–166. https://doi.org/10.24246/j.js.2018.v8.i2.p157-166

Genc, M., & Erbas, A. K. (2019). Secondary mathematics teachers’ conceptions of mathematical literacy. International Journal of Education in Mathematics, Science and Technology, 7(3), 222–237. https://files.eric.ed.gov/fulltext/EJ1223953.pdf

Haeruman, L. D., Salsabila, E., & Kharis, S. A. A. (2024). The Impact of Mathematical Reasoning and Critical Thinking Skills on Mathematical Literacy Skills. KnE Social Sciences, 2024, 542–550. https://doi.org/10.18502/kss.v9i13.15957

Indriyanti, A. D., Prehanto, D. R., & Vitadiar, T. Z. (2021). K-means method for clustering learning classes. Indonesian Journal of Electrical Engineering and Computer Science, 22(2), 835–841. https://doi.org/10.11591/ijeecs.v22.i2.pp835-841

Isnani, T., Handoko, H., & Saluky. (2023). Analysis of Students’ Mathematical Literacy Ability in Solving Mathematical Problems in View of Logical Intelligence. Educational Insights, 1(2), 41–57. https://doi.org/10.58557/eduinsights.v1i2.9

Jahagirdar, S. A. S., R, T., & S H, M. (2023). Regression-Driven Predictive Model to Estimate Learners’ Performance through Multisource Data. 2023 2nd International Conference on Futuristic Technologies (INCOFT), 1–6. https://doi.org/10.1109/INCOFT60753.2023.10425033

Jobson, J. D. (1991). Multiple Linear Regression BT - Applied Multivariate Data Analysis: Regression and Experimental Design (J. D. Jobson (ed.); pp. 219–398). Springer New York. https://doi.org/10.1007/978-1-4612-0955-3_4

Karaboga, H. A., Akogul, S., & Demir, İ. (2022). Classification of Students’ Mathematical Literacy Score Using Educational Data Mining: PISA 2015 Turkey Application. Cumhuriyet Science Journal, 43(3), 543–549. https://doi.org/10.17776/csj.1136733

Laamena, C. M., & Laurens, T. (2021). Mathematical Literacy Ability and Metacognitive Characteristics of Mathematics Pre-Service Teacher. Infinity Journal, 10(2), 259–270. https://doi.org/10.22460/infinity.v10i2.p259-270

Laitochová, J., Uhlířová, M., & Rusnoková, N. (2021). Mathematical Literacy From the Perspective of Prospective Teachers. ICERI2021 Proceedings, 1, 1710–1715. https://doi.org/10.21125/iceri.2021.0465

Liao, T. W., Wang, G., Triantaphyllou, E., Rouge, B., & Chang, P. (2001). A Data Mining Study of Weld Quality Models Constructed with MLP Neural Networks from Stratified Sampled Data. Industial Engineering Research Conference, 6.

Liz-Domínguez, M., Caeiro-Rodríguez, M., Llamas-Nistal, M., & Mikic-Fonte, F. A. (2019). Systematic literature review of predictive analysis tools in higher education. Applied Sciences (Switzerland), 9(24). https://doi.org/10.3390/app9245569

Marill, K. A. (2004). Advanced statistics: linear regression, part II: multiple linear regression. Academic Emergency Medicine : Official Journal of the Society for Academic Emergency Medicine, 11(1), 94–102. https://doi.org/10.1197/j.aem.2003.09.006

Murayama, K., Pekrun, R., Lichtenfeld, S., & vom Hofe, R. (2013). Predicting long-term growth in students’ mathematics achievement: The unique contributions of motivation and cognitive strategies. Child Development, 84(4), 1475–1490. https://doi.org/10.1111/cdev.12036

Nguyen, A., Nguyen, D., Ta, P., & Tran, T. (2019). Preservice Teachers Engage in a Project-based Task: Elucidate Mathematical Literacy in a Reformed Teacher Education Program. International Electronic Journal of Mathematics Education, 15(1), 657–666. https://doi.org/10.29333/iejme/5778

OECD. (2023). Program For International Student (PISA) 2022 Assessment and Analytical Framework. In OECD Publishing. https://www.oecd-ilibrary.org/education/pisa-2022-assessment-and-analytical-framework_dfe0bf9c-en

Omiralievna, M. B. (2024). Development and Cultivation of Mathematical Literacy: A Pedagogical Perspective. Eurasian Science Review An International Peer-Reviewed Multidisciplinary Journal, 2(4), 93–99. https://doi.org/10.63034/esr-55

Orsoni, M., Giovagnoli, S., Garofalo, S., Magri, S., Benvenuti, M., Mazzoni, E., & Benassi, M. (2023). Preliminary evidence on machine learning approaches for clusterizing students’ cognitive profile. Heliyon, 9(3), e14506. https://doi.org/10.1016/j.heliyon.2023.e14506

Osic, M. A. (2023). Teachers’ Competence, Classroom Environment, Learning Style of Students: A Structural Model on Mathematical Ability. International Journal on Emerging Mathematics Education, 6(1), 77. https://doi.org/10.12928/ijeme.v6i1.21215

Podungge, R., Rahayu, M., Setiawan, M., & Sudiro, A. (2020). Teacher Competence and Student Academic Achievement. 144(16), 69–74. https://doi.org/10.2991/aebmr.k.200606.011

Rahim, M. E., Gani, M. A., Lestari, M., & Mutmainnah, M. (2023). Gaya Belajar yang Berpengaruh Terhadap Kemampuan Literasi matematis: Literatur Review. Griya Journal of Mathematics Education and Application, 3(2), 303–312. https://doi.org/10.29303/griya.v3i2.320

Rahmanuri, A., Winarni, R., & Surya, A. (2023). Faktor-faktor yang memengaruhi literasi matematis: systematic literature review. Didaktika Dwija Indria, 11(6), 1. https://doi.org/10.20961/ddi.v11i6.78579

Ranjan, R., & Ranjan, J. (2016). Predicting parameters importance in higher education using data analysis: A regression model. Proceedings - 2016 2nd International Conference on Computational Intelligence and Communication Technology, CICT 2016, 605–608. https://doi.org/10.1109/CICT.2016.126

Rivai, A., Lestari, A., Munir, N. P., & Anas, A. (2022). Students’ Mathematical Literacy in Solving PISA Problems Observed by Learning Styles. Jurnal Pendidikan Matematika, 17(1), 121–134. https://doi.org/10.22342/jpm.17.1.19905.121-134

Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20(C), 53–65. https://doi.org/10.1016/0377-0427(87)90125-7

Sakinah, M., & Avip, P. B. (2021). An analysis of students’ mathematical literacy skills assessed from students’ learning style. Journal of Physics: Conference Series, 1882(1), 0–8. https://doi.org/10.1088/1742-6596/1882/1/012075

Serin, H. (2023). The Significance of Mathematical Literacy in Today’s Society. International Journal of Social Sciences & Educational Studies, 10(2), 396–402. https://doi.org/10.23918/ijsses.v10i2p396

Shahapure, K. R., & Nicholas, C. (2020). Cluster quality analysis using silhouette score. 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020, 747–748. https://doi.org/10.1109/DSAA49011.2020.00096

Singh, M. K., Rani, A., & Sharma, R. R. (2014). An Optimised Approach For Student’s Academic Performance By K-Means Clustering Algorithm Using Weka Interface. International Journal of Advanced Computational Engineering and Networking, 2(7), 5–9. https://api.semanticscholar.org/CorpusID:212612583

Sugiyono. (2013). METODE PENELITIAN KUANTITATIF, KUALITATIF dan R&D. Alfabeta.

Sümen, Ö. Ö., & Çalışıcı, H. (2016). The Relationships Between Preservice Teachers’ Mathematical Literacy Self Efficacy Beliefs, Metacognitive Awareness And Problem Solving Skills. Participatory Educational Research, spi16(2), 11–19. https://doi.org/10.17275/per.16.spi.2.2

Szabo, Z. K., Körtesi, P., Guncaga, J., Szabo, D., & Neag, R. (2020). Examples of problem-solving strategies in mathematics education supporting the sustainability of 21st-century skills. Sustainability (Switzerland), 12(23), 1–28. https://doi.org/10.3390/su122310113

Tariq, V. N., Qualter, P., Roberts, S., Appleby, Y., & Barnes, L. (2013). Mathematical literacy in undergraduates: role of gender, emotional intelligence and emotional self-efficacy. International Journal of Mathematical Education in Science and Technology, 44(8), 1143–1159. https://doi.org/10.1080/0020739X.2013.770087

Trpin, A. (2023). Classroom Management and the Vital Role of the Classroom Teacher: Insights from Distance Teaching Experiences. Journal of E-Learning Research, 2(2), 39–51. https://doi.org/10.33422/jelr.v2i2.486

Uygur Kabael, T., & Ata Baran, A. (2023). An Investigation of Mathematics Teachers’ Conceptions of Mathematical Literacy Related to Participation in a Web-Based PISA Course. Bartın Üniversitesi Eğitim Fakültesi Dergisi, 12(2), 315–324. https://doi.org/10.14686/buefad.1053557

Yustitia, V., Amin, S. M., & Abadi. (2020). Mathematical literacy in pre-service elementary school teacher: A case study. Journal of Physics: Conference Series, 1613(1). https://doi.org/10.1088/1742-6596/1613/1/012054

Zhang, C. (2011). Research on K-means Clustering Algorithm. Journal of Changchun Normal University. https://api.semanticscholar.org/CorpusID:63573605

Zhou, X., An, J., Zhao, X., & Dong, Y. (2016). Using data mining on students’ learning features: A clustering approach for student classification. Journal of Advanced Computational Intelligence and Intelligent Informatics, 20(7), 1141–1146. https://doi.org/10.20965/jaciii.2016.p1141