Pemodelan Profil Siswa SMP Berdasarkan Mathematics Self-Regulated Learning Menggunakan Metode K-Means Clustering

Munaji Munaji

doi:10.26877/imajiner.v8i3.27185

Pemodelan Profil Siswa SMP Berdasarkan Mathematics Self-Regulated Learning Menggunakan Metode K-Means Clustering

Munaji Munaji

Abstract

Mathematics achievement at the junior high school level remains a significant challenge, with non-cognitive factors such as self-regulated learning (SRL) playing a critical role alongside cognitive ability. This study aims to develop a mathematical model for classifying Mathematics SRL profiles of junior high school students in Cirebon City using K-Means Clustering. A descriptive-exploratory quantitative design was employed with 68 students selected through stratified random sampling. Data were collected using a validated Mathematics SRL questionnaire (α = 0.823) encompassing four dimensions: Goal Setting, Self-Monitoring, Self-Efficacy, and Metacognition & Motivation. Prior to clustering, data were standardized using Z-score normalization and analyzed through Principal Component Analysis (PCA). The optimal number of clusters (K = 3) was determined via the Elbow Method and Silhouette Index (SI = 0.286). Results identified three distinct student profiles: High (32.4%), Moderate (45.6%), and Low (22.1%), with statistically significant differences across all SRL dimensions (p < 0.001). Self-Efficacy emerged as the most discriminating dimension (F = 70.349). The resulting classification model, C*(xᵢ) = , serves as a practical diagnostic tool for identifying student SRL profiles, enabling educators to design targeted, evidence-based pedagogical interventions.

Keywords

Means Clustering; Mathematics Self-Regulated Learning; Profil Siswa, Unsupervised Machine Learning; Sekolah Menengah Pertama

Full Text:

PDF

References

Abdi, H., & Williams, L. J. (2010). Principal component analysis. WIREs Computational Statistics, 2(4), 433–459. https://doi.org/https://doi.org/10.1002/wics.101

Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Pérez, J. M., & Perona, I. (2013). An extensive comparative study of cluster validity indices. Pattern Recognition, 46(1), 243–256. https://doi.org/10.1016/j.patcog.2012.07.021

Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50(2), 248–287. https://doi.org/https://doi.org/10.1016/0749-5978(91)90022-L

Bednorz, D., & Bruhn, S. (2023). Influence of primary students’ self-regulated learning profiles on their rating of a technology-enhanced learning environment for mathematics. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1074371

Callan, G. L., & Cleary, T. J. (2019). Examining cyclical phase relations and predictive influences of self-regulated learning processes on mathematics task performance. Metacognition and Learning, 14(1), 43–63. https://doi.org/10.1007/s11409-019-09191-x

Cleary, T. J., Slemp, J., & Pawlo, E. R. (2021). Linking student self-regulated learning profiles to achievement and engagement in mathematics. Psychology in the Schools, 58(3), 443–457. https://doi.org/https://doi.org/10.1002/pits.22456

Du, Y., & Liu, H. (2020). Analysis of the Influence of Psychological Contract on Employee Safety Behaviors against COVID-19. International Journal of Environmental Research and Public Health, 17(18). https://doi.org/10.3390/ijerph17186747

Feddermann, M., Möller, J., & Baumert, J. (2021). Effects of CLIL on second language learning: Disentangling selection, preparation, and CLIL-effects. Learning and Instruction, 74, 101459. https://doi.org/https://doi.org/10.1016/j.learninstruc.2021.101459

Granello, F., Cuder, A., Doz, E., Pellizzoni, S., & Passolunghi, M. C. (2025). “I can do math!”: A self-regulated learning intervention to enhance math-related motivational factors and performance in middle school. British Journal of Educational Psychology. https://doi.org/10.1111/bjep.70034

Guo, L., Cao, J., Cheng, P., Shi, D., Cao, B., Yang, G., Liang, S., Su, N., Yu, M., Zhang, C., Liang, R., Wang, Y., Bai, C., Chen, C., & Ren, Z. (2020). Moderate-to-Severe Depression Adversely Affects Lung Function in Chinese College Students. Frontiers in Psychology, Volume 11-2020. https://doi.org/10.3389/fpsyg.2020.00652

Howard, Matt C, & Hoffman, Michael E. (2018). Variable-Centered, Person-Centered, and Person-Specific Approaches: Where Theory Meets the Method. Organizational Research Methods, 21(4), 846–876. https://doi.org/10.1177/1094428117744021

Jollife, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. In Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences (Vol. 374, Number 2065). Royal Society of London. https://doi.org/10.1098/rsta.2015.0202

Kaplan, C., & Bulbul, A. (2021). Goal driven network pruning for object recognition. Pattern Recognition, 110, 107468. https://doi.org/https://doi.org/10.1016/j.patcog.2020.107468

Khalil, M., & Ebner, M. (2017). Clustering patterns of engagement in Massive Open Online Courses (MOOCs): the use of learning analytics to reveal student categories. Journal of Computing in Higher Education, 29(1), 114–132. https://doi.org/10.1007/s12528-016-9126-9

Landa, J., Berciano, A., & Marbán, J. M. (2024). Self-Regulation Profiles of Pre-Service Mathematics Teachers for Primary Education in Mathematical Problem-Solving Contexts. Education Sciences, 14(9). https://doi.org/10.3390/educsci14091018

OECD. (2023). PISA 2022 Results (Volume I): The State of Learning and Equity in Education. , Paris: Volume 1. PISA, OECD Publishing.

Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A Manual for the Use of the Motivated Strategies for Learning Questionnaire (MSLQ).

Qi, H., Cruz, G., Botnar, R., & Prieto, C. (2021). Synergistic multi-contrast cardiac magnetic resonance image reconstruction. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2200), 20200197. https://doi.org/10.1098/rsta.2020.0197

Schunk, D. H., & Greene, J. A. (2017). Handbook of Self-Regulation of Learning and Performance ((2nd ed.)).

Sholiha, T. A., Kurniati, N., Tyaningsih, R. Y., & Prayitno, S. (2022). Pengaruh Self-Regulated Learning (SRL) terhadap Hasil Belajar Matematika Siswa Kelas XI SMAN 1 Masbagik. Jurnal Ilmiah Profesi Pendidikan, 7(3), 1355–1362. https://doi.org/10.29303/jipp.v7i3.745

Sinaga, K. P., & Yang, M. S. (2020). Unsupervised K-means clustering algorithm. IEEE Access, 8, 80716–80727. https://doi.org/10.1109/ACCESS.2020.2988796

Stoner, O., & Economou, T. (2020). An advanced hidden Markov model for hourly rainfall time series. Computational Statistics & Data Analysis, 152, 107045. https://doi.org/https://doi.org/10.1016/j.csda.2020.107045

Trouche, L., Rocha, K., Gueudet, G., & Pepin, B. (2020). Transition to digital resources as a critical process in teachers’ trajectories: the case of Anna’s documentation work. ZDM, 52(7), 1243–1257. https://doi.org/10.1007/s11858-020-01164-8

Wild, S., & Neef, C. (2023). Analyzing the associations between motivation and academic performance via the mediator variables of specific mathematic cognitive learning strategies in different subject domains of higher education. International Journal of STEM Education, 10(1). https://doi.org/10.1186/s40594-023-00423-w

Zimmerman, B. J. (2000). Chapter 2 - Attaining Self-Regulation: A Social Cognitive Perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of Self-Regulation (pp. 13–39). Academic Press. https://doi.org/https://doi.org/10.1016/B978-012109890-2/50031-7

DOI: https://doi.org/10.26877/imajiner.v8i3.27185