Title Inclusive Educational Data Mining Framework Using Classification, Clustering, and Association Rules for Parent–Child Interactions and Reflective Learning
DOI:
https://doi.org/10.52436/1.jutif.2026.7.4.5656Keywords:
Educational Data Mining, Family-Centered Learning, Inclusive Education, Reflective Learning, Student ProfilingAbstract
Inclusive education requires integrated academic, social, and emotional support systems to address learner diversity; however, existing Educational Data Mining (EDM) research has largely concentrated on academic or log-based data, with limited attention to family-centered dynamics. This study aims to develop and validate a family-centered EDM framework to examine how the quality of parent–child interactions predicts students’ reflective learning behaviors. A cross-sectional survey was conducted with 150 junior and senior high school students from urban Indonesian schools using the Parent–Child Inclusivity Scale (PCIS) and the Self-Assessment Behavior Inventory (SABI). Data were analyzed using descriptive statistics, reliability testing, and Confirmatory Factor Analysis, followed by EDM techniques including Decision Tree and Naïve Bayes classification, K-Means clustering, and Apriori association rule mining. The classification models achieved accuracies of 82% and 78%, while clustering revealed three distinct inclusivity–reflection profiles. Association rules further identified key combinations of autonomy support, constructive feedback, and joint decision-making that consistently predict high levels of reflective behavior. By explicitly integrating family-centered variables into predictive, profiling, and rule-based EDM analyses, this study advances Informatics by proposing a novel family-centered EDM framework that bridges data analytics with inclusive educational practice and adaptive learning system design.
Downloads
References
L. Sebti and B. C. Elder, ‘“Inclusion is definitely a possibility for all”: Promoting inclusive education through a critical professional development schools model’, Sch.-Univ. Partnersh., vol. 17, no. 3, pp. 285–302, Oct. 2024, doi: 10.1108/SUP-06-2023-0021.
A. C. Rapp and A. Corral-Granados, ‘Understanding inclusive education – a theoretical contribution from system theory and the constructionist perspective’, Int. J. Incl. Educ., vol. 28, no. 4, pp. 423–439, Mar. 2024, doi: 10.1080/13603116.2021.1946725.
A. R. Sadownik, ‘Bronfenbrenner: Ecology of Human Development in Ecology of Collaboration’, in (Re)theorising More-than-parental Involvement in Early Childhood Education and Care, vol. 40, A. R. Sadownik and A. Višnjić Jevtić, Eds, in International Perspectives on Early Childhood Education and Development, vol. 40. , Cham: Springer International Publishing, 2023, pp. 83–95. doi: 10.1007/978-3-031-38762-3_4.
L. Bouffard, ‘Ryan, R. M. et Deci, E. L. (2017). Self-determination theory. Basic psychological needs in motivation, development and wellness. New York, NY : Guilford Press’, Rev. Québécoise Psychol., vol. 38, no. 3, pp. 231–234, Nov. 2017, doi: 10.7202/1041847ar.
Y. Wang, H. Wang, S. Wang, S. A. Wind, and C. Gill, ‘A systematic review and meta-analysis of self-determination-theory-based interventions in the education context’, Learn. Motiv., vol. 87, p. 102015, Aug. 2024, doi: 10.1016/j.lmot.2024.102015.
W. Kassis, A. Vasiou, C. Govaris, C. Favre, D. Aksoy, and U. Graf, ‘Social and Individual Factors Predicting Students’ Resilience: A Multigroup Structural Equation Model’, Educ. Sci., vol. 14, no. 1, p. 15, Dec. 2023, doi: 10.3390/educsci14010015.
W. Chen, Y. Sun, and Y. He, ‘The Relationship between Parental Autonomy Support and Children’s Self-Concept in China—The Role of Basic Psychological Needs’, Behav. Sci., vol. 14, no. 5, p. 415, May 2024, doi: 10.3390/bs14050415.
C. Romero and S. Ventura, ‘Educational Data Mining: A Review of the State of the Art’, IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., vol. 40, no. 6, pp. 601–618, Nov. 2010, doi: 10.1109/TSMCC.2010.2053532.
R. S. Baker and P. S. Inventado, ‘Educational Data Mining and Learning Analytics’, in Learning Analytics, J. A. Larusson and B. White, Eds, New York, NY: Springer New York, 2014, pp. 61–75. doi: 10.1007/978-1-4614-3305-7_4.
M. M. Hussain, S. Akbar, S. A. Hassan, M. W. Aziz, and F. Urooj, ‘Prediction of Student’s Academic Performance through Data Mining Approach’, J. Inform. Web Eng., vol. 3, no. 1, pp. 241–251, Feb. 2024, doi: 10.33093/jiwe.2024.3.1.16.
C. Romero and S. Ventura, ‘Educational data mining and learning analytics: An updated survey’, 2024, doi: 10.48550/ARXIV.2402.07956.
E. Kalita et al., ‘Educational data mining: a 10-year review’, Discov. Comput., vol. 28, no. 1, p. 81, May 2025, doi: 10.1007/s10791-025-09589-z.
D. R. Ocy, I. Sarifah, and R. Riyadi, ‘EFA and CFA analysis: Development and validation of a test instrument for mathematical abstraction skills’, JRAMathEdu J. Res. Adv. Math. Educ., Apr. 2025, doi: 10.23917/jramathedu.v10i2.7601.
J. A. Durlak, R. P. Weissberg, A. B. Dymnicki, R. D. Taylor, and K. B. Schellinger, ‘The Impact of Enhancing Students’ Social and Emotional Learning: A Meta-Analysis of School-Based Universal Interventions’, Child Dev., vol. 82, no. 1, pp. 405–432, Jan. 2011, doi: 10.1111/j.1467-8624.2010.01564.x.
E. Panadero, A. Jonsson, and J. Botella, ‘Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-analyses’, Educ. Res. Rev., vol. 22, pp. 74–98, Nov. 2017, doi: 10.1016/j.edurev.2017.08.004.
M. Özgenel and S. Avcı, ‘Parental and Teacher Autonomy Support in Developing Self-Regulation Skills’, Behav. Sci., vol. 15, no. 12, p. 1621, Nov. 2025, doi: 10.3390/bs15121621.
Y. Li, R. Sueb, and K. Said Hashim, ‘The relationship between parental autonomy support, teacher autonomy support, peer support, and university students’ academic engagement: the mediating roles of basic psychological needs and autonomous motivation’, Front. Psychol., vol. 16, p. 1503473, Apr. 2025, doi: 10.3389/fpsyg.2025.1503473.
L. Song, Q. Zhan, L. Cao, and R. Luo, ‘Parent autonomy support and undergraduates’ academic engagement in online learning: the mediate role of self-regulation’, Psicol. Reflex. E Crítica, vol. 37, no. 1, p. 45, Nov. 2024, doi: 10.1186/s41155-024-00330-1.
H. Jang, E. J. Kim, and J. Reeve, ‘Why students become more engaged or more disengaged during the semester: A self-determination theory dual-process model’, Learn. Instr., vol. 43, pp. 27–38, Jun. 2016, doi: 10.1016/j.learninstruc.2016.01.002.
S. A. A. Kharis and A. H. A. Zili, ‘Learning Analytics dan Educational Data Mining pada Data Pendidikan’, J. Ris. PEMBELAJARAN Mat. Sekol., vol. 6, no. 1, pp. 12–20, Mar. 2022, doi: 10.21009/jrpms.061.02.
G. Lampropoulos and G. Evangelidis, ‘Learning Analytics and Educational Data Mining in Augmented Reality, Virtual Reality, and the Metaverse: A Systematic Literature Review, Content Analysis, and Bibliometric Analysis’, Appl. Sci., vol. 15, no. 2, p. 971, Jan. 2025, doi: 10.3390/app15020971.
M. J. Rotheram-Borus, D. Swendeman, E. Rotheram-Fuller, and M. K. Youssef, ‘Family Coaching as a delivery modality for evidence-based prevention programs’, Clin. Child Psychol. Psychiatry, vol. 23, no. 1, pp. 96–109, Jan. 2018, doi: 10.1177/1359104517721958.
Y. Akamoglu and L. Dinnebeil, ‘Coaching Parents to Use Naturalistic Language and Communication Strategies’, Young Except. Child., vol. 20, no. 1, pp. 41–50, Mar. 2017, doi: 10.1177/1096250615598815.
J. Fonseca et al., ‘Changing Teachers’ Feedback Practices: A Workshop Challenge’, Aust. J. Teach. Educ., vol. 40, no. 40, Jan. 2015, doi: 10.14221/ajte.2015v40n8.4.
Rogers Kaliisa, ‘Designing Learning Analytics Tools for Teachers with Teachers A Design-Based Research Study in a Blended Higher Education Context’, 2022, doi: 10.13140/RG.2.2.10558.97605.
A. Olteanu, C. Castillo, F. Diaz, and E. Kıcıman, ‘Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries’, Front. Big Data, vol. 2, p. 13, Jul. 2019, doi: 10.3389/fdata.2019.00013.
C. Maier, J. B. Thatcher, V. Grover, and Y. K. Dwivedi, ‘Cross-sectional research: A critical perspective, use cases, and recommendations for IS research’, Int. J. Inf. Manag., vol. 70, p. 102625, Jun. 2023, doi: 10.1016/j.ijinfomgt.2023.102625.
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Handoko, Handri Santoso
This work is licensed under a Creative Commons Attribution 4.0 International License.
</a
