DESIGNING A MULTICULTURAL HYBRID LEARNING FRAMEWORK FOR SOCIAL STUDIES EDUCATION IN A DIVERSE INDONESIAN CLASSROOM
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hybrid learning, inclusive education, Indonesia
Abstract
The diversity in Indonesian classrooms presents both opportunities and challenges for social studies education. The evolving cultural and ethnic backgrounds of students necessitate the development of an inclusive and adaptable educational framework. This study explores the design of a multicultural hybrid learning framework tailored for social studies education in Indonesia. The primary objective of the research is to develop an effective, culturally responsive teaching model that integrates both traditional and digital learning methods to cater to diverse student needs. A mixed-methods approach was employed, combining qualitative interviews with social studies educators and quantitative surveys of students across different regions. The research findings indicate that the proposed hybrid framework fosters a more engaging and equitable learning environment by incorporating culturally relevant content, interactive digital tools, and collaborative learning strategies. Furthermore, the study highlights the importance of teacher training in multicultural competence and the integration of technology in enhancing students' critical thinking and social awareness. The study concludes that a hybrid learning model, when thoughtfully implemented, can significantly improve the inclusivity and effectiveness of social studies education in a multicultural context. This research contributes to the broader field of education by offering a model for adapting social studies teaching in diverse classrooms.
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References
Aghanouri, A., Smirnov, N., & Olaverri-Monreal, C. (2025). Urban hydrogen adoption in Linz, Austria: Simulation and statistical detection of anomalies in sustainable mobility. Applied Energy, 401. Scopus. https://doi.org/10.1016/j.apenergy.2025.126628
Al Sailawi, A. S., Hameed Al-Hamzawi, H. A., & Mijwil, M. M. (2025). Sensitivity analysis and optimization of PEMFCs for realistic dynamic operating conditions. Journal of Power Sources, 659. Scopus. https://doi.org/10.1016/j.jpowsour.2025.238335
Ally, S., Verstraeten, T., Nowé, A., & Helsen, J. (2025). Day-ahead trading and power control for hybrid wind-hydrogen plants with multi-agent reinforcement learning. Applied Energy, 401. Scopus. https://doi.org/10.1016/j.apenergy.2025.126588
Bhasker, J. P., Shaik, A., Porpatham, E., Jambulingam, J., & Kasianantham, N. (2025). Hybridized predictive modelling of hydrogen addition with compressed natural gas in a lean burn spark ignition engine. Energy Conversion and Management, 346. Scopus. https://doi.org/10.1016/j.enconman.2025.120431
Ebrahimi, P., Asjodi, A. H., & Dolatshahi, K. M. (2025). Quantifying hybrid failure mode in cyclic-loaded reinforced concrete shear walls: Integrating unsupervised and supervised learning techniques. Engineering Applications of Artificial Intelligence, 162. Scopus. https://doi.org/10.1016/j.engappai.2025.112346
Fu, M., Basem, A., Samad, S., Bayz, D. A., Alhumaid, S., Kumar Dutta, A. K., Ali, H. E., Jastaneyah, Z., Alkhalaf, S., & Mahariq, I. (2025). Two-bed adsorption refrigeration cycle integration into a hybrid biomass-gasification multigeneration system for sustainable energy production: Comprehensive 4E analysis, and machine learning optimization. Applied Thermal Engineering, 281. Scopus. https://doi.org/10.1016/j.applthermaleng.2025.128615
Gürdal, M., Tan, M., Gürsoy, E., Arslan, K., & Gedik, E. (2025). Comparative machine learning prediction study of hybrid nanofluid flow in a magnetized dimpled tube. Applied Thermal Engineering, 281. Scopus. https://doi.org/10.1016/j.applthermaleng.2025.128569
He, W., Liu, M., & Yu, Y. (2025). Hybrid mask generation for infrared small target detection with single-point supervision. Neurocomputing, 658. Scopus. https://doi.org/10.1016/j.neucom.2025.131688
Huang, J., Li, B., Liu, X., Ye, G., Xu, Z., Yang, Z., Li, Y., He, S., & Wang, J. (2025). Predicting thermophysical properties of amine solutions: A hybrid machine learning framework with synthetic data augmentation. Separation and Purification Technology, 377. Scopus. https://doi.org/10.1016/j.seppur.2025.134452
Jiang, Z., Ying, J., Yu, Z., Chu, X., & Yu, C. (2025). A novel weight-optimized machine-learning hybrid model for daily river runoff prediction. Engineering Applications of Artificial Intelligence, 162. Scopus. https://doi.org/10.1016/j.engappai.2025.112396
Lei, C., Wei, D., Hang, S., Yutian, C., Congling, T., Gao, G., Wu, W., & Dong, D. (2025). Nonlinear dynamic modeling of turbojet engines using combined convolutional and long short-term memory networks. Engineering Applications of Artificial Intelligence, 162. Scopus. https://doi.org/10.1016/j.engappai.2025.112393
Li, Q., Zhang, Q., & Zhang, L. (2025). (Off-) Design performance analysis and prediction of micro gas turbine using hybrid fuels (NG & H2) based on dynamic model and neural network. Energy Conversion and Management, 346. Scopus. https://doi.org/10.1016/j.enconman.2025.120473
Li, X., Zhang, D., & Chen, Y. (2025). Uncertainty-aware deep distributed reinforcement learning for autonomous navigation of unmanned surface vehicles in complex environments. Ocean Engineering, 342. Scopus. https://doi.org/10.1016/j.oceaneng.2025.122899
Lin, Z., Wang, J., Fu, Z., Zhong, D., Du, X., & Zhang, J. (2025). Synergistic modification of TiO2-SiO2/PES composite membranes in gas-liquid contactor enables ultrahigh CO2/CH4 separation with machine learning-driven optimization. Separation and Purification Technology, 378. Scopus. https://doi.org/10.1016/j.seppur.2025.134708
Liu, H., & Li, L. (2025). Explainable machine learning combined with FT-ICR MS unveils the evolution of dissolved organic nitrogen during advanced wastewater treatment processes. Separation and Purification Technology, 378. Scopus. https://doi.org/10.1016/j.seppur.2025.134790
Lu, R., Liao, R., Meng, R., Guo, Y., Zhang, Y., Shi, Z., & Ye, S. (2025). Strategic sampling for training a semantic segmentation model in operational mapping: Case studies on cropland parcel extraction. Remote Sensing of Environment, 331. Scopus. https://doi.org/10.1016/j.rse.2025.115034
Ma, H., Wang, Q., Li, W., Chen, Y., Xu, J., Ma, Y., Huang, J., & Liang, S. (2025). The first gap-free 20 m 5-day LAI/FAPAR products over China (2018–2023) from integrated Landsat-8/9 and Sentinel-2 Analysis Ready Data. Remote Sensing of Environment, 331. Scopus. https://doi.org/10.1016/j.rse.2025.115048
Mahalakshmi, V., & Karthikeyan, B. (2025). Context reasoning-based vehicle traffic accident severity prediction using a machine learning algorithm in edge computing. Engineering Research Express, 7(4). Scopus. https://doi.org/10.1088/2631-8695/ae06f5
Mahapatra, D. K., & Jena, B. (2025). ML-driven investigation of non-ideal effects in multi-gate MOSFET characteristics for sub-10 nm technologies. Engineering Research Express, 7(4). Scopus. https://doi.org/10.1088/2631-8695/ae0250
Mehta, B., Bharany, S., Ghoniem, R. M., Kaur, U., & Tran, T. A. (2025). HAMSCNN: A hybrid attention multi-scale CNN for accurate ship detection in maritime surveillance. Regional Studies in Marine Science, 91. Scopus. https://doi.org/10.1016/j.rsma.2025.104493
Miao, L., Wang, J., Wu, K., Xu, H., Sun, X., Lu, G., & Kwan, M.-P. (2025). Spatiotemporal hybrid deep learning for estimating and analyzing carbon stocks: A case study in Jiangsu province, China. International Journal of Digital Earth, 18(1). Scopus. https://doi.org/10.1080/17538947.2025.2534008
Mirza, F., Mack, J. P., Duan, Z. H., & Tan, K. T. (2025). Predicting bending after impact failure mode and strength of hybrid sandwich composites: A machine learning approach. Engineering Structures, 345. Scopus. https://doi.org/10.1016/j.engstruct.2025.121493
Nasir, M., Bansal, R. C., & Saloumi, M. (2025). Reinforcement learning algorithms in AC, DC, and hybrid microgrids applications: A comprehensive review. Applied Energy, 401. Scopus. https://doi.org/10.1016/j.apenergy.2025.126724
Pourmir, M., & Miri, S. M. (2025). Machine learning methods comparison by using statistical tests in solar energy forecasting based on weather features. Engineering Applications of Artificial Intelligence, 162. Scopus. https://doi.org/10.1016/j.engappai.2025.112239
Rabbi, M. F. (2025). Cross-framework hybrid artificial intelligence for high-penetration renewable energy integration: Multi-regional forecasting and adaptive control. Applied Energy, 401. Scopus. https://doi.org/10.1016/j.apenergy.2025.126834
Regazzoni, S. (2025). Losses and Abandonments in Clara Obligado’s Una casa lejos de casa. La escritura extranjera. Oltreoceano, 2024(23), 229–237. Scopus. https://doi.org/10.53154/Oltreoceano112
Sahu, N., Azad, C., & Kumar, U. (2025). Interpretable and highly accurate tertiary tree-based ensemble hybrid models for the prediction of photocurrent density and electrode potential in PEC cell: Theoretically supported and externally validated by experimental data. Applied Energy, 401. Scopus. https://doi.org/10.1016/j.apenergy.2025.126691
Sai Pallavi, A., & Rama Sudha, K. (2025). Robust low-frequency oscillations damping in hybrid renewable energy systems using advanced controller and optimization. Engineering Research Express, 7(4). Scopus. https://doi.org/10.1088/2631-8695/adf522
Shao, Z., Zhu, G., Han, Y., Zha, J., Yang, C., & Li, F. (2025). Multi-distribution fusion based Bayesian deep neural network for short-term probabilistic electricity price forecasting. Applied Energy, 401. Scopus. https://doi.org/10.1016/j.apenergy.2025.126712
Shekhar, S., Gauchía, L., Amarís, H., Pérez-Borondo, Á., & Hernández, C. (2025). Effect of variable temperatures on machine learning battery SoH estimation for auxiliary aircraft batteries. Journal of Power Sources, 660. Scopus. https://doi.org/10.1016/j.jpowsour.2025.238451
Tang, D., Liu, Z., Zeng, Y., Xu, Z., & Su, W. (2025). A mamba-quantum attention transformer-convolutional network for automated pest and disease detection. Engineering Applications of Artificial Intelligence, 162. Scopus. https://doi.org/10.1016/j.engappai.2025.112480
van Reen, S., Serbülent, B., & Yao, H.-D. (2025). Machine learning-based multipoint optimisation for improving aerodynamics of symmetrically cambered wing sails in wind-assisted ship propulsion. Ocean Engineering, 342. Scopus. https://doi.org/10.1016/j.oceaneng.2025.122829
Varol, M., & ?skefiyeli, M. (2025). An intrusion detection system for critical infrastructures: Modbus approach. Engineering Applications of Artificial Intelligence, 162. Scopus. https://doi.org/10.1016/j.engappai.2025.112410
Wang, X., Zhang, Y., Yang, L., & Wang, R. (2025). Integrating multimodal biophysical features with hybrid deep learning for ribonucleic acid secondary structure prediction. Engineering Applications of Artificial Intelligence, 162. Scopus. https://doi.org/10.1016/j.engappai.2025.112648
Wu, H., Gui, M., & Wu, D. (2025). Physics-Informed hybrid machine learning for critical heat flux prediction: A comparative analysis of modeling approaches. Nuclear Engineering and Design, 445. Scopus. https://doi.org/10.1016/j.nucengdes.2025.114434
Xue, J., Yang, C., Fang, J., Zhang, X., & Wang, M. (2025). Real-time dynamic coordinated optimization control with near-global optimal learning for connected plug-in hybrid electric vehicles. Engineering Applications of Artificial Intelligence, 162. Scopus. https://doi.org/10.1016/j.engappai.2025.112602
Yan, C., Chen, S., Xu, J., Wang, X., & Peng, Z. (2025). Hybrid Reinforcement Learning in parameterized action space via fluctuates constraint. Engineering Applications of Artificial Intelligence, 162. Scopus. https://doi.org/10.1016/j.engappai.2025.112499
Yan, H., Chu, Z., & Tang, J. (2025). A short-term ship motion prediction method based on quaternions and transformer-LSTM model. Ocean Engineering, 342. Scopus. https://doi.org/10.1016/j.oceaneng.2025.122874
Zhai, J., Guo, L., Wang, Z., Zhu, J., Li, X., & Wang, C. (2025). Interdependence modeling of wind farm frequency support feasible region: A non-iterative system-wide dynamic characteristics scheduling. Applied Energy, 401. Scopus. https://doi.org/10.1016/j.apenergy.2025.126687
Zhang, A., & Vanapalli, S. K. (2025). Predicting the compression index of expansive soils with hybrid machine learning approaches. Engineering Applications of Artificial Intelligence, 162. Scopus. https://doi.org/10.1016/j.engappai.2025.112579
Zhang, L., Huang, D., & Wang, H. (2025). Hybrid physics-based and data-driven method for the rotor angle prediction. Engineering Applications of Artificial Intelligence, 162. Scopus. https://doi.org/10.1016/j.engappai.2025.112533
Zhang, T., Shi, R., Jia, L., & Lee, K. Y. (2025). An innovative coordinated control strategy for frequency regulation in power systems with high renewable penetration. Applied Energy, 401. Scopus. https://doi.org/10.1016/j.apenergy.2025.126700
Zhong, X., Lai, D., Yu, Q., & Liao, F. (2025). Probabilistic machine learning with a cascaded framework for robust failure mode classification and capacity estimation of rectangular concrete-filled steel tube columns under axial compression. Engineering Applications of Artificial Intelligence, 162. Scopus. https://doi.org/10.1016/j.engappai.2025.112581
Zhu, C., Li, J., Bao, H., Hu, K., & Xu, K. (2025). A novel hybrid spatiotemporal model for thermal distribution of lithium-ion batteries. Journal of Energy Storage, 139. Scopus. https://doi.org/10.1016/j.est.2025.118827
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Copyright (c) 2026 Ali Al-Jubouri, Wahju Dyah Laksmi Wardhani, Tasnia Islam Islam, Cipto Duwi Priyono
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