Learning Tournament: Inovasi Pembelajaran Kooperatif dalam Konteks Pendidikan Abad 21
DOI:
https://doi.org/10.55849/attasyrih.v11i1.306Keywords:
Learning Tournament, Pembelajaran Kooperatif, Pendidikan Abad 21Abstract
Pendidikan abad 21 menuntut pendekatan pembelajaran yang tidak hanya berorientasi pada pengetahuan, tetapi juga keterampilan kolaborasi, komunikasi, dan berpikir kritis. Model pembelajaran konvensional masih mendominasi praktik di kelas, menyebabkan rendahnya keterlibatan siswa. Learning tournament hadir sebagai inovasi pembelajaran kooperatif berbasis kompetisi akademik yang menyenangkan dan bermakna. Penelitian ini bertujuan untuk menguji efektivitas model learning tournament terhadap hasil belajar dan keterlibatan siswa dalam konteks pendidikan abad 21. Penelitian ini menggunakan pendekatan kuantitatif dengan desain eksperimen semu (quasi experiment). Dua kelas VIII di salah satu SMP dijadikan sampel; satu sebagai kelompok eksperimen yang diberi model learning tournament, dan satu sebagai kontrol dengan pembelajaran konvensional. Data diperoleh melalui tes hasil belajar dan observasi keterlibatan siswa. Kelompok eksperimen menunjukkan peningkatan hasil belajar rata-rata sebesar 19,38 poin, jauh lebih tinggi dibandingkan kelompok kontrol yang hanya meningkat 10,53 poin. Hasil observasi juga menunjukkan tingkat keterlibatan yang lebih tinggi pada kelompok eksperimen dalam diskusi, tanggung jawab individu, dan kerja sama kelompok. Learning tournament terbukti efektif meningkatkan hasil belajar dan keterlibatan siswa dalam pembelajaran kooperatif. Penelitian ini menawarkan novelty dalam bentuk metode pembelajaran baru yang memadukan kompetisi dan kolaborasi secara seimbang, memberikan model praktis yang adaptif terhadap tantangan pendidikan masa kini.
References
Arnob, S. S., Shikder, M. A. A., Ovey, T. A., Rhythm, E. R., & Rasel, A. A. (2023). Analyzing Public Sentiment on Social Media during FIFA World Cup 2022 using Deep Learning and Explainable AI. 2023 26th International Conference on Computer and Information Technology (ICCIT), 1–6. https://doi.org/10.1109/ICCIT60459.2023.10441156
Arnold, E., Dianati, M., De Temple, R., & Fallah, S. (2022). Cooperative Perception for 3D Object Detection in Driving Scenarios Using Infrastructure Sensors. IEEE Transactions on Intelligent Transportation Systems, 23(3), 1852–1864. https://doi.org/10.1109/TITS.2020.3028424
Barioul, R., & Kanoun, O. (2023). K-Tournament Grasshopper Extreme Learner for FMG-Based Gesture Recognition. Sensors, 23(3), 1096. https://doi.org/10.3390/s23031096
Borowska, B. (2022). Learning Competitive Swarm Optimization. Entropy, 24(2), 283. https://doi.org/10.3390/e24020283
Bradley, K. J., & Aguinis, H. (2023). Team Performance: Nature and Antecedents of Nonnormal Distributions. Organization Science, 34(3), 1266–1286. https://doi.org/10.1287/orsc.2022.1619
Daniel, R., Murthy, T. S., Kumari, Ch. D. V. P., Lydia, E. L., Ishak, M. K., Hadjouni, M., & Mostafa, S. M. (2023). Ensemble Learning With Tournament Selected Glowworm Swarm Optimization Algorithm for Cyberbullying Detection on Social Media. IEEE Access, 11, 123392–123400. https://doi.org/10.1109/ACCESS.2023.3326948
Deng, C., Zhang, D., & Feng, G. (2022). Resilient practical cooperative output regulation for MASs with unknown switching exosystem dynamics under DoS attacks. Automatica, 139, 110172. https://doi.org/10.1016/j.automatica.2022.110172
Fan, Q., Bi, Y., Xue, B., & Zhang, M. (2024). Multi-Tree Genetic Programming for Learning Color and Multi-Scale Features in Image Classification. IEEE Transactions on Evolutionary Computation, 1–1. https://doi.org/10.1109/TEVC.2024.3384021
Febriani, Mundilarto, Pebriana, I. N., & Setiaji, B. (2023). Learning instruments development of teams games tournament learning model based on socio-scientific issues to grow student’s social attitudes of mutual cooperation and responsibility in physics distance learning. 020008. https://doi.org/10.1063/5.0110320
Hitar-García, J. A., Morán-Fernández, L., & Bolón-Canedo, V. (2023). Machine Learning Methods for Predicting League of Legends Game Outcome. IEEE Transactions on Games, 15(2), 171–181. https://doi.org/10.1109/TG.2022.3153086
Huffman, D., Raymond, C., & Shvets, J. (2022). Persistent Overconfidence and Biased Memory: Evidence from Managers. American Economic Review, 112(10), 3141–3175. https://doi.org/10.1257/aer.20190668
Imawan, O. R., Ismail, R., Tandililing, P., & Aisyah, F. N. (2023). Application of cooperative learning model type of teams games tournament using question card learning media on sine and cosine rules. 020012. https://doi.org/10.1063/5.0142258
Jha, A., Kar, A. K., & Gupta, A. (2023). Optimization of team selection in fantasy cricket: A hybrid approach using recursive feature elimination and genetic algorithm. Annals of Operations Research, 325(1), 289–317. https://doi.org/10.1007/s10479-022-04726-z
Jiang, L., Zheng, H., Tian, H., Xie, S., & Zhang, Y. (2022). Cooperative Federated Learning and Model Update Verification in Blockchain-Empowered Digital Twin Edge Networks. IEEE Internet of Things Journal, 9(13), 11154–11167. https://doi.org/10.1109/JIOT.2021.3126207
Koloszár, L., Wimmer, Á., Takácsné György, K., & Mitev, A. (2024). Tournament rituals and experiential competence development in higher education: A case of a unique conference series. The International Journal of Management Education, 22(1), 100929. https://doi.org/10.1016/j.ijme.2023.100929
Norris, C. M., Taylor, T. A., & Lummis, G. W. (2023). Fostering collaboration and creative thinking through extra-curricular challenges with primary and secondary students. Thinking Skills and Creativity, 48, 101296. https://doi.org/10.1016/j.tsc.2023.101296
Oroojlooy, A., & Hajinezhad, D. (2023). A review of cooperative multi-agent deep reinforcement learning. Applied Intelligence, 53(11), 13677–13722. https://doi.org/10.1007/s10489-022-04105-y
Wang, J., Hong, Y., Wang, J., Xu, J., Tang, Y., Han, Q.-L., & Kurths, J. (2022). Cooperative and Competitive Multi-Agent Systems: From Optimization to Games. IEEE/CAA Journal of Automatica Sinica, 9(5), 763–783. https://doi.org/10.1109/JAS.2022.105506
Wang, J.-J., & Wang, L. (2022). A Cooperative Memetic Algorithm With Learning-Based Agent for Energy-Aware Distributed Hybrid Flow-Shop Scheduling. IEEE Transactions on Evolutionary Computation, 26(3), 461–475. https://doi.org/10.1109/TEVC.2021.3106168
Wang, Y., Wu, Y., Tang, Y., Li, Q., & He, H. (2023). Cooperative energy management and eco-driving of plug-in hybrid electric vehicle via multi-agent reinforcement learning. Applied Energy, 332, 120563. https://doi.org/10.1016/j.apenergy.2022.120563
Yang, S., Barlow, M., Townsend, T., Liu, X., Samarasinghe, D., Lakshika, E., Moy, G., Lynar, T., & Turnbull, B. (2023). Reinforcement Learning Agents Playing Ticket to Ride–A Complex Imperfect Information Board Game With Delayed Rewards. IEEE Access, 11, 60737–60757. https://doi.org/10.1109/ACCESS.2023.3287100
Yang, Y., Modares, H., Vamvoudakis, K. G., & Lewis, F. L. (2024). Cooperative Finitely Excited Learning for Dynamical Games. IEEE Transactions on Cybernetics, 54(2), 797–810. https://doi.org/10.1109/TCYB.2023.3274908
Zaheer, M. Z., Mahmood, A., Khan, M. H., Segu, M., Yu, F., & Lee, S.-I. (2022). Generative Cooperative Learning for Unsupervised Video Anomaly Detection. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14724–14734. https://doi.org/10.1109/CVPR52688.2022.01433
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