Pengembangan Sistem Umpan Balik dalam Pembelajaran Berbasis LLM dengan Gamifikasi untuk Mendukung Engagement Siswa
DOI:
https://doi.org/10.47065/bulletincsr.v6i6.1391Keywords:
Large Language Model; Feedback; Gamification; Learning Engagement; Algorithm and Programming; ADDIEAbstract
Algorithm and Programming learning requires feedback that helps students identify errors and improve their solutions during problem-solving activities. However, teachers’ limited capacity to provide individual feedback may cause students to wait for guidance when encountering difficulties. This study aims to develop a Large Language Model (LLM)-based feedback system with gamification to support student engagement. The study employed a Research and Development (R&D) method using the ADDIE model. The system was implemented with 31 students from Class X DKV 1 at SMK Negeri 2 Bandung using a One-Group Pretest-Posttest design. Data were collected through observations, interviews, expert validation, tests, and a 26-item engagement questionnaire based on the Learning Engagement instrument, using a four-point Likert scale. The instrument covers six dimensions: psychological motivation, peer cooperation, cognitive problem-solving, interaction with instructors, learning environment support, and learning management. Functional testing showed that all 23 test scenarios were successfully executed. Media validation reached 96.25%, while material validation increased from 93.24% to 97.04%. Computational Thinking skills showed an average N-Gain of 0.59, categorized as moderate. Meanwhile, student engagement achieved an average score of 3.42 out of 4, categorized as very high. This study contributes an integrated learning platform that combines rule-based flowchart checking, LLM-generated corrective explanations based on checking results, and gamification elements. However, engagement was measured through student self-reports, and the absence of a comparison group limits the interpretation of the findings.
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M. E. Alonso-Mencía, C. Alario-Hoyos, I. Estévez-Ayres, and C. D. Kloos, “Analysing Self-Regulated Learning Strategies of MOOC Learners Through Self-Reported Data,” Australasian Journal of Educational Technology, vol. 37, no. 3, pp. 56–70, 2021, doi: 10.14742/ajet.6150.
I. Molenaar, “Towards Hybrid Human-AI Learning Technologies,” Eur. J. Educ., vol. 57, no. 4, pp. 632–645, 2022, doi: 10.1111/ejed.12527.
M. Liao, K. Zhu, and G. Wang, “Can Human-Machine Feedback in a Smart Learning Environment Enhance Learners’ Learning Performance? A Meta-Analysis,” Front. Psychol., vol. 14, p. 1288503, 2024, doi: 10.3389/fpsyg.2023.1288503.
X. Deng and Z. Yu, “A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education,” Sustainability, vol. 15, no. 4, p. 2940, 2023, doi: 10.3390/su15042940.
E. Kasneci et al., “ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education,” Learn. Individ. Differ., vol. 103, p. 102274, 2023, doi: 10.1016/j.lindif.2023.102274.
S. Steinert, K. E. Avila, S. Ruzika, J. Kuhn, and S. Küchemann, “Harnessing Large Language Models to Develop Research-Based Learning Assistants for Formative Feedback,” Smart Learning Environments, vol. 11, no. 1, p. 62, 2024, doi: 10.1186/s40561-024-00354-1.
D. Sun, P. Xu, J. Zhang, R. Liu, and J. Zhang, “How Self-Regulated Learning Is Affected by Feedback Based on Large Language Models: Data-Driven Sustainable Development in Computer Programming Learning,” Electronics (Basel)., p., 2025, doi: 10.3390/electronics14010194.
F. Ouyang and P. Jiao, “Artificial Intelligence in Education: The Three Paradigms,” Computers and Education: Artificial Intelligence, vol. 2, p. 100020, 2021, doi: 10.1016/j.caeai.2021.100020.
D. H. Chang, M. P.-C. Lin, S. Hajian, and Q. Q. Wang, “Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and Personalization,” Sustainability, vol. 15, no. 17, p. 12921, 2023, doi: 10.3390/su151712921.
C. J. Hellín, F. Calles-Esteban, A. Valledor, J. Gómez, S. Otón-Tortosa, and A. Tayebi, “Enhancing Student Motivation and Engagement Through a Gamified Learning Environment,” Sustainability, vol. 15, no. 19, p. 14119, 2023, doi: 10.3390/su151914119.
W. Ge et al., “SRLAgent: Enhancing Self-Regulated Learning Skills Through Gamification and LLM Assistance,” ArXiv, vol. abs/2506.09968, p., 2025, doi: 10.48550/arXiv.2506.09968.
H. Ying and W. A. J. Wan Yahaya, “AI-Mediated Feedback in Gamified Programming Education: Effects on Vocational Students’ Achievement and Motivation,” in Frontiers in Education, Frontiers Media SA, 2026, p. 1846699. doi: 10.3389/feduc.2026.1846699.
M. Afzaal, A. Zia, J. Nouri, and U. Fors, “Informative Feedback and Explainable AI-Based Recommendations to Support Students’ Self-Regulation,” Technology, Knowledge and Learning, vol. 29, no. 1, pp. 331–354, 2024, doi: 10.1007/s10758-023-09650-0.
M. Nurtanto, M. Fawaid, and H. Sofyan, “Problem-Based Learning (PBL) in Industry 4.0: Improving Learning Quality Through Character-Based Literacy Learning and Life Career Skill (LL-LCS),” in Journal of Physics: Conference Series, IOP Publishing, 2020, p. 012006. doi: 10.1088/1742-6596/1573/1/012006.
J. Lee, H.-D. Song, and A. J. Hong, “Exploring Factors and Indicators for Measuring Students’ Sustainable Engagement in E-Learning,” Sustainability, vol. 11, no. 4, p. 985, 2019, doi: 10.3390/su11040985.
R. M. Branch and ?. Varank, Instructional Design: The ADDIE Approach, vol. 722. Springer, 2009. doi: 10.1007/978-0-387-09506-6.
S. Nita and S. Kartikawati, “Analysis of the Impact Narrative Algorithm Method, Pseudocode, and Flowchart Towards Students’ Understanding of the Programming Algorithm Courses,” in IOP Conference Series: Materials Science and Engineering, IOP Publishing, 2020, p. 012044. doi: 10.1088/1757-899X/835/1/012044.
N. Nisrina, I. Rahmawati, and F. N. Hikmah, “Pengembangan Instrumen Validasi Produk Multimedia Pembelajaran Fisika,” Lensa: Jurnal Kependidikan Fisika, vol. 10, no. 1, pp. 32–38, 2022, doi: 10.33394/j-lkf.v10i1.5278.
S. Arikunto, Dasar-Dasar Evaluasi Pendidikan Edisi 3. Bumi aksara, 2021.
R. R. Hake, “Interactive-Engagement Versus Traditional Methods: A Six-Thousand-Student Survey of Mechanics Test Data for Introductory Physics Courses,” Am. J. Phys., vol. 66, no. 1, pp. 64–74, 1998, doi: 10.1119/1.18809.
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