Motivasi Belajar Fisika melalui Model Physics-App Builder Berbasis Generative AI (AWS PartyRock): Studi Pendahuluan dengan Kerangka ARCS: Studi dengan Kerangka ARCS

Albertus Tuhu Setyo Nugroho

doi:10.22487/jpft.v14i2.6669

Authors

Albertus Tuhu Setyo Nugroho Universitas Terbuka

DOI:

https://doi.org/10.22487/jpft.v14i2.6669

Keywords:

AWS PartyRock, Generative AI, Motivasi Belajar, Model ARCS, project-based learning

Abstract

Pemanfaatan generative artificial intelligence (AI) dalam pembelajaran fisika berpotensi meningkatkan motivasi belajar, tetapi efektivitasnya bergantung pada bagaimana AI diposisikan dalam proses penalaran murid. Penelitian ini bertujuan menganalisis perubahan motivasi belajar fisika setelah penerapan model Physics-App Builder, yaitu pengembangan Project-Based Learning (PjBL) yang menerapkan pendekatan physics-first dengan menempatkan AI setelah murid membangun konsep fisika. Penelitian menggunakan pendekatan praeksperimen dengan rancangan one-group pretest-posttest yang melibatkan seluruh delapan murid kelas XI IPA SMA Dominikus Wonosari. Motivasi belajar diukur menggunakan angket ARCS yang telah divalidasi, sedangkan keterlaksanaan pembelajaran diamati melalui lembar observasi. Hasil penelitian menunjukkan keterlaksanaan pembelajaran sebesar 94,44% (kategori sangat tinggi) dan peningkatan motivasi belajar dari 77,73% menjadi 85,94% dengan N-gain 0,368 (kategori sedang). Peningkatan tertinggi terjadi pada dimensi Confidence (N-gain = 0,625), sedangkan dimensi Kreasi AI mencapai 87,50%. Temuan ini mengindikasikan bahwa penempatan AI sebagai sarana membangun dan memverifikasi representasi konsep fisika, bukan sebagai penyedia jawaban, berpotensi meningkatkan motivasi belajar sekaligus mendorong kreativitas murid.

References

Alé, J., & Arancibia, M. L. (2025). Emerging technology-based motivational strategies: A systematic review with meta-analysis. Education Sciences, 15(2), 197. https://doi.org/10.3390/educsci15020197

Alfito, A., Haeruddin, H., Ilmianih, R., Wahyono, U., & Syamsuriwal, S. (2025). Analisis persepsi peserta didik dalam konteks energi terbarukan memanfaatkan AI dan simulasi dalam pembelajaran fisika. JPFT (Jurnal Pendidikan Fisika Tadulako Online), 13(3), 325–332. https://doi.org/10.22487/jpft.v13i3.4951

Al-Kamzari, F., & Alias, N. (2025). A systematic literature review of project-based learning in secondary school physics: Theoretical foundations, design principles, and implementation strategies. Humanities and Social Sciences Communications, 12, 286. https://doi.org/10.1057/s41599-025-04579-4

Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives (Complete ed.). Longman.

Arikunto, S. (2021). Prosedur penelitian: Suatu pendekatan praktik. Rineka Cipta.

Aşıksoy, G., & Özdamlı, F. (2016). Flipped classroom adapted to the ARCS model of motivation and applied to a physics course. EURASIA Journal of Mathematics, Science and Technology Education, 12(6), 1589–1603. https://doi.org/10.12973/eurasia.2016.1251a

Chiu, T. K. F. (2024). A classification tool to foster self-regulated learning with generative artificial intelligence by applying self-determination theory: A case of ChatGPT. Educational Technology Research and Development, 72(4), 2401–2416. https://doi.org/10.1007/s11423-024-10366-w

Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. https://doi.org/10.1016/j.compedu.2024.105224

Fang, X., Ng, D. T. K., Leung, J. K. L., & Xu, H. (2024). The applications of the ARCS model in instructional design, theoretical framework, and measurement tool: A systematic review of empirical studies. Interactive Learning Environments, 32(10), 5919–5946. https://doi.org/10.1080/10494820.2023.2240867

Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006

Guo, K., Zhong, Y., Li, D., & Chu, S. K. W. (2023). Effects of chatbot-assisted in-class debates on students’ argumentation skills and task motivation. Computers & Education, 203, 104862. https://doi.org/10.1016/j.compedu.2023.104862

Hake, R. R. (1998). Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. American Journal of Physics, 66(1), 64–74. https://doi.org/10.1119/1.18809

Hmoud, M., Swaity, H., Hamad, N., Karram, O., & Daher, W. (2024). Higher education students’ task motivation in the generative artificial intelligence context: The case of ChatGPT. Information, 15(1), 33. https://doi.org/10.3390/info15010033

Keller, J. M. (2010). Motivational design for learning and performance: The ARCS model approach. Springer. https://doi.org/10.1007/978-1-4419-1250-3

Li, K., & Keller, J. M. (2018). Use of the ARCS model in education: A literature review. Computers & Education, 122, 54–62. https://doi.org/10.1016/j.compedu.2018.03.019

Liu, X., Guo, B., He, W., & Hu, X. (2025). Effects of generative artificial intelligence on K-12 and higher education students’ learning outcomes: A meta-analysis. Journal of Educational Computing Research. Advance online publication. https://doi.org/10.1177/07356331251329185

Natalia, V., Kade, A., & Laratu, W. N. (2023). Analisis motivasi belajar murid di SMAN 1 Sindue pada pembelajaran fisika. JPFT (Jurnal Pendidikan Fisika Tadulako Online), 11(3), 181–185. https://doi.org/10.22487/jpft.v11i3.3748

Pekrun, R. (2024). Control-value theory: From achievement emotion to a general theory of human emotions. Educational Psychology Review, 36(3), 83. https://doi.org/10.1007/s10648-024-09909-7

Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002.

Setyowati, D., Qadar, R., & Efwinda, S. (2022). Analisis motivasi siswa berdasarkan model ARCS (Attention, Relevance, Confidence, and Satisfaction) dalam pembelajaran fisika berbasis e-learning di SMA se-Samarinda. Jurnal Literasi Pendidikan Fisika (JLPF), 3(2), 116–129. https://doi.org/10.30872/jlpf.v3i2.1044

Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31(2), 261–292. https://doi.org/10.1007/s10648-019-09465-5

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.

Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21, 15. https://doi.org/10.1186/s41239-024-00448-3

Wang, C., Song, L., & Jiang, J. (2025). The impact of project-based learning on university physics education: Enhancing cognitive skills and core competencies. Frontiers in Psychology, 16, 1495105. https://doi.org/10.3389/fpsyg.2025.1495105

Wu, C.-H., & Liou, G.-M. (2024). ARCS model for exploring the enhancement of learning motivation and engagement through AIGC technology in computer graphics courses. In Proceedings of the 2024 8th International Conference on Education and Multimedia Technology (pp. 1–11). Association for Computing Machinery. https://doi.org/10.1145/3678726.3678745

Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, 100147. https://doi.org/10.1016/j.caeai.2023.100147