Contextual interactive animated video media development to promote environmentally relevant chemical equilibrium learning and improve students’ motivation
DOI:
https://doi.org/10.62672/joease.v4i2.234Keywords:
Chemical equilibrium, Environmental context, Interactive animated video, Learning motivation, learning outcomesAbstract
The study was conducted to generate and validate an animated video that will impact students' understanding of chemical equilibrium and its context. They hoped not only that students would grasp the concepts but also that they would show some interest in the material. The researchers designed the research and evaluated the video using the ADDIE model. They selected thirty-four students from the grade and then followed this up with some questions to answer before and after watching the video. The video is not just valid; it is quite good at explaining chemical equilibrium to students. They scored the video 4.57, which means it is valid and has good content. The video was practical when the students viewed it; sixty-two-point nine seven percent of students reported the video as easy to use, and ninety-point eight eight percent considered it very helpful. The video works well because students are interacting with pictures of the environment for information about chemical equilibrium. This encourages students to relate what they are learning to the real world. By researching chemistry concepts, the findings of this research further inform more general strategies to inform students about principles to follow in other areas. Chemical equilibrium is one of the subjects it teaches, but this video uses environmental examples for easy understanding. Students are educated on chemical equilibrium and how important it is, tied to the environment around them.
References
Ankareddy, S., Dorfleitner, G., Zhang, L., & Ok, Y. S. (2025). Embedding sustainability in higher education institutions: A review of practices and challenges. Cleaner Environmental Systems, 17, 100279. https://doi.org/10.1016/j.cesys.2025.100279
Ba, S., Shi, X., Wu, S., & Lu, G. (2026). Artificial intelligence agents in computer-supported collaborative learning: A systematic literature review. Computers and Education: Artificial Intelligence, 10, 100579. https://doi.org/10.1016/j.caeai.2026.100579
Bataeineh, M., & Aga, O. (2022). Integrating sustainability into higher education curricula: Saudi Vision 2030. Emerald Open Research, 4, 19. https://doi.org/10.35241/emeraldopenres.14499.1
Brkić, L., Mekterović, I., Fertalj, M., & Mekterović, D. (2024). Peer assessment methodology of open-ended assignments. Computers & Education, 213, 105001. https://doi.org/10.1016/j.compedu.2024.105001
Chen, C., Jamiat, N., & Mao, Y. (2023). Effects of gamified interactive e-books on students’ learning achievements and motivation. Frontiers in Psychology, 14, 1236297. https://doi.org/10.3389/fpsyg.2023.1236297
Deaningtyas, S. A., Purwandari, A., Sentanu, N. A. Z., Rafsanjani, E. R., Kamaliyah, N. L., & Setiawan, N. C. E. (2024). Development of CHEMISTER as chemistry education media with socioscientific issues integrated with augmented reality. Jurnal Pembelajaran Kimia, 9(2), 106–115. https://doi.org/10.17977/um026v9i22024p106-115
Demir, S. (2022). Comparison of normality tests under different skewness and kurtosis conditions. International Journal of Assessment Tools in Education, 9(2), 397–409. https://doi.org/10.21449/ijate.1101295
Errabo, D. D., & Ongoco, A. A. (2024). Effects of interactive mobile learning modules on students’ engagement and understanding. Journal of Research in Innovative Teaching & Learning, 17(2), 327–351. https://doi.org/10.1108/JRIT-01-2024-0023
Estrada, L. S. M., Haase, M., Baumann, M., & Cinelli, M. (2026). Decision support for energy system transformation. Energy Strategy Reviews, 63, 102016. https://doi.org/10.1016/j.esr.2025.102016
Fan, M.-R., Tran, N.-H., Nguyen, L.-H.-P., & Huang, C.-F. (2024). Effects of outdoor education on students’ learning motivation. European Journal of Educational Research, 13(3), 1353–1363. https://doi.org/10.12973/eu-jer.13.3.1353
Fernández, A. A., López-Torres, M., Fernández, J. J., & Vázquez-García, D. (2023). Student-generated videos to promote understanding of chemical reactions. Journal of Chemical Education, 100(3), 1039–1046. https://doi.org/10.1021/acs.jchemed.2c00813
García-Hernández, A., García-Valcárcel, A., Casillas-Martín, S., & Cabezas-González, M. (2022). Sustainability in digital education: A systematic review. Education Sciences, 13(1), 33. https://doi.org/10.3390/educsci13010033
García-Hernández, A., García-Valcárcel, A., Casillas-Martín, S., & Cabezas-González, M. (2025). Mathematical creativity: A systematic review. Education Sciences, 15(10), 1348. https://doi.org/10.3390/educsci15101348
Granström, M., & Oppi, P. (2025). Student engagement with AI tools in learning. Frontiers in Education, 10, 1688092. https://doi.org/10.3389/feduc.2025.1688092
Gorito, G., & Morais, C. (2025). Environmental awareness through chemistry education. Education Sciences, 16(1), 38. https://doi.org/10.3390/educsci16010038
Hake, R. R. (1998). Interactive engagement versus traditional methods. American Journal of Physics, 66(1), 64–74. https://doi.org/10.1119/1.18809
Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education. Sustainable Operations and Computers, 3, 275–285. https://doi.org/10.1016/j.susoc.2022.05.004
Hastuti, D. (2021). 21st century skills in primary school learning. SHES Conference Series, 4(5), 111–119. https://doi.org/10.20961/shes.v4i5.66138
Hasanah, D., Wiji, Mulyani, S., & Widhiyanti, T. (2024). Multiple representations in chemistry learning. KnE Social Sciences, 248–257. https://doi.org/10.18502/kss.v9i8.15554
Herunata, H., & Puteri, E. A. A. (2024). Representation learning cycle model in chemical equilibrium. Jurnal Pembelajaran Kimia, 9(2), 82–96. https://doi.org/10.17977/um026v9i22024p82-96
Hidayah, R., Iswahyuni, N., & Mitasari, R. (2021). Computer-based games as learning media. Jurnal Pembelajaran Kimia, 6(2), 100–110. https://doi.org/10.17977/um026v6i22021p100
Jordan, S., Wang, G., Nguyen, A. T. H., et al. (2026). Co-teaching model in chemistry education. Journal of Chemical Education, 103(3), 1411–1420. https://doi.org/10.1021/acs.jchemed.5c01436
Keller, J. M. (1987). Development and use of the ARCS model of instructional design. Journal of Instructional Development, 10, 2–10. https://doi.org/10.1007/BF02905780
Kitsantas, A., et al. (2025). Self-regulated learning theory. Educational Psychology Review. https://doi.org/10.1007/s10648-025-10052-0
Langitasari, I., et al. (2024). Enhancing students’ conceptual understanding in chemistry. KnE Social Sciences, 191–200. https://doi.org/10.18502/kss.v9i13.15919
Lutfi, A., Hidayah, R., Aftinia, F., & Ipmawati, N. (2023). Chemistry learning media development. Educación Química, 34, 176–187. https://doi.org/10.22201/fq.18708404e.2023.1.82798
Mediana, N. L., Funa, A. A., & Dio, R. V. (2025). Inquiry-based learning effectiveness. International Journal of Education in Mathematics, Science and Technology, 13(2), 532–552. https://doi.org/10.46328/ijemst.4769
Nieveen, N. (1999). Prototype to reach product quality. In J. van den Akker et al. (Eds.), Design approaches and tools in education. Kluwer Academic. https://doi.org/10.1007/978-94-011-4255-7_10
Palacios-Rodríguez, A., Llorente-Cejudo, C., & Cabero-Almenara, J. (2023). Educational digital transformation. Frontiers in Education, 8, 1267939. https://doi.org/10.3389/feduc.2023.1267939
Parmini, N. P., et al. (2023). 21st century skills and information literacy. Jurnal MI, 28(1), 11–20. https://doi.org/10.23887/mi.v28i1.59441
Rahmawati, Y., et al. (2022). Students’ conceptual understanding using PhET simulations. Journal of Technology and Science Education, 12(2), 303–326. https://doi.org/10.3926/jotse.1597
Sánchez-García, E., et al. (2024). Environmental management and sustainability. Journal of Environmental Management. https://doi.org/10.1016/j.jenvman.2024.123739
Sipahi, S., & Bahar, M. (2025). Visualization-based learning in science education. Education Sciences. https://doi.org/10.3390/educsci15010001
Sunday, E. S., et al. (2026). Green chemistry education and environmental awareness. Discover Education, 5, 44. https://doi.org/10.1007/s44217-025-01056-7
Suparman, R. A., Rohaeti, E., & Wening, S. (2024). Student misconception in chemistry. Pegem Journal of Education and Instruction, 14(2), 238–252. https://doi.org/10.47750/pegegog.14.02.28
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
Trevisan, L. V., Leal Filho, W., & Pedrozo, E. Á. (2024). Transformative learning for sustainability. Journal of Cleaner Production, 447, 141634. https://doi.org/10.1016/j.jclepro.2024.141634
Yuensook, T., Jantakoon, T., & Limpinan, P. (2026). AI-driven adaptive learning systems. Journal of Education and Learning, 15(2). https://doi.org/10.5539/jel.v15n2p117
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Journal of Environment and Sustainability Education

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.




Malaysia
Brazil
Australia
Taiwan, Province of China
India
Philippines
Japan
Morocco
Nigeria
Ghana
Italy
United Arab Emirates
Colombia
Kenya
Kazakhstan
New Zealand
Pakistan
Turkey
Azerbaijan
Burundi
Switzerland
China
Germany
Estonia
Egypt
United Kingdom
Mexico
Netherlands
Russian Federation
Thailand
Viet Nam
South Africa
