Exploratory Study of Student Needs in the Development of Technology-Based Chemical Computing Software
DOI:
https://doi.org/10.21831/jser.v10i1.90331Abstract
The development of digital technology in education demands innovations in chemistry learning, especially in explaining abstract concepts such as molecular structure, chemical reactions, and properties of compounds. This research aims to explore the needs of chemistry education students towards the development of technology-based chemical computing software. The research method uses a quantitative approach with an exploratory descriptive design. The data was obtained through a closed questionnaire on the Likert scale, which was distributed to 117 students of the Chemistry Education study program from three universities in Indonesia. Data analysis was carried out using descriptive statistics in the form of frequency, average, and standard deviation. The results showed that students had a high need for interactive 3D molecular visualization features (M = 4.09), integration of materials with concept animation (M = 4.09), and interactive exercises (M = 4.06). In addition, simple reaction simulation features and auto-scoring were also considered important (M = 4.03). Meanwhile, the aspect of cross-device access (M = 3.76) and the availability of Indonesian content according to the national curriculum (M = 3.55) received a lower score, although it remained in the high category. The conclusion of this study emphasizes the importance of developing chemical computing software that is not only visual, but also educational, adaptive, and according to the needs of students. The implications of the research results provide direction for educational technology developers, curriculum designers, and higher education institutions in designing chemistry learning media that are more contextual, inclusive, and sustainable.
Downloads
References
Abdo, S. N., Hsu, J. L., Kapetanakis, C., Newman, D. L., Wright, L. K., & Bailey, J. (2024). An Exploration of Spatial Visualization Skills: Investigating Students’ Use of 3D Models in Science Problems during Think-Aloud Interviews. Journal of Chemical Education, 101(9), 3624–3634. https://doi.org/10.1021/acs.jchemed.3c01355
Agustian, H. Y., Teinholt, L., Tarp, J., Maja, J., Pedersen, I., Voetmann, F., Bente, C., & Nielsen, J. A. (2022). Learning outcomes of university chemistry teaching in laboratories : A systematic review of empirical literature. Review of Education, June, 1–41. https://doi.org/10.1002/rev3.3360
Alharbi, A. A. (2025). Journal of Radiation Research and Applied Sciences Cognitive learning approach to enhance university students ’ visualization of molecular geometry in chemical compounds : A case study in Saudi Arabia. Journal of Radiation Research and Applied Sciences, 18(1), 101283. https://doi.org/10.1016/j.jrras.2024.101283
Allouche, A. (2012). Software News and Updates Gabedit — A Graphical User Interface for Computational Chemistry Software. Journal of Computational Chemistry, 32, 174–182. https://doi.org/10.1002/jcc
Ariyatun, Sudarmin, Rahmawati, A., Winarto, & Wibowo, T. (2025). Reconstruction of the Engineering Design Project ( EDPj ) Learning Model based on Ethno-ESD to Actualize Students ’ Sustainable Environmental Literacy. LUMAT: International Journal on Math, Science, and Technology, 12(4), 1–28. https://doi.org/https://doi.org/10.31129/LUMAT.12.4.2300
Arkader, A., Schardong, G., Schirmer, L., Perazzo, D., Novello, T., & Velho, L. (2025). Computers & Graphics Democratizing Interactivity : An Overview of Interfaces for Multimedia Machine Learning. Computers & Graphics, 15(2). http://www.elsevier.com/locate/cag
Arsyad, M., Mujahiddin, & Syakhrani, A. W. (2016). the Efficiency of Using Visual Learning Media in Improving the Understanding of Science Concepts in Elementary School Students. Indonesian Journal of Education, 4(3), 1–23.
Bhattacharjee, J. (2015). Constructivist Approach to Learning – An Effective Approach of Teaching Learning. International Research Journal of Interdisciplinary & Multidisciplinary Studies, 7969(65), 65–74. http://www.irjims.com/
Bílgín, A. K., Yürükel, F. N. D., & Yígít, N. (2017). The effect of a developed REACT strategy on the conceptual understanding of students: “Particulate nature of matter.” Journal of Turkish Science Education, 14(2), 65–81. https://doi.org/10.12973/tused.10199a
Brown, C. E., Alrmuny, D., Williams, M. K., Whaley, B., & Hyslop, R. M. (2021). Visualizing molecular structures and shapes : a comparison of virtual reality , computer simulation , and traditional modeling. Chemistry Teacher International, 3(1), 69–80. https://doi.org/10.1515/cti-2019-0009
Byusa, E., Kampire, E., & Mwesigye, A. R. (2022). Game-based learning approach on students’ motivation and understanding of chemistry concepts: A systematic review of literature. Heliyon, 8(5), e09541. https://doi.org/10.1016/j.heliyon.2022.e09541
Chakravartty, A. (2023). Scientific Knowledge vs. Knowledge of Science. Science & Education, 32(6), 1795–1812. https://doi.org/10.1007/s11191-022-00376-6
Chaojing, M. (2023). A Study on Strategies for Cultivating Higher-Order Thinking Skills in Primary and Secondary School Students. Frontiers in Educational Research, 6(20), 67–71. https://doi.org/10.25236/FER.2023.062011
Cheng, G. (2017). The impact of online automated feedback on students’ reflective journal writing in an EFL course. Internet and Higher Education, 34, 18–27. https://doi.org/10.1016/j.iheduc.2017.04.002
Cheng, M. M. W., & Gilbert, J. K. (2017). Modelling students’ visualisation of chemical reaction. International Journal of Science Education, 39(9), 1173–1193. https://doi.org/10.1080/09500693.2017.1319989
Demir, N., & Kayaoğlu, M. N. (2022). Multi-dimensional foreign language education : the case of an eTwinning project in Turkey. Computer Assisted Language Learning, 35(9), 2201–2238. https://doi.org/10.1080/09588221.2020.1871027
Du, D., Baird, T. J., Bonella, S., & Pizzi, G. (2023). OSSCAR, an open platform for collaborative development of computational tools for education in science. Computer Physics Communications, 282. https://doi.org/10.1016/j.cpc.2022.108546
Elmqaddem, N. (2019). Augmented Reality and Virtual Reality in Education . Myth or Reality ? International Journal Emergenci Technology Learning, 14(3), 234–242. https://doi.org/10.3991/ijet.v14i03.9289
Gather, S. M. C., Zysman-colman, E., & Lee, O. S. (2024). Digichem : computational chemistry for everyone. Digital Discovery, 3(16). https://doi.org/10.1039/d4dd00147h
Erümit, A. K., & Sarıalioğlu, R. Ö. (2025). Artificial intelligence in science and chemistry education: a systematic review. Discover Education, 4(1). https://doi.org/10.1007/s44217-025-00622-3
Fombona-Pascual, A., Fombona, J., & Vicente, R. (2022). Augmented Reality, a Review of a Way to Represent and Manipulate 3D Chemical Structures. Journal of Chemical Information and Modeling, 62(8), 1863–1872. https://doi.org/10.1021/acs.jcim.1c01255
Gholam, A. (2019). Inquiry-Based Learning: Student Teachers’ Challenges and Perceptions. Journal of Inquiry & Action in Education, 10(2), 112–133.
Gombert, S., Fink, A., Giorgashvili, T., Jivet, I., Di Mitri, D., Yau, J., Frey, A., & Drachsler, H. (2024). From the Automated Assessment of Student Essay Content to Highly Informative Feedback: a Case Study. International Journal of Artificial Intelligence in Education, 34(4), 1378–1416. https://doi.org/10.1007/s40593-023-00387-6
Ho, L. H., Sun, H., & Tsai, T. H. (2019). Research on 3D painting in virtual reality to improve students’ motivation of 3D animation learning. Sustainability (Switzerland), 11(6), 1–17. https://doi.org/10.3390/su11061605
Hoai, V. T. T., Son, P. N., Em, V. V. D., & Duc, N. M. (2023). Using 3D molecular structure simulation to develop chemistry competence for Vietnamese students. Eurasia Journal of Mathematics, Science and Technology Education, 19(7). https://doi.org/10.29333/ejmste/13345
Honke, N., & Becker-Genschow, S. (2025). Adaptive learning in bionics: transforming science education. Frontiers in Education, 10(February), 1–21. https://doi.org/10.3389/feduc.2025.1427083
Indolia, S., Goswami, A. K., Mishra, S. P., & Asopa, P. (2018). Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach. Procedia Computer Science, 132, 679–688. https://doi.org/10.1016/j.procs.2018.05.069
Ipinnaiye, O., & Risquez, A. (2024). Exploring adaptive learning, learner-content interaction and student performance in undergraduate economics classes. Computers and Education, 215(March), 105047. https://doi.org/10.1016/j.compedu.2024.105047
Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2(December 2020), 100017. https://doi.org/10.1016/j.caeai.2021.100017
Kiernan, N. A., Manches, A., & Seery, M. K. (2021). Research and Practice The role of visuospatial thinking in students ’ predictions of molecular geometry. Chemistry Education Research and Practice, 22(13), 626–639. https://doi.org/10.1039/d0rp00354a
Kiernan, N. A., & Seery, M. K. (2024). Research and Practice Resources for reasoning of chemistry concepts : multimodal molecular geometry. Chemistry Education Research and Practice, 25(24), 524–543. https://doi.org/10.1039/d3rp00186e
Kozlíková, B., Krone, M., Falk, M., Lindow, N., Baaden, M., Baum, D., Viola, I., Parulek, J., & Hege, H. C. (2017). Visualization of Biomolecular Structures: State of the Art Revisited. Computer Graphics Forum, 36(8), 178–204. https://doi.org/10.1111/cgf.13072
Kuit, V. K., & Osman, K. (2021). Eficacia del módulo electrónico CHEMBOND3D para mejorar el conocimiento de los estudiantes sobre el concepto de enlace químico y las habilidades visoespaciales. European Journal of Science and Mathematics Education, 9(4), 252–264.
Lehtola, S., & Karttunen, A. J. (2022). Free and open source software for computational chemistry education. Wiley Interdisciplinary Reviews: Computational Molecular Science, 12(5), 1–33. https://doi.org/10.1002/wcms.1610
Lilian, A. V. (2025). Challenges of Integration of Information and Communication Technology (ICT) in Chemistry Teaching in Secondary Schools in Bayelsa State. FUO-Journal of Educational Research, 4(2), 219–227. https://doi.org/10.5281/zenodo.14885888
Maulana, I., Supriyadi, E., Wijanarka, B. S., Ariyatun, & Winarto. (2025). Coding learning innovation: Interactive programming experience with virtual lab platform. Multidisciplinary Science Journal, 7(6). https://doi.org/10.31893/multiscience.2025262
Mayer, R. E. (2024). The Past, Present, and Future of the Cognitive Theory of Multimedia Learning. Educational Psychology Review, 36(1), 1–25. https://doi.org/10.1007/s10648-023-09842-1
Mukhlis, Ariyatun, Septiana, I., Wijayanto, & Arifianti, I. (2026). Integrating Ethnopedagogy and Virtual Reality in Research-Based Learning for Regenerative Education.pdf. Journal of Cultural Analysis and Social Change, 11(1), 873–881. https://doi.org/https://doi.org/10.64753/jcasc.v11i1.3988
Muthmainnah, Ibna Seraj, P. M., & Oteir, I. (2022). Playing with AI to Investigate Human-Computer Interaction Technology and Improving Critical Thinking Skills to Pursue 21stCentury Age. Education Research International, 2022. https://doi.org/10.1155/2022/6468995
Panda, D. R. (2024). Human Computer Interaction Strategies for Effective Digital Learning Experiences: From Classroom to Screen. Interantional Journal of Scientific Research in Engineering and Management, 08(008), 1–5. https://doi.org/10.55041/ijsrem37370
Raes, A., Schellens, T., Wever, B. De, & Vanderhoven, E. (2012). Computers & Education Scaffolding information problem solving in web-based collaborative inquiry learning. Computers & Education Journal, 59, 82–94. https://doi.org/10.1016/j.compedu.2011.11.010
Retnawati, H., Djidu, H., Kartianom, Apino, E., & Anazifa, R. D. (2018). Teachers’ knowledge about higher-order thinking skills and its learning strategy. Problems of Education in the 21st Century, 76(2), 215–230. https://doi.org/10.33225/pec/18.76.215
Rincon-Flores, E. G., Castano, L., Guerrero Solis, S. L., Olmos Lopez, O., Rodríguez Hernández, C. F., Castillo Lara, L. A., & Aldape Valdés, L. P. (2024). Improving the learning-teaching process through adaptive learning strategy. Smart Learning Environments, 11(1). https://doi.org/10.1186/s40561-024-00314-9
Schulz, H., & FitzPatrick, B. (2016). Teachers’ understandings of critical and higher order thinking and what this means for their teaching and assessments. Alberta Journal of Educational Research, 62(1), 61–86. https://doi.org/10.55016/ojs/ajer.v62i1.56168
Shengqiang, L., Nankhantee, A., & Srikhao, S. (2025). Combining Inquiry-Based Learning and Collaborative Learning : A New Model for Improving Students ’ Teamwork and Problem-Solving Skills. Journal of Education and Educational Development, 12(1), 13–38. https://doi.org/10.22555/joeed.v12i1.1296
Serhan, M., Sprowls, M., Jackemeyer, D., Long, M., Perez, I. D., Maret, W., Tao, N., & Forzani, E. (2019). Total iron measurement in human serum with a smartphone. AIChE Annual Meeting, Conference Proceedings, 2019-Novem. https://doi.org/10.1039/x0xx00000x
Supiyanti, Iriyadi, F. (2022). Journal of Technology and Science Education. Journal of Technology and Science Education, 4(4), 215–227. http://www.jotse.org/index.php/jotse/article/view/110/142
Üce, M., & Ceyhan, İ. (2019). Misconception in Chemistry Education and Practices to Eliminate Them: Literature Analysis. Journal of Education and Training Studies.
Vosniadou, S. (2019). The Development of Students’ Understanding of Science. Frontiers in Education, 4(April), 1–6. https://doi.org/10.3389/feduc.2019.00032
Wainman, B., Wolak, L., Pukas, G., Zheng, E., & Norman, G. R. (2018). The superiority of three-dimensional physical models to two-dimensional computer presentations in anatomy learning. Medical Education, 52(11), 1138–1146. https://doi.org/10.1111/medu.13683
Watson, C. A. (2022). CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics. Anal Chem, 94(50), 17456–17466. https://doi.org/10.1021/acs.analchem.2c03491.CCS
Whatoni, A. S., & Sutrisno, H. (2022). Development of A Learning Module Supported by Augmented Reality on Chemical Bonding Material to Improve Interest and Motivation of Students Learning for Senior High School. Jurnal Penelitian Pendidikan IPA, 8(4), 2210–2218. https://doi.org/10.29303/jppipa.v8i4.2057
Wu, H., Krajcik, J. S., & Soloway, E. (2000). Using Technology to Support the Development of Conceptual Understanding of Chemical Representations. Fourth International Conference of the Learning Sciences, 121–128.
Zendler, A., & Greiner, H. (2020). The effect of two instructional methods on learning outcome in chemistry education: The experiment method and computer simulation. Education for Chemical Engineers, 30, 9–19. https://doi.org/10.1016/j.ece.2019.09.001
Downloads
Published
How to Cite
Issue
Section
Citation Check
License
Copyright (c) 2026 Ariyatun Ariyatun, Sylva Sagita, Devita Marlina Venessa, Vida Zenitha Sudariasri, Rosi Fitri Ramadani, Novi Andri Nurcahyono

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Journal of Science Education Research allows readers to read, download, copy, distribute, print, search, or link to its articles' full texts and allows readers to use them for any other lawful purpose. The journal allows the author(s) to hold the copyright without restrictions. Finally, the journal allows the author(s) to retain publishing rights without restrictions
- Authors are allowed to archive their submitted article in an open access repository
- Authors are allowed to archive the final published article in an open access repository with an acknowledgment of its initial publication in this journal





