Bridging Chemistry and Computing Science: Innovative Teaching Tools in Pre-service Teacher Education

Main Article Content

Suthida Chamrat
Surasak Maotheuak
Kittaporn Puakanokhirun
Theenawat Nanta
Nattaya Apichatyotin
Warissara Kraijitmate
Pongsathorn Suyamool


This study investigated integrating computing science into chemistry education for pre-service teachers, focusing on using microcontrollers like Micro:bit and Arduino to develop STEM-based chemistry lessons. It involved eight pre-service teachers selected via purposive and voluntary sampling methods, participating in a longitudinal exploration from 2017 to 2020. Data were collected through various methods on courses in a chemistry education program, encompassing lesson plans, artifacts, videos, and photographs, and they were analyzed through content analysis. The key findings encapsulate the essence and impact of this integration: (1) Micro:bit emerged as the primary microcontroller used by pre-service teachers; (2) Projects often centered around environmental issues; (3) Effective integration of computer programming into chemistry teaching necessitated support from computer specialists or programmers; (4) The creation of microcontroller-based sensors/projects significantly enhanced the incorporation of computing science within science lessons; (5) Initial endeavors in blending computing science into chemistry education led to a rich variety of activity designs and innovations; (6) The development and use of microcontroller-based sensors facilitated the execution of more complex experiments in chemistry education. These findings underscore the potential of an interdisciplinary approach in enriching STEM-oriented chemistry education, highlighting the importance of teacher competence, professional development, and integrative teaching methodologies. This research provides vital insights for future pedagogical strategies and underscores the value of incorporating computing science in science education.

Article Details

How to Cite
Chamrat, S., Maotheuak, S., Puakanokhirun, K., Nanta, T., Apichatyotin, N., Kraijitmate, W., & Suyamool, P. (2024). Bridging Chemistry and Computing Science: Innovative Teaching Tools in Pre-service Teacher Education. Journal of Education and Innovative Learning, 4(1), 81–96. Retrieved from
Research Articles


Bennett, N., & Lemoine, G. J. (2014). What a difference a word makes: Understanding threats to performance in a VUCA world. Business Horizons, 57(3), 311-317. doi:10.1016/j.bushor.2014.01.001

Bevan, B. (2017). The promise and the promises of making in science education. Studies in Science Education, 53(1), 75-103. doi:10.1080/03057267.2016.1275380

Blikstein, P. (2018). Maker movement in education: History and prospects. In M. J. de Vries (Ed.), Handbook of technology education (419-437). Springer Cham. doi:10.1007/978-3-319-44687-5

Chamrat, S. (2019). Teachers as makers: The key provision of teacher preparations for STEM education. Journal of Physics: Conference Series, 1340(1), 012085. doi:10.1088/1742-6596/1340/1/012085

Conklin, W. (2011). Higher-order thinking skills to develop 21st-century learners. Huntington Beach, CA: Shell Educational Publishing, Inc

Diawati, C., Liliasari, Setiabudi, A., & Buchari. (2018). Using project-based learning to design, build, and test student-made photometer by measuring the unknown concentration of colored substances. Journal of Chemical Education, 95(3), 468-475. doi:10.1021/acs.jchemed.7b00254

Dong, J., Cao, D. S., Miao, H. Y., Liu, S., Deng, B. C., Yun, Y. H., ... & Chen, A. F. (2015). ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation. Journal of Cheminformatics, 7(1), 1-10. doi:10.1186/s13321-015-0109-z

Faikhamta, C., Ketsing, J., Tanak, A., & Chamrat, S. (2018). Science teacher education in Thailand: a challenging journey. Asia-Pacific Science Education, 4(1), 1-18. doi:10.1186/s41029-018-0021-8

Harel, I. E., & Papert, S. E. (1991). Constructionism. Ablex Publishing.

Honma, T. (2017). Advancing alternative pathways to science. Transformations: The Journal of Inclusive Scholarship and Pedagogy, 27(1), 41-50. doi:10.5325/trajincschped.27.1.0041

Isaacson, W. (2014). The innovators: How a group of inventors, hackers, geniuses and geeks created the digital revolution. New York: Simon and Schuster.

Koçak, E., Çelik, A. Y., & Uluyol, C. (2023). Pre-service Teachers' Environmental Literacy: The Role of STEM-based environmental education with microcontrollers. Participatory Educational Research, 10(5), 233-247. doi:10.17275/per.

Lee, I., Grover, S., Martin, F., Pillai, S., & Malyn-Smith, J. (2020). Computational thinking from a disciplinary perspective: Integrating computational thinking in K-12 science, technology, engineering, and mathematics education. Journal of Science Education and Technology, 29(1), 1-8. doi:10.1007/s10956-019-09803-w

Mabbott, G. A. (2014). Teaching electronics and laboratory automation using microcontroller boards. Journal of Chemical Education, 91(9), 1458-1463. doi:10.1021/ed4006216

Ministry of Education. (2017). Indicators and core contents of science learning group (Revised edition B.E. 2560) according to The Basic Education Core Curriculum B.E. 2551. Bangkok, Thailand: Printing Agriculture Cooperatives of Thailand. [in Thai]

Microsoft Learn Educator Center. (2023). Hacking STEM. Retrieved from

Neuendorf, K. A. (2018). 18 Content analysis and thematic analysis. In P. Brough (Ed.), Advanced research methods for applied psychology: Design, analysis and reporting (211-223), New York: Routledge.

Oteri, O. M. (2021, November 9-11). Opportunities in microcontroller based mobile labs as applied in STEM online, mobile, face to face and blended learning during and post covid-19 era; A case study of Arduino. In Proceeding of the 2021 Sustainable Leadership and Academic Excellence International Conference (SLAE), 1-6. Manama, Bahrain: IEEE. doi:10.1109/SLAE54202.2021.9788093

Pewkam, W., & Chamrat, S. (2022). Pre-service teacher training program of stem-based activities in computing science to develop computational thinking. Informatics in Education, 21(2), 311-329.

Rivers, K., Harpstead, E., & Koedinger, K. R. (2016, September 8-12). Learning curve analysis for programming: Which concepts do students struggle with?. In Proceedings of the 2016 ACM Conference on International Computing Education Research (143-151). Melbourne, Australia. doi:10.1145/2960310.2960333

Rodríguez-Becerra, J., Cáceres-Jensen, L., Diaz, T., Druker, S., Padilla, V. B., Pernaa, J., & Aksela, M. (2020). Developing technological pedagogical science knowledge through educational computational chemistry: a case study of pre-service chemistry teachers’ perceptions. Chemistry Education Research and Practice, 21(2), 638-654. doi:10.1039/C9RP00273A

Swaid, S. I. (2015). Bringing computational thinking to STEM education. Procedia Manufacturing, 3, 3657-3662. doi:10.1016/j.promfg.2015.07.761

Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351-380. doi:10.1007/s10639-012-9240-x

Scolnic, J. (2015). Design, development and analysis of the EVDuino robotics prototyping Platform (Doctoral dissertation). Tufts University, United States. Retrieved from

Wangsalae, S., & Swengam, W. (2021). Guidelines of computing science instruction for lower secondary school. Journal of the Association of Researchers, 26(1), 116-132. [in Thai]

Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127-147. doi:10.1007/s10956-015-9581-5