Utilizing ChatGPT for Content Analysis of STEM Activity Module within DIY, Tinkering, and Maker Frameworks
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Abstract
The objective of this research was to (1) study the application of artificial intelligence in qualitative data analysis employing a thematic analysis approach and (2) extract insights on the structural and component characteristics of STEM activities aligned with the DIY framework. The researcher designed and developed five STEM activity modules: UVC Box Experiment, Digital pH Meter, Air Sensor, Startup & Rare Earth Board Games, and Motion Sensors, underpinned by the DIY framework. The employment of ChatGPT 4.0, an AI leveraging natural language processing (NLP), facilitated content analysis through an issue-oriented analytical model encompassing four primary NLP steps: syntactic analysis, semantic analysis, entity recognition, and relationship extraction. The content analysis results indicated that ChatGPT 4.0 could analyze qualitative data promptly and precisely, mitigating biases inherent in researcher interpretations and identifying central themes and repetitive patterns within the data. This research suggested novel methodologies for qualitative data analysis, fostering the evolution and refinement of research processes.In analyzing the results of STEM activities within the DIY, Tinker, and Maker frameworks, eight pivotal characteristics were identified: (1) scientific inquiry and experimentation, (2) STEM concept integration, (3) coherent educational curriculum organization, (4) innovative learning and interactivity, (5) DIY and educational accessibility, (6) computational thinking and technology usage, (7) real-world application and problem-solving, and (8) creativity and adaptability. The study concluded that the NLP-based AI, known as ChatGPT version 4.0, was proficient in thematic analysis, offering significant promise as a tool for qualitative research. Furthermore, STEM activities rooted in the DIY, Tinker, and Maker frameworks reflect critical educational traits that are instrumental for enhancing learning management and the professional development of educators.
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หากผู้เสนอบทความมีความจำเป็นเร่งด่วนในการตีพิมพ์โปรดส่งลงตีพิมพ์ในวารสารฉบับอื่นแทน โดยกองบรรณาธิการจะไม่รับบทความหากผู้เสนอบทความไม่ปฏิบัติตามเงื่อนไขและขั้นตอนที่กำหนดอย่างเคร่งครัด ข้อมูลของเนื้อหาในบทความถือเป็นลิขสิทธิ์ของ Journal of Inclusive and Innovative Education คณะศึกษาศาสตร์ มหาวิทยาลัยเชียงใหม่
References
Ahn, J., Choi, K., & Kim, J. (2017). The effects of STEM education on students’ learning outcomes: A meta-analysis. International Journal of STEM Education, 4(1), 1-14.
Baldwin, S., Ching, Y. H., & Hsu, Y. C. (2017). Learning to make and making to learn: A case study of pre-service science teachers' maker projects. In P. Resta & S. Smith (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference (pp. 2302-2309). Association for the Advancement of Computing in Education (AACE).
Barrows, H. S. (1996). Problem-based learning in medicine and beyond: A brief overview. In L. Wilkerson & W. H. Gijselaers (Eds.), Bringing Problem-Based Learning to Higher Education: Theory and Practice (pp. 3–12). San Francisco, CA: Jossey-Bass.
Blikstein, P., & Krannich, D. (2013). The maker movement: A new paradigm for learning. Technology, Knowledge and Learning, 18(2), 157-166.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.
Breckler, S. J., Olson, J. M., & Wiggins, E.C. (2006). Social psychology alive. Boston: Thomson Wadsworth.
Bybee, R. W. (2009). The BSCS 5E instructional model: Origins and effectiveness. In N. G. Lederman & S. K. Abell (Eds.), Handbook of research on science education (pp. 582-594). Routledge.
Chad, R., & Lochmiller, C. R. (2021). Conducting thematic analysis with qualitative data. The Qualitative Report, 26(6), 2029 – 2044.
ChatGPT. (2024). OpenAI. Retrieved from https://chat.openai.com.
Cobern, W. W. (1993). Contextual constructivism: The impact of culture on the learning and teaching of science. In K. G. Tobin (Ed.), The Practice of Constructivism in Science Education (pp. 51-69). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
Enger, K. S. (2017). Environmental science: A study of interrelationships (15th ed.). New York: McGraw-Hill Education.
Fosnot, C. T. (2013). Constructivism: Theory, perspectives, and practice. New York: Teachers College Press.
Guest, G., MacQueen, K. M., & Namey, E. R. (2012). Applied thematic analysis. London: Sage Publications.
Halverson, E. R., & Sheridan, K. M. (2014). The maker movement in education. Harvard Educational Review, 84(1), 49-74.
Henrich, J. (2015). The secret of our success: How culture is driving human evolution, domesticating our species, and making us smarter. New Jersey: Princeton University Press.
Hidayat, R., Nugroho, I., Zainuddin, Z., & Ingai, T. A. (2023). A systematic review of analytical thinking skills in STEM education settings. Information and Learning Sciences, 7-8, 565-586.
Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 168-177).
Hsu, Y. C., Baldwin, S., & Ching, Y. H. (2017). Learning to make and making to learn: A case study of pre-service science teachers' maker projects. In P. Resta & S. Smith (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference (pp. 2302-2309). Association for the Advancement of Computing in Education (AACE).
ISTE. (2016). ISTE standards for students. Arlington, VA: International Society for Technology in Education.
Jurafsky, D., & Martin, J. H. (2009). Speech and language processing (2nd ed.). New Jersey: Pearson.
Kahneman, D. (2011). Thinking, Fast and Slow. New York: Farrar, Straus, and Giroux.
Kolb, D. A. (2014). Experiential learning: Experience as the source of learning and development. New Jersey: FT press.
Kolb, D. A., & Kolb, A. Y. (2005). Learning styles and learning spaces: Enhancing experiential learning in higher education. Academy of Management Learning & Education, 4(2), 193-212.
Krippendorff, K. (2018). Content analysis: An introduction to its methodology (4th ed.). Sage Publications.
Kukla, A. (2000). Social constructivism and the science classroom. Science & Education, 9(2), 191-201.
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge: Cambridge University Press.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Lee, L. W., Dabirian, A., McCarthy, I. P., & Kietzmann, J. H. (2020). Making sense of text: Artificial intelligence-enabled content analysis. European Journal of Marketing, 54, 615-644.
Manning, C. D., Raghavan, P., & Schütze, H. (2009). Introduction to information retrieval. Cambridge: Cambridge University Press.
Martin, L. (2015). The promise of the maker movement for education. Journal of Pre-College Engineering Education Research (J-PEER), 5(1), 1-12.
Marzano, R. J. (2003). What works in schools: Translating research into action. Alexandria, VA: Association for Supervision and Curriculum Development.
Mayer, R. E. (2005). The Cambridge handbook of multimedia learning. Cambridge: Cambridge University Press.
National Research Council. (2000). How people learn: Brain, mind, experience, and school. Washington, DC: National Academies Press.
Office of the Basic Education Commission. (2017). Active Learning. Bangkok: Office of the Basic Education Commission. [in Thai]
Papert, S., & Harel, I. (1991). Constructionism. Norwood, NJ: Ablex Publishing Corporation.
Piaget, J. (1954). The construction of reality in the child. New York: Basic Books.
Piaget, J. (2013). The construction of reality in the child (Vol. 82). London: Routledge.
Porter, A.C. (2002). Measuring the content of instruction: Uses in research and practice. Educational Researcher, 31(7), 3-14.
Roehrig, G. H., & Luft, J. A. (2004). Assessment of learning in tinkering environments. Journal of Science Education and Technology, 13(2), 187-198.
Rose, D. H., & Meyer, A. (2002). Teaching every student in the digital age: Universal design for learning. Alexandria, VA: Association for Supervision and Curriculum Development.
Runco, M. A. (2004). Creativity. Annual Review of Psychology, 55, 657–687.
Russell, S. J., & Norvig, P. (2010). Artificial intelligence: A modern approach (3rd ed.). Upper Saddle River, New Jersey: Pearson.
Sanders, M. (2009). STEM education and the 21st century workforce. The Bridge on Education, 40(1), 15-24.
Siregar, N. C., Rosli, R., Maat, S. M., & Capraro, M. M. (2019). The effect of science, technology, engineering and mathematics (STEM) program on students’ achievement in mathematics: A meta-analysis. International Electronic Journal of Mathematics Education, 15(1), em0549.
Stemler, S. E. (2000). An overview of content analysis. Practical Assessment, Research, and Evaluation, 7(1), 137-146.
Taşdemir, F. (2022). Examination of the effect of STEM education on academic achievement: A Meta-analysis study. Education Quarterly Reviews, 5(2), 282 – 298.
Trott, P. (2016). Creativity and innovation. London: Routledge.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman, Eds.). Cambridge, MA: Harvard University Press.
Wang, H. H., Moore, T. J., Roehrig, G. H., & Park, M. S. (2011). STEM integration: Teacher perceptions and practice. Journal of Pre-College Engineering Education Research (J-PEER), 1(2), 2.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
Yildirim, B. (2016). An Analyses and Meta-Synthesis of Research on STEM Education. Journal of Education and Practice, 7(34), 23-33.
