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Abstract

ChatGPT is a chatbot with potential educational benefits, particularly in enhancing computational thinking (CT) proficiencies such as programming, debugging, and algorithmic thinking for students. Despite its promise, there is limited research on how ChatGPT can specifically support the integration of CT into mathematics education using tools like GeoGebra. The researchers implemented plugged-computational thinking in mathematics (Math+CT) lessons by means of the utilization of GeoGebra, an application that requires students to input commands in order to generate mathematical objects. The present investigation employed an educational design research (EDR) methodology in which the researchers incorporate ChatGPT into our Math+CT lessons to assist students in accomplishing the task. We purposely selected the participants who are mainly postgraduate students and collected data from the participants’ conversation with ChatGPT and recorded their screens while interacting with ChatGPT and our Math+CT task. We analyzed the data through descriptive qualitative method on the participants’ prompts, the final codes and the number of iterations. The researchers examined how ChatGPT could be utilized to assist the participants in writing GeoGebra commands in terms of its benefits and limitations. ChatGPT assisted most participants in completing the task successfully, with only a basic need for proficiency in GeoGebra commands, mathematics, and critical thinking. However, it revealed that participants did not yet utilize an affective prompt to ChatGPT. Furthermore, ChatGPT has the potential to be utilized for differentiated instruction due to the fact that its responses to individual users vary significantly based on the input prompts. Limited understanding of basic GeoGebra commands, and mathematical concepts could hinder the participants from training ChatGPT or prevent them from arguing with ChatGPT. This study enhances the existing literature by illustrating that ChatGPT can facilitate critical CT aspects, including programming and debugging, in a mathematics education context. This suggests that AI tools such as ChatGPT can contribute to the development of independent problem-solving skills, provide tailored support based on the needs of individual students, and enhance personalized learning experiences. Additional research involving students in school is required in order to gain a deeper understanding of the integration of ChatGPT into Math+CT lessons.

Keywords

ChatGPT Computational Thinking Educational Design Research GeoGebra Mathematics Education

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How to Cite
Yunianto, W., Lavicza, Z., Kastner-Hauler, O., & Houghton, T. (2024). Investigating the use of ChatGPT to solve a GeoGebra based mathematics+computational thinking task in a geometry topic. Journal on Mathematics Education, 15(3), 1027–1052. https://doi.org/10.22342/jme.v15i3.pp1027-1052
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References

  1. Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62. https://doi.org/10.61969/jai.1337500
  2. Cascella, M., Montomoli, J., Bellini, V., & Bignami, E. (2023). Evaluating the feasibility of ChatGPT in healthcare: An analysis of multiple clinical and research scenarios. Journal of Medical Systems, 47(1), 33. https://doi.org/10.1007/s10916-023-01925-4
  3. Castelvecchi, D. (2023). How will AI change mathematics? Rise of chatbots highlights discussion. Nature, 615(7950), 15-16. https://doi.org/10.1038/d41586-023-00487-2
  4. Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148
  5. Denscombe, M. (2010). The good research guide for small scale research projects. Open University Press. https://doi.org/10.1371/journal.pone.0017540
  6. Dos Santos, O. L., & Cury, D. (2023). Challenging the confirmation bias: Using ChatGPT as a virtual peer for peer instruction in computer programming education. 2023 IEEE Frontiers in Education Conference (FIE) (pp. 1–7). https://doi.org/10.1109/FIE58773.2023.10343247
  7. Ellis, A. R., & Slade, E. (2023). A new era of learning: Considerations for ChatGPT as a tool to enhance statistics and data science education. Journal of Statistics and Data Science Education, 31(2), 128–133. https://doi.org/10.1080/26939169.2023.2223609
  8. Frieder, S., Pinchetti, L., Chevalier, A., Griffiths, R.-R., Salvatori, T., Lukasiewicz, T., Petersen, P. C., & Berner, J. (2023). Mathematical capabilities of ChatGPT. Neural Information Processing Systems (NeurIPS), 36, 1–38. https://doi.org/10.48550/arXiv.2301.13867
  9. Hardman, P. (2023). Introducing: ChatGPT Edu-Mega-Prompts. https://drphilippahardman.substack.com/p/introducing-chatgpt-edu-mega-prompts
  10. King, A. (2013). Mathematical explorations: Finding Pi with Archimedes’s exhaustion method. Mathematics Teaching in the Middle School, 19(2), 116-123. https://doi.org/10.5951/mathteacmiddscho.19.2.0116
  11. Kynigos, C. (2007). Using half-baked microworlds to challenge teacher educators’ knowing. International Journal of Computers for Mathematical Learning, 12, 87–111. https://doi.org/10.1007/s10758-007-9114-2
  12. Li, P. H., Lee, H. Y., Cheng, Y. P., Starčič, A. I., & Huang, Y. M. (2023). Solving the self-regulated learning problem: Exploring the performance of ChatGPT in mathematics. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14099 LNCS. https://doi.org/10.1007/978-3-031-40113-8_8
  13. McKenney, S., & Reeves, T. C. (2018). Conducting Educational Design Research. Routledge. https://doi.org/10.4324/9781315105642
  14. Papert, S. (1980). Mindstorms: Children, Computers, and Powerful Ideas (1st ed.). Basic Books.
  15. Plata, S., De Guzman, M. A., & Quesada, A. (2023). Emerging research and policy themes on academic integrity in the age of Chat GPT and generative AI. Asian Journal of University Education, 19(4), 743–758. https://doi.org/10.24191/ajue.v19i4.24697
  16. Rane, N. (2023). Enhancing mathematical capabilities through ChatGPT and similar generative artificial intelligence: Roles and challenges in solving mathematical problems. SSRN Electronic Journal, 1–9. https://doi.org/10.2139/ssrn.4603237
  17. Reis, S. M., & Renzulli, J. S. (2018). The five dimensions of differentiation. International Journal for Talent Development and Creativity, 6(1), 87-94. https://files.eric.ed.gov/fulltext/EJ1296874.pdf
  18. Rudolph, J., Tan, S., & Tan, S. (2023). War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education. Journal of Applied Learning and Teaching, 6(1), 364-389. https://doi.org/10.37074/jalt.2023.6.1.23
  19. Shabunina, V., Sarancha, V., Maslak, V., Shevchenko, O., & Tur, O. (2023). Educational potential of ChatGPT: Teaching tool for students’ competencies development. 2023 IEEE 5th International Conference on Modern Electrical and Energy System (MEES) (pp. 1–6). https://doi.org/10.1109/MEES61502.2023.10402380
  20. Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158. https://doi.org/10.1016/j.edurev.2017.09.003
  21. Silver, E. (1995). The nature and use of open problems in mathematics education: Mathematical and pedagogical perspectives. Zentralblatt Fur Didaktik Der Mathematik/International Reviews on Mathematical Education, 27(2), 67-72.
  22. Sohail, S. S., Farhat, F., Himeur, Y., Nadeem, M., Madsen, D. Ø., Singh, Y., Atalla, S., & Mansoor, W. (2023). Decoding ChatGPT: A taxonomy of existing research, current challenges, and possible future directions. Journal of King Saud University - Computer and Information Sciences, 35(8), 101675. https://doi.org/10.1016/j.jksuci.2023.101675
  23. Tomlinson, C. A. (1999). Differentiated Classroom: Responding to the Needs of All Learners. Association for Supervision and Curriculum Development.
  24. van Borkulo, S. P., Chytas, C., Drijvers, P., Barendsen, E., & Tolboom, J. (2021). Computational thinking in the mathematics classroom: Fostering algorithmic thinking and generalization skills using dynamic mathematics software. ACM International Conference Proceeding Series. https://doi.org/10.1145/3481312.3481319
  25. Vygotsky, L. (1978). Mind and Society: The Development of Higher Psychological Processes. Harvard University Press.
  26. Wardat, Y., Tashtoush, M. A., AlAli, R., & Jarrah, A. M. (2023). ChatGPT: A revolutionary tool for teaching and learning mathematics. Eurasia Journal of Mathematics, Science and Technology Education, 19(7), em2286. https://doi.org/10.29333/ejmste/13272
  27. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
  28. Wollny, S., Schneider, J., Di Mitri, D., Weidlich, J., Rittberger, M., & Drachsler, H. (2021). Are we there yet?-A systematic literature review on chatbots in education. Frontiers in artificial intelligence, 4, 654924. https://doi.org/10.3389/frai.2021.654924
  29. Yunianto, W., Bautista, G., Prahmana, R. C. I., & Lavicza, Z. (2023). GeoGebra applet to learn programming and debugging in mathematics lessons. 2023 International Symposium on Computers in Education (SIIE) (pp. 1–5). https://doi.org/10.1109/SIIE59826.2023.10423707
  30. Yunianto, W., Bautista Jr, G., & Dias Prasetyo, B. (2024). A HLT for integrated CT and mathematics lessons: Supporting students’ possible struggles when debugging in GeoGebra environment. International Journal for Technology in Mathematics Education, 31(1), 11–19. https://doi.org/10.1564/tme_v31.1.02
  31. Yunianto, W., Prodromou, T., & Lavicza, Z. (2023). Debugging on GeoGebra-based Mathematics+Computational Thinking lessons. 28th Asian Technology Conference in Mathematics (pp. 322-331). https://atcm.mathandtech.org/EP2023/regular/22065.pdf
  32. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators?. International Journal of Educational Technology in Higher Education, 16(1), 1-27. https://doi.org/10.1186/s41239-019-0171-0
  33. Zdravkova, K., Dalipi, F., & Ahlgren, F. (2023). Integration of large language models into higher education: A perspective from learners. 2023 International Symposium on Computers in Education (SIIE) (pp. 1–6). https://doi.org/10.1109/SIIE59826.2023.10423681