Abstract
In flipped math classrooms, chatbots are commonly used to assist students and provide personalized learning to improve self-regulation issues that students face when learning through online resources at home. ChatGPT, the state-of-the-art natural language model, has been tested in this study to explore its ability to impact middle school students’ math learning since middle school math is a crucial stage that affects future math learning success. The study tested ChatGPT’s accuracy by using it to answer questions from Taiwan’s past education examinations, and the accuracy rate was found to be as high as 90% (A+). Moreover, compared to most studies that developed chatbots for a single unit or course, this study found that ChatGPT’s accuracy in each of the six major areas of mathematics education in Taiwan exceeded 80% (A). The results indicate that ChatGPT is an excellent learning tool that can improve students’ self-regulation issues and has the potential to impact middle school math education.
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References
Turan, Z., Akdag-Cimen, B.: Flipped classroom in English language teaching: a systematic review. Comput. Assist. Lang. Learn. 33, 590–606 (2020)
Howell, R.A.: Engaging students in education for sustainable development: the benefits of active learning, reflective practices and flipped classroom pedagogies. J. Clean. Prod. 325, 129318 (2021)
López Belmonte, J., Fuentes Cabrera, A., López Núñez, J.A., Pozo Sánchez, S.: Formative transcendence of flipped learning in mathematics students of secondary education. Mathematics 7, 1226 (2019)
Rojas-Celis, C., Cely-Rojas, V.: Propuesta de enseñanza en Cálculo Vectorial: un acercamiento a la clase invertida. Revista científica 58–66 (2020)
Cevikbas, M., Kaiser, G.: Flipped classroom as a reform-oriented approach to teaching mathematics. ZDM Math. Educ. 52(7), 1291–1305 (2020). https://doi.org/10.1007/s11858-020-01191-5
Jiang, M.Y., Jong, M.S., Lau, W.W., Chai, C., Liu, K.S., Park, M.: A scoping review on flipped classroom approach in language education: challenges, implications and an interaction model. Comput. Assisted Lang. Learn. 35, 1218–1249 (2022)
Rasheed, R.A., et al.: Self-regulated learning in flipped classrooms: a systematic literature review. Int. J. Inf. Educ. Technol. 10(11), 848–853 (2020). https://doi.org/10.18178/ijiet.2020.10.11.1469
Lai, C.-L., Hwang, G.-J.: A self-regulated flipped classroom approach to improving students’ learning performance in a mathematics course. Comput. Educ. 100, 126–140 (2016)
Morales-Chicas, J., Graham, S.: Do I belong in my math class? The importance of perceived racial/ethnic context and math course sequence. Contemp. Educ. Psychol. 67, 102012 (2021)
Supriatna, I., Asmahasanah, S., Rachmadtullah, R., Asdar, A.K.: The effect of learning methods and self regulation on problem-solving ability of mathematics in elementary school. In: Journal of Physics: Conference Series, p. 012139. IOP Publishing (2019)
Wang, A.Y., Fuchs, L.S., Fuchs, D., Gilbert, J.K., Krowka, S., Abramson, R.: Embedding self-regulation instruction within fractions intervention for third graders with mathematics difficulties. J. Learn. Disabil. 52, 337–348 (2019)
Hew, K.F., Huang, W., Du, J., Jia, C.: Using chatbots to support student goal setting and social presence in fully online activities: learner engagement and perceptions. J. Comput. High. Educ. 35, 40–68 (2023)
Baidoo-Anu, D., Owusu Ansah, L.: Education in the era of generative artificial intelligence (AI): understanding the potential benefits of ChatGPT in promoting teaching and learning. Available at SSRN 4337484 (2023)
Kung, T.H., et al.: Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLoS Digit. Health 2, e0000198 (2023)
Zimmerman, B.J.: Attaining self-regulation: a social cognitive perspective. In: Handbook of Self-regulation, pp. 13–39. Elsevier (2000)
Dignath, C., Veenman, M.V.: The role of direct strategy instruction and indirect activation of self-regulated learning—evidence from classroom observation studies. Educ. Psychol. Rev. 33, 489–533 (2021)
Fang, J.-W., He, L.-Y., Hwang, G.-J., Zhu, X.-W., Bian, C.-N., Fu, Q.-K.: A concept mapping-based self-regulated learning approach to promoting students’ learning achievement and self-regulation in STEM activities. Interact. Learn. Environ. 1–23 (2022)
Carlini, N., et al.: Extracting training data from large language models. In: USENIX Security Symposium (2021)
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training (2018)
Nugroho, K.S., Sukmadewa, A.Y., Yudistira, N.: Large-scale news classification using bert language model: spark NLP approach. In: 6th International Conference on Sustainable Information Engineering and Technology 2021, pp. 240–246 (2021)
Chiu, K.-L., Collins, A., Alexander, R.: Detecting hate speech with GPT-3. arXiv preprint arXiv:2103.12407 (2021)
Li, Y.S., Lam, C.S.N., See, C.: Using a machine learning architecture to create an AI-powered chatbot for anatomy education. Med. Sci. Educ. 31, 1729–1730 (2021)
Bathija, R., Agarwal, P., Somanna, R., Pallavi, G.: Guided interactive learning through chatbot using bi-directional encoder representations from transformers (BERT). In: 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 82–87. IEEE (2020)
Risang Baskara, F.X.: Chatbots and flipped learning: enhancing student engagement and learning outcomes through personalised support and collaboration. IJORER : Int. J. Recent Educ. Res. 4(2), 223–238 (2023). https://doi.org/10.46245/ijorer.v4i2.331
Wollny, S., Schneider, J., Di Mitri, D., Weidlich, J., Rittberger, M., Drachsler, H.: Are we there yet?-A systematic literature review on chatbots in education. Front. Artif. Intell. 4, 654924 (2021)
Nguyen, H.D., Pham, V.T., Tran, D.A., Le, T.T.: Intelligent tutoring chatbot for solving mathematical problems in High-school. In: 2019 11th International Conference on Knowledge and Systems Engineering (KSE), pp. 1–6. IEEE (2019)
Ullah, S., Liaqat, M., Asif, A., Khan, A., Aslam, U., Asif, H.: Deep auto encoder based chatbot for discrete math course. In: 2022 International Conference on Recent Advances in Electrical Engineering & Computer Sciences (RAEE & CS), pp. 1–7. IEEE (2022)
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Li, PH., Lee, HY., Cheng, YP., Starčič, A.I., Huang, YM. (2023). Solving the Self-regulated Learning Problem: Exploring the Performance of ChatGPT in Mathematics. In: Huang, YM., Rocha, T. (eds) Innovative Technologies and Learning. ICITL 2023. Lecture Notes in Computer Science, vol 14099. Springer, Cham. https://doi.org/10.1007/978-3-031-40113-8_8
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