CO-DESIGNING PYTHON-BASED INTERACTIVE MEDIA FOR TRIGONOMETRY LEARNING USING CHATGPT: A PRELIMINARY STUDY

Authors

  • Churun L Maknun Universitas Pendidikan Indonesia
  • Rizky Rosjanuardi Universitas Pendidikan Indonesia
  • Yaya S Kusumah Universitas Pendidikan Indonesia

DOI:

https://doi.org/10.26418/jpmipa.v17i1.96973

Keywords:

Interactive Media, Python, Trigonometry Learning

Abstract

Challenges in conceptual understanding and engagement persist in undergraduate trigonometry education, often due to static instructional methods and limited use of dynamic visual tools. Recent advances in artificial intelligence offer new opportunities for educators to independently design interactive instructional media, even without advanced programming expertise. Leveraging this potential, the present study aimed to develop and evaluate Python-based interactive media, integrating AI-generated content using the ADDIE design model. Data were collected through semi-structured interviews, Likert-scale questionnaires, and observations. Qualitative data were analysed thematically, while quantitative responses were summarised using descriptive statistics. The findings indicate that the interactive media was perceived as engaging in visualising trigonometric concepts, particularly sine and cosine, but revealed areas of confusion regarding the tangent function and challenges in self-directed learning. The study concludes that AI-assisted interactive media support conceptual understanding and student engagement, while also empowering educators to create personalised learning tools without requiring sophisticated technical skills. These results inform the design of accessible, technology-enhanced mathematics instruction

References

N. Bornstein, “Teaching transformations of trigonometric functions with technology,” J. Interact. Media Educ., vol. 2020, no. 1, pp. 1–9, 2020, doi: 10.5334/jime.503.

Y. C. Hsu, Y. H. Ching, J. Callahan, and D. Bullock, “Enhancing STEM majors’ college trigonometry learning through collaborative mobile apps coding,” TechTrends, vol. 65, pp. 26–37, 2021, doi: 10.1007/s11528-020-00541-0.

F. Abakah and D. Brijlall, “Misconceptions of mathematical concepts vis-à-vis how they pose as barriers to developing students’ conceptual understanding,” Gulf J. Math., 2024, doi: 10.56947/gjom.v16i2.1871.

O. M. Dairo, C. A. Okonkwo, and C. U. Orakwe, “A review of primary school teachers’ insight into traditional instruction and activity-based learning in mathematics education,” Int. J. Appl. Res. Soc. Sci., vol. 6, no. 11, pp. 2778–2790, 2024, doi: 10.51594/ijarss.v6i11.1725.

K. Zhou, Y. Li, and X. Han, “Visualization Techniques for Analyzing Learning Effects – Taking Python as an Example,” 2024, pp. 42–52. doi: 10.1007/978-3-031-50580-5_4.

M. Rahman and R. Paudel, “Visual Programming and Interactive Learning Based Dynamic Instructional Approaches to Teach an Introductory Programming Course,” Front. Educ. Conf., pp. 1–6, 2018, doi: 10.1109/FIE.2018.8658581.

V. Solorzano et al., “The Fourth Educational Revolution and the Impact of AI on Pedagogy,” Evol. Stud. imaginative Cult., pp. 1116–1131, 2024, doi: 10.70082/esiculture.vi.1315.

U. Mittal, S. Sai, V. Chamola, and Devika, “A Comprehensive Review on Generative AI for Education,” IEEE Access, p. 1, 2024, doi: 10.1109/access.2024.3468368.

M. Sarıtepeci and H. Durak, “Effectiveness of artificial intelligence integration in design-based learning on design thinking mindset, creative and reflective thinking skills: An experimental study,” Educ. Inf. Technol., 2024, doi: 10.1007/s10639-024-12829-2.

O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, “Systematic review of research on artificial intelligence applications in higher education – where are the educators?,” Int. J. Educ. Technol. High. Educ., vol. 16, no. 1, 2019, doi: 10.1186/s41239-019-0171-0.

T. M. Cavanagh and C. E. Kiersch, “Using commonly-available technologies to create online multimedia lessons through the application of the Cognitive Theory of Multimedia Learning,” Educ. Technol. Res. Dev., pp. 1–21, 2022, doi: 10.1007/s11423-022-10181-1.

V. Braun and V. and Clarke, “Using thematic analysis in psychology,” Qual. Res. Psychol., vol. 3, no. 2, pp. 77–101, Jan. 2006, doi: 10.1191/1478088706qp063oa.

C. L. Maknun, R. Rosjanuardi, and A. Jupri, “Epistemological Obstacle in Learning Trigonometry,” Math. Teaching-Research J., vol. 14, no. 2, pp. 5–25, 2022, [Online]. Available: https://eric.ed.gov/?id=EJ1350528

R. E. Mayer, “Cognitive Theory of Multimedia Learning,” Cambridge University Press, 2014, pp. 43–71. doi: 10.1017/CBO9781139547369.005.

R. Huang, J. M. Spector, and J. Yang, “Introduction to Educational Technology,” in Educational Technology, Springer, Singapore, 2019, ch. 1, pp. 3–31. doi: 10.1007/978-981-13-6643-7_1.

Z. Şahin, Cognitive analysis of students’ learning of trigonometry in dynamic geometry environment: a teaching experiment. open.metu.edu.tr, 2015. [Online]. Available: https://open.metu.edu.tr/handle/11511/25260

R. Duval, “A cognitive analysis of problems of comprehension in a learning of mathematics,” Educ. Stud. Math., vol. 61, no. 1–2, pp. 103–131, 2006, doi: 10.1007/s10649-006-0400-z.

H. Zulnaidi, E. Oktavika, and R. Hidayat, “Effect of use of GeoGebra on achievement of high school mathematics students,” Educ. Inf. Technol., vol. 25, no. 1, pp. 51–72, 2020, doi: 10.1007/S10639-019-09899-Y.

J. Sweller, “Cognitive Load During Problem Solving : Effects on Learning,” Cogn. Sci., vol. 285, no. 12, pp. 257–285, 1988, doi: 10.1207/s15516709cog1202.

D. H. Jonassen, “Computers as cognitive tools: Learning with technology, not from technology,” J. Comput. High. Educ., vol. 6, no. 2, pp. 40–73, 1995, doi: 10.1007/BF02941038.

D. H. Chang, M. P. Lin, S. Hajian, and Q. Q. Wang, “Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and Personalization,” Sustainability, 2023, doi: 10.3390/su151712921.

Downloads

Published

2026-02-10