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All Journal Numeracy : Jurnal Ilmiah Pendidikan Matematika Hexagon: Jurnal Ilmu dan Pendidikan Matematika Unnes Journal of Mathematics Education
Haarsa, Panyawat
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Education Mathematics

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Solving Bernoulli’s Equations Using Python: Enhancing Student Understanding Through Inquiry-Based Learning Haarsa, Panyawat
Unnes Journal of Mathematics Education Vol. 13 No. 3 (2024): Special Issue
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujme.v13i3.14283

Abstract

This study explores using Python to solve Bernoulli’s equations with the goal of enhancing student understanding. Inquiry-Based Learning (IBL) is integrated into it. The research included second-year students majoring in mathematics from Srinakharinwirot University. The teaching approach began with an introduction to Bernoulli’s equations in theory. These equations were then converted into linear form and solved using Python programming. Among the practical tasks in which students took part were post-test assessments, group problem-solving, and pre-test evaluations. Group members used Bernoulli’s equations to real-world scientific problems including fluid mechanics and population dynamics using Python to generate solutions and present discoveries. Inquiry-based learning (IBL) principles were used in the study, where students posed questions, looked into problems, and assessed the solutions using Python. The results demonstrated a greater understanding of mathematical concepts as well as computational techniques. The decrease in standard deviation between the pre- and post-test data showed how well IBL and Python combine to foster critical thinking and practical problem-solving skills. This method has the potential to assist student teachers acquire computational abilities and a deeper understanding of mathematics, which will better prepare them for careers as teachers.
Enhancing Mathematics Achievement Through Cooperative Learning: A Study of The Jigsaw Technique in Exponent Multiplication and Division Kaewrueang, Kwanpichcha; Phobubpa, Ratchadaporn; Haarsa, Panyawat
Hexagon: Jurnal Ilmu dan Pendidikan Matematika Vol. 3 No. 1 (2025): April
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/hexagon.v3i1.11727.

Abstract

Motivating students and fostering deeper conceptual understanding remain key challenges in mathematics education, particularly with abstract topics such as exponent multiplication and division. Achievement gaps often result from traditional lecture-based methods that fail to address diverse learning needs. Cooperative learning strategies, especially the Jigsaw technique, have been shown to improve student engagement, peer interaction, and content mastery. The method promotes active participation, accountability, and deep comprehension through structured group collaboration, making it particularly effective for challenging mathematical concepts. This study investigates the impact of the Jigsaw technique on eighth-grade students’ performance in exponent multiplication and division. A posttest-only experimental design was employed with 30 secondary school students. Data were collected through a researcher-developed posttest containing multiple-choice and short-answer questions aligned with national learning objectives. The data were analyzed using descriptive statistics (mean, percentage, standard deviation) and a one-sample t-test to evaluate statistical significance. The results demonstrated a significant improvement in student performance (M = 15.13, SD = 2.10, t = 8.186, p < .05), surpassing the 60% proficiency benchmark. These findings suggest that the Jigsaw method not only enhances mathematical understanding but also cultivates essential 21st-century skills such as critical thinking, communication, and teamwork.
INTEGRATING SYMPY INTO COLLABORATIVE LEARNING FOR PARTIAL FRACTIONS AND INTEGRATION: A QUALITATIVE STUDY IN CALCULUS Haarsa, Panyawat
Jurnal Numeracy Vol 12 No 2 (2025)
Publisher : Program Studi Pendidikan Matematika, Universitas Bina Bangsa Getsempena

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46244/numeracy.v12i2.3422

Abstract

This qualitative classroom study explores how collaborative learning (CL) supported by Python (SymPy) can strengthen university students’ understanding of partial fraction decomposition and its application to integration. Thirty‑two second‑year mathematics majors participated in four weekly sessions that blended manual problem solving with computational checking. Students worked in six small groups with defined roles (problem solver, coder, recorder, presenter), engaged in real‑world tasks (e.g., economic growth, fluid flow, and motion analysis), kept reflective journals, and delivered group presentations evaluated with an analytic rubric. Data sources comprised observations, journals, and presentation assessments. Thematic analysis indicates improved participation, clearer conceptual linking between algebraic manipulation and integral calculus, and more systematic error‑checking when Python was used to validate manual work. Groups demonstrating stronger within‑group communication tended to employ Python more effectively and reached higher rubric scores. The study discusses practical design choices for CL tasks that combine traditional and computational approaches, and reflects on limitations such as heterogeneous prior programming experience and the absence of pre–post achievement testing. Implications for practice include structuri
Co-Authors Kaewrueang, Kwanpichcha Phobubpa, Ratchadaporn