ENHANCING ENGINEERING MATH UNDERSTANDING FOR MECHANICAL ENGINEERING EDUCATION STUDENTS VIA VAN HIELE PHASES AND GEOGEBRA
Abstract
This study examined the integration of Van Hiele teaching phases and GeoGebra software to enhance comprehension in engineering mathematics among mechanical engineering education students. A quasi-experimental design was employed to compare learning outcomes between an experimental group using this integrated approach and a control group receiving traditional instruction. Structured teaching methods in this context referred to a systematic instructional framework based on the Van Hiele model, which organized learning into progressive phases from basic exposure to advanced conceptual analysis to build a solid cognitive foundation. Meanwhile, dynamic visualisation tools, denoted as interactive software, such as GeoGebra, that allowed students to manipulate and explore mathematical models in real time, thereby transforming abstract concepts into tangible visual experiences. Data were collected using mathematics proficiency tests and were analysed through the Structure of the Observed Learning Outcome taxonomy and Cognitive Load Theory principles. The findings revealed that the mean score increase represented the absolute difference between pre-test and post-test averages, with the control group showing a higher raw improvement (23.29%) than the experimental group (16.17%). However, the normalized gain (N-gain), which measured relative learning progression accounting for initial knowledge levels, was greater in the experimental group (48%) compared to the control group (40%). A t-test analysis (p < 0.05) confirmed a statistically significant difference, highlighting the advantages of integrating Van Hiele teaching phases with GeoGebra in engineering mathematics education. This study contributed to the development of technology-enhanced learning strategies in mathematics education and recommended that future research explore the scalability and cross-disciplinary applicability of this integrative approach.
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DOI: https://doi.org/10.17509/jmee.v12i1.84470
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