Using argument-driven inquiry learning to improve students' mental models

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Rian Priyadi, Markus Diantoro, Parno, M. Reyza Arief Taqwa

2020 AIP Conference Proceedings Vol. 2215 Conference paper Cited by 2 Quartile

Abstract

Many efforts on the improvement of students' mental models have been reported. However, many of them were only explore mental models which are possesed by students. In this work, we report the enhancing of students' mental models on heat and temperature deeper than those reported previously. We used the experiment class that study by argument-driven inquiry learning and the control class that study by conventional learning. The data were obtained through pre- and post-tests using mental models instrument. The resulted data were grouped into three categories, i.e., scientific, synthetic, and initial. We employed Mann-Whitney U test and cross-tabulation to evaluate the enhancement of students' mental models. We found a significant increase in post-test scores in the experiment class and the students' frequency in the scientific group. Furthermore, it was found that a total of students' post-test scores in the experiment class increased more significant than the control class. The difference in enhancement shows that argument-driven inquiry learning gives an impact better than conventional class. Furthermore, we also discuss differences in student's understanding. © 2020 Author(s).

Affiliations

Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Malang, Jl. Semarang 5, Malang, 65145, Indonesia