Document Type : Original Research Paper
Authors
1 Department of education, Faculty of Education and Psychology, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2 Department of Educational Administration, Faculty of Humanities, Islamic Azad University, Ahvaz Branch, Ahvaz, Iran
Abstract
Background and Objectives: Computer modelling helps a lot in learning comprehensive scientific concepts, including the causal mechanisms of phenomena, which is challenging for novice learners. Despite the many studies that have been published to show the effectiveness of using computers in the classroom, fewer studies have investigated the use of computer modelling and its effects on students' thinking. The causal structure of many natural and physics phenomena, the emphasis of science education standards on systems thinking development, and its improvement in students, the key role of causal reasoning in a better understanding of science, the increasing use of computer technologies in the physics classroom, the rapid development of computer software and Internet systems for modelling and simulating the real world in order to help physics teaching and learning, and to solve the shortcomings of paper modelling with the help of computers, prompted researchers to investigate the effectiveness of using computer modelling in the physics classroom to see how it would improve the students’ causal reasoning. Investigating the effectiveness of computer modelling on students' understanding of causal links and reasoning in physics phenomena is the main goal of this research.
Methods: A sample of 80 secondary high school students in the 11th grade was selected and participated in a semi-experimental design, consisting of two classes of 20 students (using computer modelling) and two classes of 20 students (using conceptual modelling on paper). The students' scores of the causal reasoning were collected in pre-test and post-test; to remove the pre-test effect (mental retention of answers), analysis of covariance was used. In this analysis, the effect of the pre-test scores on the post-test scores was first predicted with the help of simple linear regression, and after removing this effect, the difference between the post-test mean values of causal reasoning between the groups was explored with the analysis of variance. In this research, the mean difference was investigated both for the type of modelling (computer and paper) and for gender; therefore, due to having two independent variables, the analysis of covariance was two-way. With this analysis, the effect of the interaction between the gender variable and the teaching method was also measured.
Findings: Compared to paper modelling, computer modelling was effective in increasing students' ability to present coherent causal expressions and better explanations of scientific evidence and ideas, and enriched their systems thinking. Recognizing the reasoning elements, gathering evidence and expressing their reasons in order to end reasoning, as well as the coherence of reasoning, were more difficult for students who were trained with paper modelling than for those who were trained with the help of computer modelling. The findings showed that the connection among the pieces of evidence was one of the most difficult parts of physics reasoning. In fact, the student's ability to integrate the pieces of evidence in order to conclude the argument and express the result was less than their other reasoning abilities. However, computer modelling could improve this ability better than paper modelling
Conclusion: This quasi-experimental design helped us to reach important conclusions about the differences in causal reasoning between two different groups. Using computer tools can handle the learning of relatively complex cognitive skills such as causal reasoning. Computer simulation and conceptual models that are produced with computers can help to explain more causal links and more coherence of reasoning in physics classrooms. Therefore, we recommend curriculum designers and physics teachers use more computer simulation and modelling in order to strengthen system thinking in physics classrooms, and scientific explanations with the help of causal reasoning.
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©2023 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.
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