Fostering scientific methods in simulations through symbolic regressions

Abstract

Two-dimensional computer and tablet PC physics simulations have proved to be effective in helping students understand the fundamental principles of physics and related natural processes. However, the current approach to using these simulations tends to follow a repetitive cognitive and procedural pathway, regardless of the specific physical concepts being explored or software environment being used. This approach involves manipulating the simulation interface and collecting data through interaction with controls, widgets, or other contextual elements. Students then attempt to determine how these experimental measurements align with established laws, interactions, or mechanisms, as the teacher might have previously explained. We believe that this approach, while appropriate for education, obscures scientific processes, mainly related to the hypothetico-deductive model. To address this issue, we have developed a simple and adaptable computer environment that makes use of genetic algorithms (GAs) and symbolic regression to derive many of the basic laws of nature from the data collected by students using the popular physics education technology (PhET) simulations environment. Our proposal enables learners to observe how the order and relationships of mathematical tokens are routinely refined as new data points are added to the simulation setting. This iterative distillation technique can also be augmented with the interplay of dimensional analysis. In contrast with other more sophisticated artificial intelligence patterns, GA fit into the realm of grey box machine learning models. These type of evolutionary algorithms achieve the sought results by evolving mathematical models on each stage in an understandable way, which mimics the way scientific breakthroughs are accomplished (over the course of generations of researchers and based of prior knowledge). By implementing this innovative approach, we can provide students with a more authentic empirical experience that fosters a deeper understanding of the principles of science and scientific discovery. Field tests with students supporting this claim have also been carried out.

Utilization of Technology in Physics Education: A Literature Review and Implications for the Future Physics Learning

“…Simulation technology has become a cornerstone in modern physics education, offering students a dynamic way to explore and understand complex concepts that are often challenging to grasp through traditional instructional methods alone. One significant advancement is presented by Llorella et al (2024), who developed an environment that leverages genetic algorithms and symbolic regression within physics simulations. This method not only supports the learning of fundamental physics principles but also introduces students to scientific processes resembling the hypothetico-deductive model used by researchers.…”

“…The integration of advanced technological tools in physics education is set to revolutionize the learning environment, offering both students and educators an array of opportunities to enhance educational outcomes. Simulation-based learning, as showcased by Llorella et al (2024), exemplifies how technology can facilitate a deeper understanding of complex physics concepts through dynamic and interactive environments. The utilization of genetic algorithms and symbolic regression within simulations not only supports the mastery of fundamental physics principles but also mimics real scientific processes, thereby fostering a genuine appreciation of scientific inquiry.…”