Unravelling Student Learning: Exploring Nonlinear Dynamics in Science Education

Abstract

Abstract Background: Traditional education research often relies on static linear approaches to measure dynamic systems involved in student information processing, overlooking the complexity of learning. Emerging research in related fields acknowledges the highly dynamic and nonlinear nature of cognitive states and information processing. Current educational research methods, predominantly based on quantitative and qualitative "snapshot" examinations, inadequately capture the dynamic and nonlinear aspects of cognitive processing during learning. Objective: This study aims to explore nonlinear dynamics as a means to describe and understand student learning processes. Methods: This study analyzed actions of 158,000 high school students in science-based immersive video games, specifically focusing on task completion within a virtual setting. Students aged 14-18, enrolled in Earth Science, Biology, Chemistry, and Physics programs, participated. Tasks, resembling Piagetian tasks, centered on volume conservation within a chemistry classroom context, employing the Student Task and Cognition Model (STAC-M) to emphasize computational cognition modeling. Results: The study tracked alterations in cognitive activations during information processing using derivatives, modeled through parameters from the authors' cognitive dataset. Employing the STAC-M model, achaotic attractors depicted convergence and sensitivity to initial conditions, reflecting cognitive associations and stability. Random data lacked the observed dynamic properties found in cognitive data, while bifurcation plots illustrated transitions from stability to chaos in cognitive processing pathways, highlighting the system's intricate nature. Conclusion: Modern science education explores beyond conventional assessments, acknowledging teaching methods' impact on students' cognitive processing. Achaotic attractors depict shifts from stable to unstable mental activities, highlighting the potential for diverse teaching approaches to minimize misconceptions and enable quicker transitions to responsive, stable learning states, aligning with educational objectives. Keyword: Cognition, Nonlinear Dynamics, Student Learning, Science Education.