YoungHa Hwang  Seoung-Hey Paik

Vol. 69,  No. 5, pp. 241-251, Oct.  2025
10.5012/jkcs.2025.69.5.241

Emergent process model (EPM) Systems thinking Entropy Boltzmann distribution Lesson design

PDF JATS XML

The purpose of this study was to develop and analyze the effectiveness of that supports students in moving beyond the Sequential Process Model (SPM)—which explains natural phenomena based on single-variable, linear causality—toward understanding the Emergent Process Model (EPM), which interprets phenomena as outcomes of cumulative random motion and interactions among particles. To implement the explanatory structure of EPM with greater coherence, this study incorporated statistical mechanics concepts—entropy and the Boltzmann distribution—into the lesson design to help students interpret the probabilistic motion and interactions of many particles. The study was conducted with 17 students majoring in or double-majoring in chemistry education at a university of education located in Chungcheongbuk-do Province. Activities included pre- and post-instructional writing tasks, small group discussions, the introduction of a lattice-based particle arrangement model, and interviews. Written responses were analyzed by categorizing students’ explanatory frameworks as either SPM- or EPM-based, and pre- and post-instruction frequencies were compared using McNemar’s test. In addition, interview data were analyzed to examine changes in students’ conceptual structures. The results showed that while most participants initially explained phenomena using single-variable, linear reasoning, they shifted after instruction to EPM-based explanations incorporating energy distributions, the number of possible microstates, and cumulative interactions among particles. The significance of this study lies in demonstrating the effectiveness of a lesson that uses statistical mechanics concepts to enhance the coherence of the EPM explanatory structure and support students’ transitions in explanatory reasoning. These conceptual shifts suggest that participants began to interpret macroscopic phenomena based on particle-level interactions and cumulative processes—reflecting the development of core components of systems thinking, such as interaction, emergence, and nonlinearity.