A Systemic view of the learning and differentiation of scientific concepts:The case of electric current and voltage revisited

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Ismo T Koponen
Tommi Kokkonen


In learning conceptual knowledge in physics, a common problem is the incompleteness of a learning process, where students’ personal, often undifferentiated concepts take on more scientific and differentiated form. With regard to such concept learning and differentiation, this study proposes a systemic view in which concepts are considered as complex, dynamically evolving structures. The dynamics of the concept learning and differentiation is driven by the competition of model utility in explaining the evidence. Based on the systemic view, we introduce computational model, which represents the essential features of the conceptual system in the form of directed graph (DGM), where concepts are nodes connected to other conceptual elements (nodes) in the graph. The results of a DGM are then compared to the empirical findings to identify differentiation between concepts of electric current and voltage based on a re-analysis of previously published empirical findings on upped secondary school students’ learning paths in the context of DC circuits. The comparison shows that the model predicts and explains many relevant, empirically observed features of the learning paths of concept learning and differentiation, such as: 1) Context-dependent dynamics, 2) the persistence of ontological shift and concept differentiation, and 3) the effects of communication on individual learning paths.  The systemic view and the DGM model based on it make these generic features of interest in concept learning and differentiation understandable and show that these features are associated with the guidance of theoretical knowledge. Finally, we discuss briefly the implications of the results on teaching and instruction. 

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Koponen, I. T., & Kokkonen, T. (2014). A Systemic view of the learning and differentiation of scientific concepts:The case of electric current and voltage revisited. Frontline Learning Research, 2(3), 140–166. https://doi.org/10.14786/flr.v2i2.120


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