Abstract
Model-based learning (MBL) has an established position within science education. It has been found to enhance conceptual understanding and provide a way for engaging students in authentic scientific activity. Despite ample research, few studies have examined the cognitive processes regarding learning scientific concepts within MBL. On the other hand, recent research within cognitive science has examined the learning of so-called relational categories. Relational categories are categories whose membership is determined on the basis of the common relational structure. In this theoretical paper, I argue that viewing models as relational categories provides a well-motivated cognitive basis for MBL. I discuss the different roles of models and modeling within MBL (using ready-made models, constructive modeling, and generative modeling) and discern the related cognitive aspects brought forward by the reinterpretation of models as relational categories. I will argue that relational knowledge is vital in learning novel models and in the transfer of learning. Moreover, relational knowledge underlies the coherent, hierarchical knowledge of experts. Lastly, I will examine how the format of external representations may affect the learning of models and the relevant relations. The nature of the learning mechanisms underlying students’ mental representations of models is an interesting open question to be examined. Furthermore, the ways in which the expert-like knowledge develops and how to best support it is in need of more research. The discussion and conceptualization of models as relational categories allows discerning students’ mental representations of models in terms of evolving relational structures in greater detail than previously done.
Highlights
Model-based learning (MBL) has gained an established status in science education during the last three decades
Models are central in learning the concepts of physics as they encompass the concepts themselves and, importantly, the relations among them
I conceptualized models as relational categories, which are categories whose membership is determined by a common relational structure
Summary
Model-based learning (MBL) has gained an established status in science education during the last three decades. Goldwater and Schalk (2016) have proposed that this might provide an interesting interdisciplinary link between science education research (especially conceptual change) and cognitive science They proposed that the fundamental link between them is that relational concepts are central in learning science, as reasoning about such mundane topics as density, for example, requires quite sophisticated relational knowledge let alone more complex concepts found in physics. In this theoretical paper, I examine the role of models in concept learning from the viewpoint of relational knowledge and argue that models can be conceptualized as relational categories. By approaching MBL and its benefits by viewing models as relational structures or complex arrangements thereof opens up and interesting possibility for bridging research on MBL with cognitive science
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