Page 154 - Getting the Picture Modeling and Simulation in Secondary Computer Science Education

Chapter 7
modeling, on the other hand, these two stages are interpreted in computational terms. Formalization means constructing a computational model in the form of a computer program. That is in itself a cyclic process which cycles through the phases of establishing requirements and specification, designing the program, and its testing and evaluation. This cyclic formalization process is thus embedded within the encompassing modeling cycle. This aspect, too, signifies that modeling in Computational Science is essentially different from modeling in mathematics. The execution aspect entails designing and running experiments, thus using the computational model to perform simulation (Law, 2015). This framework is a novelty as it provides a detailed operationalization of the learning objectives for modeling and simulation within CS education.
The scientific value of our framework lies in the fact that it can serve as a conceptual model for the investigation of students’ cognitive activities in empirical studies, i.e., to explore their understanding and difficulties while engaging in modeling and simulation tasks through the development and use of computational models. This methodological application is particularly important since the application of computational thinking in modern science education is gaining interest. According to Lee et al., (2020), computational thinking “is seen as having the potential to deepen STEM25 learning by positioning students as young scientists and innovators through engagement in authentic STEM practices”. Regarding modeling itself, Gilbert & Justi (2016) state that it plays a significant role in the development and learning of science (Gilbert & Justi, 2016), as do Hallström & Schönborn (2019) who believe that it contributes to authentic STEM education. Similar to our framework, but with less detail, Sengupta et al. (2013) propose a theoretical framework for integrating computational thinking with science in primary and secondary education. In that framework, learning- by-doing activities are also represented as a cyclic process where students iterate between scientific enquiry (i.e., understanding of the scientific phenomena and modeling practices), algorithm design (i.e., development of a computational model) and engineering (i.e., refining models and simulations). Our framework is therefore particularly useful for research of computational thinking in context where computational modeling is used to enhance STEM learning. Furthermore, our framework provides an interpretation of modeling and simulation within CS which is not geared toward the use a specific software tool. In that sense, it is new as it shifts focus from the production of computational artifacts to embedding
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