Working Together: Integrating Computational Modeling Approaches to Investigate Complex Phenomena

Abstract

Complex systems are made up of many entities, whose interactions emerge into distinct collective patterns. Computational modeling platforms can provide a powerful means to investigate emergent phenomena in complex systems. Some research has been carried out in recent years about promoting students’ modeling practices, specifically using technologically advanced tools and approaches that allow students to create, manipulate, and test computational models. However, not much research had been carried out on the integration of several modeling approaches when investigating complex phenomena. In this paper, we describe the design principles used to develop a middle school unit about ants’ collective behavior that integrates three modeling approaches: conceptual drawn models, agent-based models, and system dynamics models. We provide results from an initial implementation of an 8th grade curricular unit, indicating that students engaged with several aspects of the modeling practice. Students’ conceptual knowledge about ant pheromone communication increased following learning the unit. We also found gains in students’ metamodeling knowledge about models as tools for investigating phenomena. We discuss the affordances and challenges of engaging students with several modeling approaches in science classroom.

Socio-Scientific Learning During the COVID-19 Pandemic: Comparing In-person and Virtual Science Learning Using Model-Evidence Link Diagrams

“…Such explanations are counter to the scientific explanation that increases in extreme weather events are linked to climate change caused by human activities, such as fossil fuel use (Schiermeier, 2018 ). Because students may face challenges related to being reflective and purposeful when making judgments about alternative explanations (e.g., relative plausibility judgments about the scientific explanation that human activities are the underlying cause of the climate crisis), instructional scaffolding may be required to facilitate their learning (see, for example, Bielik et al, 2021 ; Gobert & Pallant, 2004 ).…”

Interlocking models in classroom science

“…Research has begun to explore students working with multiple models in instruction (e.g., Bielik et al, 2021; Blikstein et al, 2016; Gouvea & Wagh, 2018; Ke et al, 2021; Lehrer & Schauble, 2012; Pierson et al, 2021). These can include descriptive models, explanatory or mechanistic models, systems models, simulations, data representations, and empirical investigations, among others.…”

“…They then used simulations to explore the effect of distancing and mathematical models to understand exponential viral spread given different reproduction rates. This research has suggested that different model forms support students to attend to different entities and relations, and further, that students can layer the learnings from one model onto another to deepen their understanding (Bielik et al, 2021; Lehrer & Schauble, 2012; Wilkerson et al, 2015).…”

Getting the Picture

“…The difficulties our students faced while constructing their models were also reported in case of students constructing mathematical models: wrong level of abstraction and erroneous assumptions (Maaß, 2006). Not being familiar with the affordances of the tool used to implement the model -in our case, insufficient command of the programming language involved -is found to have a detrimental effect on the quality of the model being produced (Bielik et al, 2021;Sins et al, 2005). Other behaviors we identified are characteristic for the development and use of computational models: not knowing whether unexpected behavior of a model is caused by an error or emergent behavior is typical for the development of agent-based models, as is incremental model development (Wilensky & Rand, 2015).…”

“…The (computational) aspects of the model construction played a secondary, supportive role as a means to reach that goal -cf. Basu et al (2016), Bielik et al (2021), Eraslan & Kant (2015), Maaß (2006) and Sins et al, (2005). Contrary to that approach, we focus specifically on the embedding of computational modeling into application domain from the point of view of computer science.…”