Supporting Student System Modelling Practice Through Curriculum and Technology Design

Abstract

Developing and using models to make sense of phenomena or to design solutions to problems is a key science and engineering practice. Classroom use of technology-based tools can promote the development of students’ modelling practice, systems thinking, and causal reasoning by providing opportunities to develop and use models to explore phenomena. In previous work, we presented four aspects of system modelling that emerged during our development and initial testing of an online system modelling tool. In this study, we provide an in-depth examination and detailed evidence of 10th grade students engaging in those four aspects during a classroom enactment of a system modelling unit. We look at the choices students made when constructing their models, whether they described evidence and reasoning for those choices, and whether they described the behavior of their models in connection with model usefulness in explaining and making predictions about the phenomena of interest. We conclude with a set of recommendations for designing curricular materials that leverage digital tools to facilitate the iterative constructing, using, evaluating, and revising of models.

System-thinking progress in engineering programs: A case for broadening the roles of students

“…Deliberately encouraging students to reconsider the satellite as a whole may be an important guideline for complex engineering projects such as the one presented here. Bielik et al (2022) stress the need to revisit the overarching phenomenon, since students can easily lose the big picture of what they are modeling, and our findings seem to support that recommendation.…”

“…Modeling while studying complex systems allows students to evaluate the system's properties and to express its complexity by depicting the relationships between its components while iteratively revising them. The model revision process allows students to think about the system in new ways (Bielik et al, 2022). The use of models in education consists of two central parts: models that communicate scientific or engineering content to students, and modeling done by students to gain insight (Upmeier zu Belzen et al, 2019).…”

Ways of thinking in STEM-based problem solving

“…It is considered a key component of "critical thinking and problem solving" in 21st Century Learning (P21, 2015) and is often cited as a "habit of mind" in engineering education (e.g., Lippard et al, 2018;Lucas et al, 2014). Numerous definitions exist for systems thinking (e.g., Bielik et al, 2022;Damelin et al, 2017;Jacobson & Wilenski, 2022), with Bielik et al (2022) identifying such thinking as the ability to "consider the system boundaries, the components of the system, the interactions between system components and between different subsystems, and emergent properties and behaviour of the system" (p. 219). In more basic terms, Shin et al (2022) refer to systems thinking as "the ability to understand a problem or phenomenon as a system of interacting elements that produces emergent behavior" (p. 936).…”

“…Despite its centrality across the STEM domains, systems thinking is almost absent from mathematics education (Curwin et al, 2018). This is despite claims by many researchers that modelling, systems thinking, and associated thinking processes should be significant components of students' education (Bielik et al, 2022;Jacobson & Wilenski, 2022). Indeed, systems thinking is featured prominently in the US A Framework for K-12 Science Education (NRC, 2012) and the Next Generation Science Standards (NGSS Lead States, 2013), and has received considerable attention in science education (e.g., Borge, 2016;Hmelo-Silver et al, 2017;York et al, 2019) and engineering education (e.g., Lippard & Riley, 2018;Litzinger, 2016).…”

Multiple Levels of Heuristic Reasoning Processes in Scientific Model Construction

“…An implication is that work on frameworks may inform socio-cognitive approaches that are badly needed to teach scientific thinking skills (e.g. Osborne et al, 2004 ; Schwarz et al, 2009 ; Wiser and Smith, 2009 ; Windschitl et al, 2012 ; Lattery, 2016 ; Bielik et al, 2021 ), including grounded ( Stephens and Clement, 2010 ; Newcombe, 2013 ; Price et al, 2017 ; Alibali and Nathan, 2018 ; Ramey et al, 2020 ; Mathayas et al, 2021 ) and hierarchical approaches ( Schoenfeld, 1998 ; Williams and Clement, 2015 ; Nunez-Oviedo and Clement, 2019 ).…”