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We face complex global issues such as climate change that challenge our ability as humans to manage them. Models have been used as a pivotal science and engineering tool to investigate, represent, explain, and predict phenomena or solve problems that involve multi-faceted systems across many fields. To fully explain complex phenomena or solve problems using models requires both systems thinking (ST) and computational thinking (CT). This study proposes a theoretical framework that uses modeling as a way to integrate ST and CT. We developed a framework to guide the complex process of developing curriculum, learning tools, support strategies, and assessments for engaging learners in ST and CT in the context of modeling. The framework includes essential aspects of ST and CT based on selected literature, and illustrates how each modeling practice draws upon aspects of both ST and CT to support explaining phenomena and solving problems. We use computational models to show how these ST and CT aspects are manifested in modeling.

This paper introduces project-based learning (PBL) features for developing technological, curricular, and pedagogical supports to engage students in computational thinking (CT) through modeling. CT is recognized as the collection of approaches that  involve people in computational problem solving. CT supports students in deconstructing and reformulating a phenomenon such that it can be resolved using an information-processing agent (human or machine) to reach a scientifically appropriate explanation of a phenomenon. PBL allows students to learn by doing, to apply ideas, figure out how phenomena occur and solve challenging, compelling and complex problems. In doing so, students  take part in authentic science practices similar to those of professionals in science or engineering, such as computational thinking. This paper includes 1) CT and its associated aspects, 2) The foundation of PBL, 3) PBL design features to support CT through modeling, and 4) a curriculum example and associated student models to illustrate how particular design features can be used for developing high school physical science materials, such as an evaporative cooling unit to promote the teaching and learning of CT.

Computational experiments based on solving fundamental physics equations bring authentic science to the classroom. C omputational physics, which provides digital representations of natural phenomena by solving their governing equations numerically, has transformed areas as diverse as scientifi c research, engineering design (1), and fi lm production (2). It is also changing the way science is taught. The Molecular Workbench (MW) software, http://mw.concord.org, developed by the Concord Consortium, illustrates this perspective. MW models atomicscale phenomena on the basis of molecular dynamics and quantum mechanics calculations, which enables students to carry out computational experiments to investigate and learn a wide range of science concepts.The atomic world is alien to students: Electrons, atoms, and molecules are too small to be seen, and their interactions resemble nothing in the everyday observations that shape our intuitions. In the world of electromagnetic forces, thermodynamics, and quantum mechanics, there is little that students can assemble or tear apart with their bare hands in order to learn how those rules work. Traditional static ball-and-stick models of molecules fall short of conveying those essentially dynamic rules.When direct hands-on experiences are not feasible in the classroom, computational experiments provide attractive alternatives. Inquiry through computational experiments is similar to inquiry through real experiments: Students observe visualizations, raise "what-if " questions, formulate hypotheses, design and conduct investigations to test their ideas, and, fi nally, analyze and interpret results. In some cases, good simulations can be just as effective as their real counterparts (3, 4).For a computer simulation to become a versatile experimentation tool, a computational engine capable of accurately simulating many real-world situations is needed.Each instance of such a generic engine models a specifi c phenomenon.For example, the computer models of a pendulum and a spring are two engines for solving Newton's equation of motion. The two appear to be different, but they are computed using the exact same engine. MW is a tool for designing and conducting computational experiments with atoms and molecules, based on molecular dynamics and quantum dynamics simulation methods that originate from molecular modeling research (5). These computational methods approximate the fundamental laws in the world of atoms and molecules, and so MW's computational engines create dynamic visualizations of atomic motions and electron waves (see the fi rst fi gure). The molecular dynamics engine uses classical mechanics to predict the motion of atoms according to the forces computed from potential energy functions that model interatomic interactions (6). The quantum dynamics engine solves the time-dependent Schrödinger equation to predict the propagation of wave functions in potential fi elds that model atomic-scale structures (7). The engines can be confi gured to simulate real or thought experiments. U...

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.