Implementing the Next Generation Science Standards (NGSS) is difficult. While the benefits of having students engage in three-dimensional learning are profound (we get excited when students ask new questions to investigate or explain their diagrammatic models), the demands of such rigorous pedagogy are also clear. We believe that computational thinking and modeling promote student access to and engagement in science.
Our work began three years ago when our team, comprised of science content experts, NGSS writers, curriculum specialists, and applied linguists working in collaboration with classroom teachers, began developing a fifth-grade science curriculum for all students, with a focus on English learners. The project, Science And Integrated Language (SAIL), developed a curriculum that addresses fifth-grade NGSS performance expectations in physical science; life science; Earth and space science; and engineering, technology and applications of science. Students investigate their questions to argue and explain local, relevant phenomena.
A major focus of the SAIL curriculum is students’ development and use of models. Students develop both physical models in material environments and diagrammatic models in print environments. For example, in our physical science unit, students explore the phenomenon of their own school garbage and answer the driving question, “What happens to our garbage?” To investigate this question, students develop physical models of landfill bottles (Figure 1). Each group of students puts soil, water, and garbage materials such as metal, plastic, and food into a mason jar. Half of the student groups close their landfill bottles and the other half leave their bottles open, creating both closed and open landfill bottle systems. Students observe the changes in the properties of garbage materials in the closed and open systems over the course of the unit.
Over time, students figure out that the weight of the open landfill bottle decreases because gas particles (smell), which are caused by microbes decomposing some of the garbage materials, leave the open system. Students develop diagrammatic models at different time points throughout the unit. At the end of the unit, the diagrammatic models allow students to explain the causal mechanism (microbes cause food to decompose) that is not visible in the physical models (Figure 2).
Overall, students use models to argue and explain using evidence. However, at the end of the unit, we realized that the physical and diagrammatic models had both affordances and limitations. The physical model proved useful for students to make observations of the changing properties of garbage materials over time in their classroom. The diagrammatic model afforded opportunities for students to represent their thinking about the observable properties and changes. Still, some students found it difficult to articulate two science ideas that were invisible to the naked eye: (1) the idea that microbes decomposed the fruit from solid particles into gas particles (smell) and (2) the notion that in the closed system, the weight didn’t change because the fruit materials were still inside as gas particles (smell). Enter computational thinking and modeling.
Computational thinking, or “a way of solving problems, designing systems, and understanding human behavior that draws on concepts fundamental to computer science” (Wing, 2006, p. 33), affords opportunities for students to make complex science ideas and processes, such as decomposition and conservation of weight, more explicit. Computational modeling allows our students to identify each component in the system (e.g., microbes, solid banana, and gas banana) and give the components computational rules of behavior and interaction (e.g., move and “run” the system) to observe emergent, whole-system behaviors (Klopfer, 2003; Wilensky, 2001).
Using StarLogo Nova, an agent-based game and simulation programming environment that utilizes blocks-based programming, students work in groups to construct computational models of landfill bottles. After being introduced to blocks-based programming through embodied activities, groups use a starter model, a model with pre-programmed components, to develop computational models (Figure 3). Students program the microbe agent, upon collision with a solid banana particle, to (1) delete the solid banana particle and (2) create the gas banana particle. Over time, the solid banana weight decreases, while the gas banana weight increases. Through the entire process, the total weight of the banana (solid banana + gas banana) remains unchanged, thus representing conservation of weight during decomposition. In short, computational modeling enables our students to visually represent the invisible process of the microbes decomposing the solid particles of the banana into gas particles (rotting banana smell). Also, developing models with peers provides a rich context for all students, including English learners, to develop computational thinking and modeling while learning science and language.
As expected, there are challenges when integrating computational thinking and modeling into the SAIL curriculum. Teacher and student familiarity with StarLogo Nova, instructional time, and integration of computational modeling into the science unit storyline are among them. In addition, NGSS instructional shifts are new to many teachers, and adding another layer of complexity by integrating computational thinking and modeling might seem overwhelming. However, the affordances of computational thinking and modeling make addressing these challenges a worthwhile endeavor. Through computational thinking and modeling, students have the opportunity to model unseen or difficult-to-imagine science ideas as they make sense of a phenomenon and develop their science understanding.
Computer science is now included as part of STEM education (STEM Education Act of 2015) and by 2020, one of every two jobs in the STEM fields will be in computing (ACM pathways report, 2013). Computational thinking and modeling need to be in the classroom to prepare students for the future.
Kaczmarczyk, L., Dopplick, R., & EP Committee. (2014). Rebooting the pathway to success: Preparing students for computing workforce needs in the United States. Education Policy Committee, Association for Computing Machinery.(ACM, New York, 2014). http://pathways. acm. org/ACM_pathways_report. pdf Accessed.
Klopfer, E. (2003). Technologies to support the creation of complex systems models—using StarLogo software with students. Biosystems, 71(1-2), 111-122.
STEM Education Act of 2015, H.R.1020, 114th Cong. (2015).
Wilensky, U. (2001). Modeling nature’s emergent patterns with multi-agent languages. Proceedings of EuroLogo 2001.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.