Fostering Synergistic Learning of Computational Thinking and Middle School Science in Computer-based Intelligent Learning Environments

Abstract

Recent advances in computing are transforming our lives at an astonishing pace. Computational Thinking (CT) is a term used to describe the representational practices and behaviors involved in formulating problems and their solutions so that the solutions can be carried out by a computer or a computing agent. Driven by the needs of a 21st century workforce, there is currently a great emphasis on teaching students to think computationally from an early age. Computer science education is gradually being incorporated into K-12 curricula, but a more feasible approach to make CT accessible to all students may be to integrate it with components of existing K-12 curricula. While CT is considered a vital ingredient of science learning, successfully leveraging the synergy between the two in middle school classrooms is non-trivial. This dissertation research presents Computational Thinking using Simulation and Modeling (CTSiM), a computer-based environment that integrates learning of CT concepts and practices with middle school science curricula. CTSiM combines the use of an agent-based visual language for conceptual and computational modeling of science topics, hypertext resources for information acquisition, and simulation tools to study and analyze the behaviors of the modeled science topics. We discuss assessments metrics developed to study the computational artifacts students build and the CT practices and learning strategies they employ in the CTSiM environment. These metrics can be used online to interpret students’ behavior and performance, and provide the framework for adaptively scaffolding students based on their observed deficiencies. Results from a classroom study with ninety-eight middle school students demonstrate the effectiveness of the CTSiM environment and the adaptive scaffolding framework. Students display better understanding of important science and CT concepts, improve their modeling performance over time, adopt useful modeling behaviors, and are able to transfer their modeling skills to new scenarios. In addition, students’ modeling performance and use of CT practices during modeling are significantly correlated with their science learning, demonstrating the synergy between CT and science learning.