Collaborative Research: Improving Student Learning Outcomes in Computer Science Theory Courses Using Conceptual Models

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This project aims to serve the national interest by improving college students’ understanding of concepts in computer science theory courses. These foundational courses are designed to help students learn how to formalize computational problems, build efficient algorithms to solve problems, and argue convincingly, when a problem is so complex that efficient algorithms are unlikely to exist. However, students have difficulty in trying to understand the theoretical concepts in these courses, which can create a significant barrier to student success. While conceptual models have been widely adopted in primary and secondary STEM education to help students make sense of important concepts, their use in computer science education has not been well explored. This project will study the impact of teaching students how to create their own conceptual models of theories. It is expected that the project will result in measurable improvements in student understanding of theoretical concepts and ability to create well-conceived conceptual models. This project will also provide insights for instructors on how to effectively integrate conceptual models in existing computer science theory courses. Students who understand the theoretical concepts will be better prepared to address the complex computational problems that they will encounter in the computing workforce. The goal of this project is to improve student learning outcomes related to the theory of computing, particularly with respect to understanding, skill sets, and ability to apply theory to new situations. This study is grounded in constructivist theories of learning and previous work on the use of modeling in STEM education. After presenting a theoretical concept in a class, a set of open-ended questions will be posed to students who will be asked to draw a conceptual model and then iteratively revise it as they address the open-ended questions. The research questions that will be addressed by this study are: (1) What is the quality of models produced by students? (2) How do the models impact student understanding of theoretical concepts? (3) How do the models affect instructors’ teaching? To answer these questions, the project team will use mixed research methods to analyze qualitative and quantitative data including student survey responses, assessments of student models, student performance on exams, and weekly discussions with instructors. Educational materials will be made available to the STEM education community through a public online repository and project results will be presented at computer science education conferences. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.