Automated Feedback in Undergraduate Computing Theory Courses: A Multi-Institutional Study

About This Grant

This project aims to serve the national interest by improving curricula in computer science education. Computing professionals need to understand the possibilities and limitations of computation in order to design efficient algorithms for problems that can be solved in practice, or to avoid large investments in attempts to implement solutions for problems which have been proven to require unreasonable amounts of time or other resources. Modeling computation is an important building block for this understanding, however, students often struggle with abstract modeling and visualization. A prior Level 1 Engaged Student Learning project resulted in a prototype tool which provides immediate feedback on the computational models designed by students. This Level 2 Engaged Student Learning project aims to add features to the tool, improve its usability and adaptability, and investigate its impact on student problem-solving at a larger scale, in different educational settings. The existing Automated Feedback for Computing Theory (AFCT) prototype tool was built on the widely used Java Formal Languages and Automata Package (JFLAP) visualization tool that aids students in learning the basic concepts of formal languages and automata theory. The enhanced tool developed in this project will initially be deployed and outcomes assessed in theoretical computer science courses at the five collaborating institutions. It will be made available under an opensource license to enable others to use and modify the software to suit their needs. The research questions are focused on understanding the impacts of the tool on students' behavior, performance, and learning of computing theory; whether students from different types of institutions are impacted in significantly different ways; and the effects of various types of feedback on students' learning. The tool's added functionality, improved usability, and availability as opensource software will encourage its adoption at other institutions and increase its educational benefits. The project, including the upgraded feedback tool and the associated research study, will provide new insights into pedagogical approaches for improving student learning and will help students to be better prepared to develop high-quality software. The NSF IUSE: EDU 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.