Collaborative Research: Bridging the Gap Between Education and Industry Practices in Computer Architecture Through Learner-Centered Teaching

This project aims to serve the national interest by improving undergraduate computer architecture education so that students learn how modern computing systems are designed for rapidly changing technological needs. Many products and services now depend on specialized computing hardware for artificial intelligence, data analytics, gaming, and cybersecurity, creating a need for undergraduate courses to give students opportunities to ask open-ended design questions, compare alternatives, and connect hardware design to real-world software workloads that are essential to applications including artificial intelligence and quantum cryptography. The significance of this work lies in its potential to help students move from learning existing computer designs to developing the reasoning, problem-solving, and teamwork skills needed to create new computing designs. This Level 1 Engaged Student Learning project plans to redesign selected computer architecture learning experiences around active, student-centered approaches that combine preparation before class, guided inquiry, and collaborative design studios. The importance of the project is its potential to produce adaptable course materials and evidence-based teaching practices that help undergraduate students engage more deeply with computer architecture and prepare for emerging STEM careers. The project's goals are to develop, implement, and study learner-centered instructional modules that introduce workload-driven instruction set architecture and domain-specific architecture design as core learning experiences in undergraduate computer architecture courses. The scope of the project includes a three-year, cross-institutional effort to adapt and test modules across earlier and advanced computer architecture courses at a quarter-based private university and a semester-based public university. The project plans to use flipped learning for foundational concepts, inquiry-based learning to guide students in analyzing software workloads and proposing architecture design choices, and problem-solving studio activities to support collaborative design, simulation, and evaluation. Example learning contexts include artificial intelligence and machine learning workloads and quantum information science focused post-quantum cryptography workloads, which allow students to connect architecture concepts to contemporary computing needs. The project plans to study the effectiveness and transferability of these instructional approaches using pre- and post-tests, student work products, surveys, interviews, instructor reflections, peer observations, and cross-institutional comparisons. The project plans to disseminate findings, reusable modules, studio materials, and evaluation rubrics through open educational resources, conference presentations, and peer-reviewed publications to advance understanding of conditions under which improved student learning occurs in computer architecture education. 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.