Integrating HCI Datasets in Project-Based Machine Learning Courses: A College-Level Review and Case Study
It addresses the challenge of improving teaching and learning in project-based ML courses for educators and students, though it is incremental as it builds on existing educational practices.
This study tackled the problem of enhancing machine learning education by integrating real-world HCI datasets into college courses, finding that it increased student engagement and skill development while helping instructors teach complex concepts.
This study explores the integration of real-world machine learning (ML) projects using human-computer interfaces (HCI) datasets in college-level courses to enhance both teaching and learning experiences. Employing a comprehensive literature review, course websites analysis, and a detailed case study, the research identifies best practices for incorporating HCI datasets into project-based ML education. Key f indings demonstrate increased student engagement, motivation, and skill development through hands-on projects, while instructors benefit from effective tools for teaching complex concepts. The study also addresses challenges such as data complexity and resource allocation, offering recommendations for future improvements. These insights provide a valuable framework for educators aiming to bridge the gap between