Integrating HCI Datasets in Project-Based Machine Learning Courses: A College-Level Review and Case Study

TL;DR

This paper reviews and demonstrates how integrating human-computer interaction datasets into project-based machine learning courses can improve student engagement and teaching effectiveness, supported by a case study and best practices.

Contribution

It offers a comprehensive review and practical case study on incorporating HCI datasets into college ML courses, highlighting effective strategies and addressing challenges.

Findings

Increased student engagement and motivation

Enhanced skill development through hands-on projects

Effective teaching tools for complex ML concepts

Abstract

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