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

TL;DR

This paper investigates how to integrate real-world human-computer interaction (HCI) datasets into college-level project-based ML courses to enhance teaching and learning. It combines a PRISMA-guided literature review, an analysis of course websites, and a mixed-methods case study using EEG/BCI data to identify best practices, benefits, and challenges. The findings provide a structured design framework that improves student engagement and skill development while giving instructors practical tools for teaching complex concepts and assessing learning. The work offers actionable guidance for educators to align project-based ML instruction with industry needs and AI-literate outcomes.

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

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• Figure 1: Selection process for the papers