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

Aug 4, 2026

This study explores integrating real-world machine learning projects that use human-computer interface (HCI) datasets within college-level courses to improve teaching and learning. The work combines a comprehensive literature review, analysis of related course websites, and a detailed case study to identify best practices. Results indicate higher student engagement, motivation, and practical skill development through hands-on projects, while supporting instructors in teaching complex concepts. It also examines challenges such as data complexity and resource allocation and provides recommendations for future improvements.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Introduction

Motivation

Research Questions

Related Works

Overview

Project-Based Learning in ML Education

arXiv :2408 .03472v1 [ cs .LG] 6 Aug 2024 Integrating HCI Datasets in Project-Based Machine Learning Courses: A College-Level Review and Case Study Xiaodong Qu, Matthew Key, Eric Luo and Chuhui Qiu The George Washington University Washington, DC 20052 (x.qu, matthewlkey, qluo, chqiu)@[gwu.edu](gwu.edu) 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 findings 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 theoretical knowledge and practical application in ML education. Keywords: Machine Learning education · Human-Computer Interfaces (HCI) · Real-World Datasets · Project-based learning · Applied Machine Learning 1 Introduction 1.1 Motivation The rapid advancement of Machine Learning (ML) and Artificial Intelligence (AI) technologies has created a pressing demand for skilled professionals in various industries [27,72,40,3,59] . In response, higher education institutions have expanded their ML curricula to include diverse courses aimed at imparting AI literacy and problem-solving skills [8,26,37,39] . These courses cater to undergraduates and working professionals seeking to enhance their expertise. Despite the proliferation of ML courses, significant discrepancies exist in their design and delivery. Project-based ML courses, emphasizing hands-on experience and practical application, show considerable variation in structure and content, creating challenges in achieving consistent educational outcomes. A critical aspect is the integration of datasets for practical exercises. HumanComputer Interaction (HCI) datasets offer rich opportunities for applying ML techniques. However, effective use requires careful course design and delivery. 2 Xiaodong Qu, Matthew Key, Eric Luo and Chuhui Qiu This paper addresses key issues by conducting a comprehensive literature review, analyzing related course websites, and performing a detailed case study to identify best practices and provide actionable insights for educators. The primary audience includes computer science professors and students. The focus on learners’ engagement and instructors’ perspectives aims to bridge the gap between theoretical knowledge and practical application, enhancing ML education for the AI-driven job market. 1.2 Research Questions The following research questions guide this study and form the basis for its analysis and conclusions: – What best practices can be identified from a review of academic papers related to project-based ML course delivery, particularly those incorporating HCI datasets? – What trends in course design, including the integration of math and statistics, and dataset usage emerge from an analysis of inperson ML courses at colleges and universities? – What insights can be gained from a detailed case study of teaching ML courses with specific HCI datasets over the past three years, considering both learner engagement and instructor perspectives? By addressing these questions, this paper aims to bridge the gap between theoretical knowledge and practical application in ML education. The findings are intended to guide educators in enhancing the design and delivery of projectbased ML courses, ultimately improving student learning outcomes and better preparing them for the demands of the AI-driven job

FAQs

What is the main goal of the study?

To identify best practices for integrating HCI datasets into project-based machine learning courses and provide actionable guidance for educators.

How does the paper conduct its research?

Through a comprehensive literature review, analysis of course websites, and a detailed case study focused on HCI dataset–based teaching in ML courses over the past three years.

What benefits are reported from using HCI datasets in project-based ML education?

The findings show increased student engagement and motivation, along with improved skill development via hands-on projects, while helping instructors teach complex concepts effectively.

Aug 4, 2026

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