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

arXiv: https://arxiv.org/abs/2408.03472 · HTML: https://mlsanity.com/paper/2408.03472/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 73 references that are in the map.
- First posted on arXiv 6 Aug 2024.

## Builds on (most cited first, 16 of 16)

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- [Deep Learning based Multi-Label Image Classification of Protest Activities](https://mlsanity.com/paper/2301.04212/) (2023)
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- [Human-Centred Learning Analytics and AI in Education: a Systematic Literature Review](https://mlsanity.com/paper/2312.12751/) (2023)
- [Charting the Future of AI in Project-Based Learning: A Co-Design Exploration with Students](https://mlsanity.com/paper/2401.14915/) (2024)
- [Time Majority Voting, a PC-based EEG Classifier for Non-expert Users](https://mlsanity.com/paper/2207.12662/) (2022)
- [Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers](https://mlsanity.com/paper/2307.02819/) (2023)
- [BrainActivity1: A Framework of EEG Data Collection and Machine Learning Analysis for College Students](https://mlsanity.com/paper/2207.13239/) (2022)
- [Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education](https://mlsanity.com/paper/2311.05792/) (2023)

## Abstract (arXiv)

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