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

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HCI International 2024 – Late Breaking Papers (HCII 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15382))

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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.

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Notes

  1. 1.

    https://faculty.cs.gwu.edu/xiaodongqu/.

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Authors and Affiliations

  1. The George Washington University, Washington, DC, 20052, USA

    Xiaodong Qu, Matthew Key, Eric Luo & Chuhui Qiu

Authors
  1. Xiaodong Qu
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  2. Matthew Key
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  3. Eric Luo
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  4. Chuhui Qiu
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Corresponding author

Correspondence to Xiaodong Qu .

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Editors and Affiliations

  1. Siemens Corporation, Princeton, NJ, USA

    Helmut Degen

  2. and Technology - Hellas, Foundation for Research, Heraklion, Crete, Greece

    Stavroula Ntoa

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Qu, X., Key, M., Luo, E., Qiu, C. (2024). Integrating HCI Datasets in Project-Based Machine Learning Courses: A College-Level Review and Case Study. In: Degen, H., Ntoa, S. (eds) HCI International 2024 – Late Breaking Papers. HCII 2024. Lecture Notes in Computer Science, vol 15382. Springer, Cham. https://doi.org/10.1007/978-3-031-76827-9_8

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  • DOI: https://doi.org/10.1007/978-3-031-76827-9_8

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