An Interactive Digital Image Processing Course Utilizing Tools in the Jupyter Ecosystem

ABSTRACT This paper proposes a course designed for engineering students about digital image processing using tools from the Jupyter ecosystem, including Jupyter Notebooks, JupyterHub, and Jupyter Widgets. The course integrates theoretical concepts with different practical applications through project work and real‐world case analysis to ensure involvement and comprehension. Jupyter Widgets represent one of the main features of the course and enable interactive learning through data manipulation capabilities that create dynamic visualizations. Two other important components of the course are case studies and projects. These components teach students how to solve real‐world image processing problems and strengthen their problem‐solving skills. Additionally, regular exercises reinforce learning and ensure that students can apply theoretical knowledge in practical scenarios. While a survey conducted among participants indicated a generally positive reception, the focus on Jupyter tools and real‐world applications was particularly appreciated, demonstrating the course's success in bridging the gap between theory and practice. Future iterations of the course will continue to build on these strengths, further enhancing the educational experience and better preparing students for professional careers in engineering.

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Publication Details

Journal
Computer Applications in Engineering Education
Published
2025-12-04
DOI
https://doi.org/10.1002/cae.70121
Primary Topic
Experimental Learning in Engineering
Type
article
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article

An Interactive Digital Image Processing Course Utilizing Tools in the Jupyter Ecosystem

Levent Bayındır
Computer Applications in Engineering Education
Experimental Learning in Engineering
article

An Interactive Digital Image Processing Course Utilizing Tools in the Jupyter Ecosystem

Levent Bayındır
article en

Abstract

ABSTRACT This paper proposes a course designed for engineering students about digital image processing using tools from the Jupyter ecosystem, including Jupyter Notebooks, JupyterHub, and Jupyter Widgets. The course integrates theoretical concepts with different practical applications through project work and real‐world case analysis to ensure involvement and comprehension. Jupyter Widgets represent one of the main features of the course and enable interactive learning through data manipulation capabilities that create dynamic visualizations. Two other important components of the course are case studies and projects. These components teach students how to solve real‐world image processing problems and strengthen their problem‐solving skills. Additionally, regular exercises reinforce learning and ensure that students can apply theoretical knowledge in practical scenarios. While a survey conducted among participants indicated a generally positive reception, the focus on Jupyter tools and real‐world applications was particularly appreciated, demonstrating the course's success in bridging the gap between theory and practice. Future iterations of the course will continue to build on these strengths, further enhancing the educational experience and better preparing students for professional careers in engineering.

Computer Applications in Engineering EducationVol. 34(1)
Kocaeli Üniversitesi (TR)
Openalex Percentile: Top 49%
Experimental Learning in Engineering
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