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    A new approach to teaching digital image processing addresses challenges in large undergraduate courses. This method enhances student engagement and simplifies grading for introductory digital image processing (IDIP) courses.

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    Area of Science:

    • Computer Science
    • Engineering Education

    Background:

    • Large undergraduate practical courses, particularly in image processing, face significant challenges.
    • Issues include limited scope of problems, heavy grading burden, and difficulties in detecting academic dishonesty.
    • Traditional teaching methods struggle to provide a comprehensive "big picture" view in large-scale settings.

    Purpose of the Study:

    • To develop and present a novel course approach for "Introduction to Digital Image Processing" (IDIP).
    • The aim is to address the inherent difficulties of teaching large undergraduate practical courses.
    • This approach is designed for students in media informatics, visual computing, and medical informatics.

    Main Methods:

    • The authors designed a new pedagogical strategy based on their experience with traditional large-scale image processing courses.
    • The novel approach was implemented for an "Introduction to Digital Image Processing" course at TU Wien.
    • Focus was placed on overcoming the limitations of small-scale problems and repetitive grading.

    Main Results:

    • The developed approach aims to mitigate the challenges associated with large student cohorts in practical courses.
    • It seeks to provide a more engaging and manageable learning experience for students in digital image processing.
    • The new method is intended to streamline the grading process for teaching staff.

    Conclusions:

    • The novel course approach offers a potential solution for the challenges in large-scale undergraduate practical courses.
    • This strategy can improve the educational experience in "Introduction to Digital Image Processing" for informatics and medical informatics students.
    • Further evaluation may be needed to quantify the impact on student learning and grading efficiency.