Using open-source software and interdisciplinary teaching to increase digital forensics accessibility, and...

Area of Science:

• Digital Forensics
• Educational Pedagogy

Background:

• Rigorous forensic image processing requires adherence to industry standards and a deep understanding of underlying principles.
• Traditional teaching methods may not fully equip students with the practical skills needed for professional development in forensic image processing.
• The integration of open-source software and interdisciplinary approaches is explored to enhance learning.

Purpose of the Study:

• To evaluate a level 5 undergraduate "Forensic Image Processing" module over six years.
• To assess the effectiveness of using open-source software and interdisciplinary teaching for active learning.
• To investigate the impact of these methods on professional development and educational diversity.

Main Methods:

• Analysis of a six-year dataset from an undergraduate "Forensic Image Processing" module.
• Implementation of competence-based active learning strategies.
• Utilisation of open-source software and interdisciplinary teaching methodologies.
• Examination of student outcomes, including award gaps and attainment levels, with consideration for remote learning.

Main Results:

• The approach demonstrated benefits for cultural diversity in education.
• Computing workshops showed a negligible award gap.
• The exam revealed a 6% gap favoring ethnic minority students.
• A significant factor affecting student attainment was the delivery mode, with remote workshops leading to a drop in average performance.

Conclusions:

• Interdisciplinary teaching and open-source software foster competence-based active learning in forensic image processing.
• The pedagogical approach positively influences professional development and shows benefits for educational diversity.
• While overall attainment dropped during remote learning, the module's structure contributed to equitable outcomes across different student groups.