Abstract:

At universities, the courses about computer vision and image processing have been usually taught using a classical methodology based on textbooks, notes and practical exercises which are explained on a blackboard, although sometimes the teachers use video projector and PC presentations for showing slides with static contents; mainly slides with text, figures and photos. However, this teaching model is focused in teacher instead of students and consequently, this approach is not effective when the teacher seeks to achieve cognitive objectives which involve a student's critical thinking. That is, student should be able to develop skills such as acquired knowledge about how filters, operators, methods and techniques for image processing are implemented by computer vision software libraries. Besides, student should be motived to use that background in order to solve practical and real problems in which it is advisable both a study and an analysis of features based on image processing.

This manuscript will present the development, implementation, and assessment of a practical-cases-based engineering course, more specifically in image and video processing course at the University of Alicante. All course lectures and hands-on laboratory activities have the main goal that student acquires not only image-specific technical skills but also general knowledge in data analysis to discover phenomena in pixel regions of images and video frames. This way, our teaching-learning process accomplishes both knowledge assimilation and skill developments using a strategy continuous evaluation.

In addition, this work will present a learning analysis based on a comparison of student results by considering the prior knowledge (at the beginning) and acquired knowledge as well as experience (at the end of the course) in issues related to specific subject.

The image and video processing course is a compulsory subject of Degree in Multimedia Engineering at the University of Alicante, in Spain. It is taught during 14-week period of the third academic year and it also has a workload of 6 ECTS (European Credit Transfer and Accumulation System) distributed as 30 hours of computer and laboratory practices and 30 hours of theory class. Nowadays, 80 students are enrolled in this course.

Keywords:

Engineering Education, Image processing, Practical cases and projects-based learning, Continous assessment.