INCORPORATING PRIMARY SOURCE MATERIAL INTO THE UNDERGRADUATE COMPUTER VISION CURRICULUM
| Autor: | Kevin Novins, Brendan McCane |
|---|---|
| Rok vydání: | 2001 |
| Předmět: |
Focus (computing)
business.industry Computer science Subject (documents) Field (computer science) Course (navigation) Artificial Intelligence ComputingMilieux_COMPUTERSANDEDUCATION Mathematics education Source material Computer vision Computer Vision and Pattern Recognition Artificial intelligence Student learning business Set (psychology) Curriculum Software |
| Zdroj: | International Journal of Pattern Recognition and Artificial Intelligence. 15:775-787 |
| ISSN: | 1793-6381 0218-0014 |
| DOI: | 10.1142/s0218001401001143 |
| Popis: | Computer vision is a very broad and rapidly changing field. A major challenge for teachers of this subject is to cover a large body of basic theory while at the same time giving a good sense of state-of-the-art research. Traditionally our fourth year undergraduate course in computer vision was taught using lectures and textbooks. We report on the results of two different alterations of the course, both of which focus on our students' own research into recent primary source literature. In the first alteration we used a seminar-based format. It was a qualified success. The students found the course interesting, gained a high-level understanding of the state-of-the-art in computer vision, and developed critical analysis skills. However, despite the fact that they worked hard, student learning of details of the techniques suffered. They were able to complete the course without a good understanding of the theoretical and mathematical foundations of computer vision. In response to this, our second alteration was to base the course around six problem-based learning units. The students benefitted from the reduced set of topics and higher level of practical work. |
| Databáze: | OpenAIRE |
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