engleski

Model of information density measuring in e-learning videos

There are many benefits to using video in education as shown in several decades of research. When authors make educational videos they must be especially careful that the content of that video material is adequ ate for the audience and that the purpose is on the term level. Also authors must pay attention that the content does not cause information overloading. Viewer can experience it if video has too much motions and movements. Information overload has become an increasingly popular area of study and a lot of authors indicated that information density of movies and videos have risen in the last few decades. High information density of videos and movies may cause information overloading and it has been proved by Lang et al in their theoretic model named Limited Capacity Model of Motivated Mediated Message Processing. This theoretic model suggests how to measure information density in videos and movies. This method is very slow and only trained observers can do this measuring properly. In this paper a new model for information density measuring in videos and movies is described and tested. This new model is based on special usage of background subtraction algorithms and in this paper multi-layer background subtraction algorithm based on color and texture was used. This algorithm is presented by authors Yao and Odobez on CVPR Visual Surveillance Workshop in 2007. Results of our model are compared with the trained observer’s results and the same video clips that have been used by Lang et al are used for testing. It can be seen that there is a significant correlation between information densities measured by trained observers and our model. Our model can be used to determine some forms of information density in videos and movies and it can be used to indicate higher possibility of information overloading.

information density ; visual activity ; e-learning

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