This work addresses the challenge of extracting structure in educational and training media based on the type of material that is presented during lectures and training sessions. The narrative structure that arises out of a use of different types of presentation content such as slides, web pages, and white board writings is useful in segmenting an educational video for easy content access and nonlinear browsing of the material presented. Automatically detecting sections of videos as delineated by the use of supplementary teaching/instructional visual aids allows for structuralizing educational video with high level of semantics, and provides a concise means for organizing learning content according to the needs of different users in e-learning scenarios. Experiments on the videos from classrooms show encouraging results with discriminating different narrative sections in the proposed presented-material based video structuralization scheme.