2015 IEEE Frontiers in Education Conference (FIE)
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Abstract

Video of classroom lectures is a valuable and increasingly popular learning resource. A major weakness of the video format is the inability to quickly access the content of interest. The goal of this work is to automatically partition a lecture video into topical segments which are then presented to the user in a customized video player. The approach taken in this work is to identify topics based on text similarities across the video. The paper investigates the use of screen text extracted by Optical Character Recognition tools, as well as the speech text extracted by Automatic Speech Recognition tools. An automatic text-based segmentation algorithm is developed to identify topic changes and evaluated on a set of twenty-five lecture videos. The key conclusions are as follows. Screen text is a better guide to discovering topic changes than speech text, the effectiveness of speech text can be improved significantly with the correction of speech text, and combining screen text and accurate speech text can improve accuracy. Results are presented from surveys showing a high level of satisfaction among student users of automatically segmented videos. The paper also discusses the limits of automatic segmentation and the reasons why it is far from perfect.
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