Abstract
Videos are commonly used as course materials for e-learning. In most existing systems, the lecture videos are usually presented in a linear manner. Structuring the video corpus has proven an effective way for the learners to conveniently browse the video corpus and design their learning strategies. However, the content analysis of lecture videos is difficult due to the low recognition rate of speech and handwriting texts and the noisy information. In this paper, we explore the use of external domain knowledge from Wikipedia to construct learning maps for online learners. First, with the external knowledge, we filter the noisy texts extracted from videos to form a more precise and elegant representation of the video content. This facilitates us to construct a more accurate video map to represent the domain knowledge of the course. Second, by combining the video information and the external academic articles for the domain concepts, we construct a directed map to show the relationships between different concepts. This can facilitate online learners to design their learning strategies and search for the target concepts and related videos. Our experiments demonstrate that external domain knowledge can help organize the lecture video corpus and construct more comprehensive knowledge representations, which improves the learning experience of online learners.
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Acknowledgments
The work described in this paper was supported by the Science and Technology Commission of Shanghai Municipality under research grant no. 14DZ2260800, the National Natural Science Foundation of China (No. 1103127, No. 61375016), SRF for ROCS, SEM, and the Fundamental Research Funds for the Central Universities.
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© 2015 Springer International Publishing Switzerland
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Wang, F., Li, X., Lei, W., Huang, C., Yin, M., Pong, TC. (2015). Constructing Learning Maps for Lecture Videos by Exploring Wikipedia Knowledge. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_54
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DOI: https://doi.org/10.1007/978-3-319-24075-6_54
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