• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.359-367
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• 2022

The purpose of this study is to examine the perception of college students about English classes using TED talks and to examine whether TED talks are appropriate as a learning material for college English. As a college English subject, 50-60 minutes of online classes were conducted for one semester where TED talks were used, and the data collected by conducting a survey on learners' English learning motivation, interest, attitude, satisfaction, and learning effect were analyzed utilized SMART PLS 3. The results are as follows. First, English learning motivation had a statistically significant effect on learning attitude while it did not affect the learning satisfaction. Second, the level of interest in the TED Talk-using class had a positive effect on the learning attitude and satisfaction. Third, the learning attitude positively affected the learning effect perception. Fourth, satisfaction with the TED Talk class had a positive effect on the learning effect perception. In conclusion, English classes using TED talk can increase the interest and satisfaction of learners, and induce active class participation, which lead to a positive perception in learners' learning effects. Therefore, this study implies that TED talks are valuable and significant enough as materials in college English classes.

• Journal of Computing Science and Engineering
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• v.11 no.1
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• pp.24-31
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• 2017

Face-based video retrieval has become an active and important branch of intelligent video analysis. Face profiling and matching is a fundamental step and is crucial to the effectiveness of video retrieval. Although many algorithms have been developed for processing static face images, their effectiveness in face-based video retrieval is still unknown, simply because videos have different resolutions, faces vary in scale, and different lighting conditions and angles are used. In this paper, we combined content-based and semantic-based image analysis techniques, and systematically evaluated four mainstream local features to represent face images in the video retrieval task: Harris operators, SIFT and SURF descriptors, and eigenfaces. Results of ten independent runs of 10-fold cross-validation on datasets consisting of TED (Technology Entertainment Design) talk videos showed the effectiveness of our approach, where the SIFT descriptors achieved an average F-score of 0.725 in video retrieval and thus were the most effective, while the SURF descriptors were computed in 0.3 seconds per image on average and were the most efficient in most cases.