Abstract
This research proposes a deep neural network architecture for detecting focal periods for online students in lecture archives. Due to the COVID-19 pandemic, most universities attempted to use online education instead of traditional classrooms. However, watching long lecture archives, just recorded face-to-face lectures, is difficult for students to keep their attention. Hence, how to provide focal periods of the lecture archives is essential to maintain educational effectiveness in such a situation. This research divides lecture archives with high quality and fixed camera angles into 1-min segments, counts how many times students have accessed each segment from LMS as the label data, and defines the students’ focal periods. Then, we demonstrated deep neural network architectures with the combined features to improve detection reliability. Our experiments showed that the proposed method could detect the focal periods with 56.8% accuracy. Although there is room for improvement in accuracy, this enables us to detect certain focal periods with a small amount of computation without using semantic features.
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Sheng, R., Ota, K., Hasegawa, S. (2022). An Automatic Focal Period Detection Architecture for Lecture Archives. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_71
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DOI: https://doi.org/10.1007/978-3-031-11647-6_71
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