Automatic Knowledge Discovery in Lecturing Videos via Deep Representation

Jinjiao Lin, Chunfang Liu, Yibin Li, Lizhen Cui, Rui Wang, Xudong Lu, Yan Zhang, Jian Lian
2019 IEEE Access  
In recent years, e-learning systems such as massive open online courses (MOOC) have been widely employed in academic institutions and have shown its power in enhancing the students' learning ability. Automatic detection and classification of knowledge points in lecture video would significantly enhance the performance of the online learning platform. Most of the previously presented approach for knowledge discovery focused on the text and audio documents, whereas the identification of knowledge
more » ... cation of knowledge points in videos still remains a challenge. To bridge this gap, we proposed a novel convolutional neural network which was designed for the characteristics of lecture video. It could both extract the temporalspatial and semantic information from the multimedia record. To evaluate the performance of the proposed technique, we conducted comparison experiments between the state-of-the-art methods and ours. The experimental results demonstrated that the presented approach outperformed the state-of-the-art techniques and could be potentially invaluable for the accurate discovery of knowledge points within videos. INDEX TERMS Knowledge acquisition, machine vision, neural network, optimization.
doi:10.1109/access.2019.2904046 fatcat:jpkx2b2jcra73k5fa5p22a7tki