Automatic MOOC video classification using transcript features and convolutional neural networks
Chatbri, Houssem, McGuinness, KevinORCID: 0000-0003-1336-6477, Little, SuzanneORCID: 0000-0003-3281-3471, Zhou, JiangORCID: 0000-0002-3067-8512, Kameyama, Keisuke, Kwan, Paul and O'Connor, Noel E.ORCID: 0000-0002-4033-9135
(2017)
Automatic MOOC video classification using transcript features and convolutional neural networks.
In: ACM Multimedia 2017 - MultiEdTech Workshop, 23-27 Oct 2017, Mountain View, CA, USA.
ISBN 978-1-4503-5508-7
The amount of MOOC video materials has grown exponentially in recent years. Therefore, their storage and analysis need to be made as fully automated as possible in order to maintain their management quality.
In this work, we present a method for automatic topic classification of MOOC videos using speech transcripts and convolutional neural networks (CNN). Our method works as follows: First, speech recognition is used to generate video transcripts. Then, the transcripts are converted into images using a statistical co-occurrence transformation that we designed. Finally, a CNN is used to produce video category labels for a transcript image input.
For our data, we use the Khan Academy on a Stick dataset that contains 2,545 videos, where each video is labeled with one or two of 13 categories. Experiments show that our method is strongly competitive against other methods that are also based on transcript features and supervised learning.
Metadata
Item Type:
Conference or Workshop Item (Paper)
Event Type:
Workshop
Refereed:
Yes
Uncontrolled Keywords:
MOOC video classification; transcript features; convolutional neural networks (CNN)
Proceedings of the 2017 ACM Workshop on Multimedia-based Educational and Knowledge Technologies for Personalized and Social Online Training.
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ACM. ISBN 978-1-4503-5508-7