Details
| Original language | English |
|---|---|
| Title of host publication | SALMM 2019 - Proceedings of the 1st International Workshop on Search as Learning with Multimedia Information, co-located with MM 2019 |
| Subtitle of host publication | Proceedings of the 1st International Workshop on Search as Learning with Multimedia Information |
| Pages | 11-19 |
| Number of pages | 9 |
| Publication status | Published - Oct 2020 |
| Event | The 27th ACM International Conference on Multimedia - Nice, France Duration: 21 Oct 2019 → 25 Oct 2019 Conference number: 27 |
Abstract
Keywords
- cs.MM, H.5.1, Correlation, Multimodal, Video assessment, Knowledge gain
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
Cite this
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SALMM 2019 - Proceedings of the 1st International Workshop on Search as Learning with Multimedia Information, co-located with MM 2019: Proceedings of the 1st International Workshop on Search as Learning with Multimedia Information. 2020. p. 11-19.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Investigating Correlations of Automatically Extracted Multimodal Features and Lecture Video Quality
AU - Shi, Jianwei
AU - Otto, Christian
AU - Ewerth, Ralph
AU - Hoppe, Anett
AU - Holtz, Peter
N1 - Conference code: 27
PY - 2020/10
Y1 - 2020/10
N2 - Ranking and recommendation of multimedia content such as videos is usually realized with respect to the relevance to a user query. However, for lecture videos and MOOCs (Massive Open Online Courses) it is not only required to retrieve relevant videos, but particularly to find lecture videos of high quality that facilitate learning, for instance, independent of the video's or speaker's popularity. Thus, metadata about a lecture video's quality are crucial features for learning contexts, e.g., lecture video recommendation in search as learning scenarios. In this paper, we investigate whether automatically extracted features are correlated to quality aspects of a video. A set of scholarly videos from a Mass Open Online Course (MOOC) is analyzed regarding audio, linguistic, and visual features. Furthermore, a set of cross-modal features is proposed which are derived by combining transcripts, audio, video, and slide content. A user study is conducted to investigate the correlations between the automatically collected features and human ratings of quality aspects of a lecture video. Finally, the impact of our features on the knowledge gain of the participants is discussed.
AB - Ranking and recommendation of multimedia content such as videos is usually realized with respect to the relevance to a user query. However, for lecture videos and MOOCs (Massive Open Online Courses) it is not only required to retrieve relevant videos, but particularly to find lecture videos of high quality that facilitate learning, for instance, independent of the video's or speaker's popularity. Thus, metadata about a lecture video's quality are crucial features for learning contexts, e.g., lecture video recommendation in search as learning scenarios. In this paper, we investigate whether automatically extracted features are correlated to quality aspects of a video. A set of scholarly videos from a Mass Open Online Course (MOOC) is analyzed regarding audio, linguistic, and visual features. Furthermore, a set of cross-modal features is proposed which are derived by combining transcripts, audio, video, and slide content. A user study is conducted to investigate the correlations between the automatically collected features and human ratings of quality aspects of a lecture video. Finally, the impact of our features on the knowledge gain of the participants is discussed.
KW - cs.MM
KW - H.5.1
KW - Correlation
KW - Multimodal
KW - Video assessment
KW - Knowledge gain
UR - http://www.scopus.com/inward/record.url?scp=85074761015&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2005.13876
DO - 10.48550/arXiv.2005.13876
M3 - Conference contribution
SN - 978-1-4503-6919-0
SP - 11
EP - 19
BT - SALMM 2019 - Proceedings of the 1st International Workshop on Search as Learning with Multimedia Information, co-located with MM 2019
T2 - The 27th ACM International Conference on Multimedia
Y2 - 21 October 2019 through 25 October 2019
ER -