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 -