Extracting Production Style Features of Educational Videos with Deep Learning

  • Enforced by the pandemic, the production of videos in educational settings and their availability on learning platforms allow new forms of video-based learning. This has a strong benefit of covering multiple topics with different design styles and facilitating the learning experience. Consequently, research interest in video-based learning has increased remarkably, with many studies focusing on examining the diverse visual properties of videos and their impact on learner engagement and knowledge gain. However, manually analysing educational videos to collect metadata and to classify videos for quality assessment is a time-consuming activity. In this paper, we address the problem of automatic video feature extraction related to video production design. To this end, we introduce a novel use case for object detection models to recognize the human embodiment and the type of teaching media used in the video. The results obtained on a small-scale custom dataset show the potential of deepEnforced by the pandemic, the production of videos in educational settings and their availability on learning platforms allow new forms of video-based learning. This has a strong benefit of covering multiple topics with different design styles and facilitating the learning experience. Consequently, research interest in video-based learning has increased remarkably, with many studies focusing on examining the diverse visual properties of videos and their impact on learner engagement and knowledge gain. However, manually analysing educational videos to collect metadata and to classify videos for quality assessment is a time-consuming activity. In this paper, we address the problem of automatic video feature extraction related to video production design. To this end, we introduce a novel use case for object detection models to recognize the human embodiment and the type of teaching media used in the video. The results obtained on a small-scale custom dataset show the potential of deep learning models for visual video analysis. This will allow for future use in developing an automatic video assessment system to reduce the workload for teachers and researchers.show moreshow less

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Author:Fatima Maya, Philipp KrieterORCiD, Karsten D. Wolf, Andreas Breiter
open access:Diamant - ohne Publikationskosten mit Lizenzhinweis
Fachbereich/Einrichtung:Hochschule Düsseldorf / Fachbereich - Medien
Document Type:Conference Proceeding
Year of Completion:2022
Language of Publication:English
Publisher:Gesellschaft für Informatik e.V.
Place of publication:Bonn
Parent Title (English):Proceedings of DELFI Workshops 2022 Karlsruhe, 12. September 2022
Editor:Martin Mandausch, Peter A. Henning
First Page:123
Last Page:132
DOI:https://doi.org/10.18420/delfi2022-ws-23
Tag:video-based learning
MOOC; YOLOv4-algorithm; deep learning; object detection; video features; video lecture design
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke
Licence (German):Creative Commons - CC BY-SA - Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International
Release Date:2025/04/04
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