Creating TikToks, Memes, Accessible Content, and Books from Engineering Videos? First Solve the Scene Detection Problem

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Autor/inn/en	Angrave, Lawrence; Li, Jiaxi; Zhong, Ninghan
Titel	Creating TikToks, Memes, Accessible Content, and Books from Engineering Videos? First Solve the Scene Detection Problem
Quelle	(2022), (22 Seiten)Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; Monographie
Schlagwörter	Instructional Materials; Material Development; Video Technology; Course Content; Problem Solving; Engineering Education; Electronic Publishing; Books; Semantics; Accuracy; Visual Aids; Mathematics; Access to Education; Technology Uses in Education; Open Source Technology; Assistive Technology; Layout (Publications); Transcripts (Written Records); Hearing Impairments; Deafness; Classification; Artificial Intelligence + Suchen Sie Ihr Suchwort? Lehrmaterial; Lehrmittel; Unterrichtsmedien; Lehrmaterialentwicklung; Kursprogramm; Problemlösen; Ingenieurausbildung; Elektronisches Publizieren; Book; Buch; Monographie; Monografie; Semantik; Anschauungsmaterial; Mathematik; Education; Access; Bildung; Zugang; Bildungszugang; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Textgestaltung; Hearing impairment; Hörbehinderung; Gehörlosigkeit; Taubstummheit; Classification system; Klassifikation; Klassifikationssystem; Künstliche Intelligenz
Abstract	To efficiently create books and other instructional content from videos and further improve accessibility of our course content we needed to solve the scene detection (SD) problem for engineering educational content. We present the pedagogical applications of extracting video images for the purposes of digital book generation and other shareable resources, within the themes of accessibility, inclusive education, universal design for learning and how we solved this problem for engineering education lecture videos. Scene detection refers to the process of merging visually similar frames into a single video segment, and subsequent extraction of semantic features from the video segment (e.g., title, words, transcription segment and representative image). In our approach, local features were extracted from inter-frame similarity comparisons using multiple metrics. These include numerical measures based on optical character recognition (OCR) and pixel similarity with and without face and body position masking. We analyze and discuss the trade-offs in accuracy, performance and computational resources required. By applying these features to a corpus of labeled videos, a support vector machine determined an optimal parametric decision surface to model if adjacent frames were semantically and visually similar or not. The algorithm design, data flow, and system accuracy and performance are presented. We evaluated our system using videos from multiple engineering disciplines where the content was comprised of different presentation styles including traditional paper handouts, Microsoft PowerPoint slides, and digital ink annotations. For each educational video, a comprehensive digital-book composed of lecture clips, slideshow text, and audio transcription content can be generated based on our new scene detection algorithm. Our new scene detection approach was adopted by ClassTranscribe, an inclusive video platform that follows Universal Design for Learning principles. We report on the subsequent experiences and feedback from students who reviewed the generated digital-books as a learning component. We highlight remaining challenges and describe how instructors can use this technology in their own courses. The main contributions of this work are: Identifying why automated scene detection of engineering lecture videos is challenging; Creation of a scene-labeled corpus of videos representative of multiple undergraduate engineering disciplines and lecture styles suitable for training and testing; Description of a set of image metrics and support vector machine-based classification approach; Evaluation of the accuracy, recall and precision of our algorithm; Use of an algorithmic optimization to obviate GPU resources; Student commentary on the digital book interface created from videos using our SD algorithm; Publishing of a labeled corpus of video content to encourage additional research in this area; and an independent open-source scene extraction tool that can be used pedagogically by the ASEE community e.g., to remix and create fun shareable instructional content memes, and to create accessible audio and text descriptions for students who are blind or have low vision. Text extracted from each scene can also used to improve the accuracy of captions and transcripts, improving accessibility for students who are hard of hearing or deaf. (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01