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OpenOPAF: An Open-Source Multimodal System for Automated Feedback for Oral Presentations

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Title: OpenOPAF: An Open-Source Multimodal System for Automated Feedback for Oral Presentations
Language: English
Authors: Xavier Ochoa (ORCID 0000-0002-4371-7701), Heru Zhao
Source: Journal of Learning Analytics. 2024 11(3):224-248.
Availability: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
Peer Reviewed: Y
Page Count: 25
Publication Date: 2024
Document Type: Journal Articles
Reports - Evaluative
Descriptors: Open Source Technology, Multimedia Materials, Automation, Feedback (Response), Speech Skills, Technology Uses in Education, Educational Technology, Communication Skills
ISSN: 1929-7750
Abstract: Providing automated feedback that facilitates the practice and acquisition of oral presentation skills has been one of the notable applications of multimodal learning analytics (MmLA). However, the closedness and general unavailability of existing systems have reduced their potential impact and benefits. This work introduces OpenOPAF, an open-source system designed to provide automated multimodal feedback for oral presentations. By leveraging analytics to assess body language, gaze direction, voice volume, articulation speed, filled pauses, and the use of text in visual aids, it provides real-time, actionable information to presenters. Evaluations conducted on OpenOPAF show that it performs similarly, both technically and pedagogically, to existing closed solutions. This system targets practitioners who wish to use it as-is to provide feedback to novice presenters, developers seeking to adapt it for other learning contexts, and researchers interested in experimenting with new feature extraction algorithms and report mechanisms and studying the acquisition of oral presentation skills. This initiative aims to foster a community-driven approach to democratize access to sophisticated analytics tools for oral presentation skill development.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1456262
Database: ERIC
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  Data: OpenOPAF: An Open-Source Multimodal System for Automated Feedback for Oral Presentations
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  Data: <searchLink fieldCode="AR" term="%22Xavier+Ochoa%22">Xavier Ochoa</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4371-7701">0000-0002-4371-7701</externalLink>)<br /><searchLink fieldCode="AR" term="%22Heru+Zhao%22">Heru Zhao</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Learning+Analytics%22"><i>Journal of Learning Analytics</i></searchLink>. 2024 11(3):224-248.
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  Data: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
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  Data: Providing automated feedback that facilitates the practice and acquisition of oral presentation skills has been one of the notable applications of multimodal learning analytics (MmLA). However, the closedness and general unavailability of existing systems have reduced their potential impact and benefits. This work introduces OpenOPAF, an open-source system designed to provide automated multimodal feedback for oral presentations. By leveraging analytics to assess body language, gaze direction, voice volume, articulation speed, filled pauses, and the use of text in visual aids, it provides real-time, actionable information to presenters. Evaluations conducted on OpenOPAF show that it performs similarly, both technically and pedagogically, to existing closed solutions. This system targets practitioners who wish to use it as-is to provide feedback to novice presenters, developers seeking to adapt it for other learning contexts, and researchers interested in experimenting with new feature extraction algorithms and report mechanisms and studying the acquisition of oral presentation skills. This initiative aims to foster a community-driven approach to democratize access to sophisticated analytics tools for oral presentation skill development.
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        Type: general
      – SubjectFull: Multimedia Materials
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      – SubjectFull: Automation
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      – SubjectFull: Feedback (Response)
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              Y: 2024
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