Presentation Trainer: what experts and computers can tell about your nonverbal communication

Jan Schneider*, Dirk Börner, P. van Rosmalen, M. Specht

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

The ability to present effectively is essential for professionals; therefore, oral communication courses have become part of the curricula for higher education studies. However, speaking in public is still a challenge for many graduates. To tackle this problem, driven by the recent advances in computer vision techniques and prosody analysis, multimodal tools have been designed to support the development of public speaking skills. One of these tools is the Presentation Trainer, a research prototype able to provide learners with real-time feedback on a set of nonverbal communication aspects. Despite initial positive evaluations, the application still lacks grounding in a valid assessment model for nonverbal communication aspects in the context of presentations. To come up with such a model, we conducted semi-structured inter- views with experts in the public speaking domain. Furthermore, the objective of these interviews was also to have a formative evaluation of the Presentation Trainer, analysing how it suits with common practices for teaching and learning public speaking skills. The results of this study iden- tify 131 nonverbal communication practices that affect the quality of a presentation and summa- rize experts’ points of view regarding multimodal public speaker instructors.
Original languageEnglish
Pages (from-to)164-177
Number of pages14
JournalJournal of Computer Assisted Learning
Volume33
Issue number2
DOIs
Publication statusPublished - Apr 2017

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non-verbal communication
speaking
expert
Communication
Electric grounding
interview
evaluation
Curricula
Computer vision
instructor
Teaching
Education
graduate
Feedback
curriculum
communication
lack
ability
learning
education

Keywords

  • expert study
  • multimodal systems
  • nonverbal communication
  • public speaking
  • sensor-based learning.

Cite this

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title = "Presentation Trainer: what experts and computers can tell about your nonverbal communication",
abstract = "The ability to present effectively is essential for professionals; therefore, oral communication courses have become part of the curricula for higher education studies. However, speaking in public is still a challenge for many graduates. To tackle this problem, driven by the recent advances in computer vision techniques and prosody analysis, multimodal tools have been designed to support the development of public speaking skills. One of these tools is the Presentation Trainer, a research prototype able to provide learners with real-time feedback on a set of nonverbal communication aspects. Despite initial positive evaluations, the application still lacks grounding in a valid assessment model for nonverbal communication aspects in the context of presentations. To come up with such a model, we conducted semi-structured inter- views with experts in the public speaking domain. Furthermore, the objective of these interviews was also to have a formative evaluation of the Presentation Trainer, analysing how it suits with common practices for teaching and learning public speaking skills. The results of this study iden- tify 131 nonverbal communication practices that affect the quality of a presentation and summa- rize experts’ points of view regarding multimodal public speaker instructors.",
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author = "Jan Schneider and Dirk B{\"o}rner and {van Rosmalen}, P. and M. Specht",
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Presentation Trainer : what experts and computers can tell about your nonverbal communication. / Schneider, Jan; Börner, Dirk; van Rosmalen, P.; Specht, M.

In: Journal of Computer Assisted Learning, Vol. 33, No. 2, 04.2017, p. 164-177.

Research output: Contribution to journalArticleAcademicpeer-review

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N1 - DS_Citation:Schneider, J., Börner, D., Van Rosmalen, P., & Specht, M. (2017). Presentation Trainer: what experts and computers can tell about your nonverbal communication. Journal of Computer Assisted Learning, 33(2), 164-177.

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