Estimation of presentations skills based on slides and audio features

Gonzalo Luzardo, Bruno Guamán, Katherine Chiluiza, Jaime Castells, Xavier Ochoa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a simple estimation of the quality of student oral presentations. It is based on the study and analysis of features extracted from the audio and digital slides of 448 presentations. The main goal of this work is to automatically predict the values assigned by professors to different criteria in a presentation evaluation rubric. Ma- chine Learning methods were used to create several models that classify students in two clusters: high and low perform- ers. The models created from slide features were accurate up to 65%. The most relevant features for the slide-base models were: number of words, images, and tables, and the maximum font size. The audio-based models reached up to 69% of accuracy, with pitch and filled pauses related features being the most significant. The relatively high degrees of ac- curacy obtained with these very simple features encourage the development of automatic estimation tools for improving presentation skills.

Original languageEnglish (US)
Title of host publicationMLA 2014 - Proceedings of the 2014 ACM Multimodal Learning Analytics Workshop and Grand Challenge, Co-located with ICMI 2014
PublisherAssociation for Computing Machinery, Inc
Pages37-44
Number of pages8
ISBN (Electronic)9781450304887
DOIs
StatePublished - Nov 12 2014
Event3rd Multimodal Learning Analytics Workshop and Grand Challenges, MLA 2014 - Istanbul, Turkey
Duration: Nov 12 2014Nov 12 2014

Publication series

NameMLA 2014 - Proceedings of the 2014 ACM Multimodal Learning Analytics Workshop and Grand Challenge, Co-located with ICMI 2014

Other

Other3rd Multimodal Learning Analytics Workshop and Grand Challenges, MLA 2014
CountryTurkey
CityIstanbul
Period11/12/1411/12/14

Keywords

  • Audio features
  • Multimodal Learning Analytics
  • Presentation skills
  • Slides features

ASJC Scopus subject areas

  • Computer Science Applications
  • Education

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