Authors:
Federico Domínguez
1
;
2
;
Leonardo Eras
1
;
Josué Tomalá
2
and
Adriana Collaguazo
2
Affiliations:
1
Information Technology Center, Escuela Superior Politécnica Del Litoral, ESPOL, Guayaquil, Ecuador
;
2
Faculty of Electrical and Computer Engineering, Escuela Superior Politécnica Del Litoral, ESPOL, Guayaquil, Ecuador
Keyword(s):
Oral Presentation Skills, Human Pose Identification, Feedforward Neural Network, Automatic Presentation Feedback.
Abstract:
Mastering oral presentation skills is of paramount importance for new graduates as they navigate the competitive job market of the 21st century. Consequently, procuring the effective development of these skills in students is an essential task for higher education institutions (HEIs). We developed a technological solution that facilitates oral presentation skills learning by providing automatic and immediate feedback using machine learning algorithms on audiovisual recordings of oral presentations. We have been using this tool to record novice students' presentations since 2017 and, by using the resulting data corpus, developed a methodology to accurately detect and evaluate posture and gaze in oral presentations. This article presents this methodology and its application on more than 3,000 recordings from more than 2,000 different students across all study programs at our university. Preliminary results provide a glimpse of the prevalence and distribution of oral presentation skills
across several demographic variables. Statistically significant patterns point to possible oral communication deficiencies in engineering programs at our HEI, highlighting the potential of our methodology to serve as a diagnostic tool for communication skills learning strategies.
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