Insights on the Distribution of Nonverbal and Verbal Oral Presentation Skills in an Educational Institution

Developing effective oral presentation skills is crucial for new graduates in today’s competitive job market. 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 basic 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 since 2017 to measure and aid learning of verbal, nonverbal, and visual oral presentation skills at our university and, by using the resulting data corpus, developed a methodology to accurately detect and evaluate two key nonverbal features of an oral presentation: posture and gaze. This article presents this methodology and shares the insights gleaned by comparing it with verbal features (filled pauses and voice volume) from more than 2000 different students across all study programs at our university. Preliminary results provide a glimpse of the prevalence and distribution of verbal and nonverbal oral presentation skills across several demographic variables. Statistically significant patterns point to possible differences between genders in verbal communication skills and nonverbal 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.