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Area of Science:

  • Educational Technology
  • Human-Computer Interaction
  • Speech Analysis

Background:

  • Public speaking is crucial for academic and professional success, posing evaluation challenges for higher education.
  • Objective assessment and feedback generation for student presentations remain difficult.
  • Developing effective evaluation methods for oral presentations is essential.

Purpose of the Study:

  • To understand and detect patterns in oral student presentations.
  • To identify objective features for evaluating presentation skills.
  • To provide a foundation for automated feedback systems.

Main Methods:

  • Collected data from 222 Computer Engineering students over two years (2017-2018).
  • Utilized a developed system with Microsoft Kinect to capture 12 features of corporal posture and speech.
  • Applied clustering and statistical analysis (Wilcoxon rank-sum test) to the collected data.

Main Results:

  • Identified three distinct clusters of presentation patterns across both years.
  • Observed stronger, more defined patterns in 2017 presentations.
  • Noted a convergence in presentation attributes over time, with fewer statistically different features between presentations within the same course time.

Conclusions:

  • The study successfully identified patterns in student oral presentations using objective data.
  • Results suggest the potential for automated feedback systems to improve student presentation skills.
  • Findings provide a baseline for future comparative studies across different academic disciplines.