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Real-Time Robotic Presentation Skill Scoring Using Multi-Model Analysis and Fuzzy Delphi-Analytic Hierarchy Process.

Rafeef Fauzi Najim Alshammari1,2, Abdul Hadi Abd Rahman1, Haslina Arshad1

  • 1Center for Artificial Intelligence Technology, Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia.

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Summary
This summary is machine-generated.

This study introduces Robotics-based Presentation Skill Scoring (RPSS), a novel robotic multi-classification model for evaluating student presentations. RPSS accurately assesses facial expressions, eye contact, and movements, achieving 99% agreement with human tutors.

Keywords:
AHPeducational robotevaluationfuzzy Delphihuman–robot interactionmachine learningpresentation scoring

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Education Technology

Background:

  • Current student presentation scoring methods lack multi-classification and active learning, leading to potential inaccuracies.
  • Existing systems often focus narrowly on facial expressions, neglecting other critical presentation skills like eye contact and body language.

Purpose of the Study:

  • To develop and evaluate a Robotics-based Presentation Skill Scoring (RPSS) system for comprehensive and accurate assessment.
  • To integrate multiple analytical models for real-time evaluation of key presentation parameters.

Main Methods:

  • RPSS utilizes a multi-model approach analyzing facial expressions (EfficientNet), eye contact (DeepEC), and movements (Kalman/heuristic).
  • The fuzzy Delphi method and analytic hierarchy process were used for criteria selection and weighting.
  • A novel active learning approach enhanced performance through robot mobility.

Main Results:

  • RPSS demonstrated practical applicability through real-world evaluations.
  • Offline and comparative evaluations showed high accuracy and effectiveness.
  • Achieved 99% average agreement with human tutors in assessing presentation skills.

Conclusions:

  • RPSS offers a robust and accurate solution for scoring student presentations, overcoming limitations of existing methods.
  • The system's multi-parameter analysis and robotic implementation enhance scoring objectivity and comprehensiveness.