Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Emotion-Aware Classroom Quality Assessment Leveraging IoT-Based Real-Time Student Monitoring

Hai Nguyen, Hieu Dao, Hung Nguyen, Nam Vu, Cong Tran

Abstract

This study presents high-throughput, real-time multi-agent affective computing framework designed to enhance classroom learning through emotional state monitoring. As large classroom sizes and limited teacher student interaction increasingly challenge educators, there is a growing need for scalable, data-driven tools capable of capturing students' emotional and engagement patterns in real time. The system was evaluated using the Classroom Emotion Dataset, consisting of 1,500 labeled images and 300 classroom detection videos. Tailored for IoT devices, the system addresses load balancing and latency challenges through efficient real-time processing. Field testing was conducted across three educational institutions in a large metropolitan area: a primary school (hereafter school A), a secondary school (school B), and a high school (school C). The system demonstrated robust performance, detecting up to 50 faces at 25 FPS and achieving 88% overall accuracy in classifying classroom engagement states. Implementation results showed positive outcomes, with favorable feedback from students, teachers, and parents regarding improved classroom interaction and teaching adaptation. Key contributions of this research include establishing a practical, IoT-based framework for emotion-aware learning environments and introducing the 'Classroom Emotion Dataset' to facilitate further validation and research.

Emotion-Aware Classroom Quality Assessment Leveraging IoT-Based Real-Time Student Monitoring

Abstract

This study presents high-throughput, real-time multi-agent affective computing framework designed to enhance classroom learning through emotional state monitoring. As large classroom sizes and limited teacher student interaction increasingly challenge educators, there is a growing need for scalable, data-driven tools capable of capturing students' emotional and engagement patterns in real time. The system was evaluated using the Classroom Emotion Dataset, consisting of 1,500 labeled images and 300 classroom detection videos. Tailored for IoT devices, the system addresses load balancing and latency challenges through efficient real-time processing. Field testing was conducted across three educational institutions in a large metropolitan area: a primary school (hereafter school A), a secondary school (school B), and a high school (school C). The system demonstrated robust performance, detecting up to 50 faces at 25 FPS and achieving 88% overall accuracy in classifying classroom engagement states. Implementation results showed positive outcomes, with favorable feedback from students, teachers, and parents regarding improved classroom interaction and teaching adaptation. Key contributions of this research include establishing a practical, IoT-based framework for emotion-aware learning environments and introducing the 'Classroom Emotion Dataset' to facilitate further validation and research.
Paper Structure (50 sections, 9 equations, 9 figures, 6 tables, 1 algorithm)

This paper contains 50 sections, 9 equations, 9 figures, 6 tables, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. METHODOLOGY
  4. Dataset creation pipeline
  5. Data collection
  6. Data processing
  7. Data annotation
  8. Annotation team
  9. Classroom quality model validation
  10. Analysis of emotional distribution by engagement category
  11. AI service layer
  12. Overview of the AI service layer
  13. Face detection
  14. Facial expression recognition
  15. Classroom quality assessment
  16. ...and 35 more sections

Figures (9)

  • Figure 1: Main processing flow
  • Figure 2: Demographic and educational composition of the Classroom Emotion Dataset, illustrating age-group and school-level representation across participating institutions.
  • Figure 3: Dataset creation pipeline for emotion recognition in classroom environments
  • Figure 4: Emotional distribution across different engagement categories
  • Figure 5: Real-world application of emotion recognition technology
  • ...and 4 more figures