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A Real-Time Learning Analytics Dashboard for Automatic Detection of Online Learners' Affective States
Mohammad Nehal Hasnine1, Ho Tan Nguyen1, Thuy Thi Thu Tran1
1Research Center for Computing and Multimedia Studies, Hosei University, Tokyo 102-8160, Japan.
Sensors (Basel, Switzerland)
|May 13, 2023
Summary
This study introduces MOEMO, a novel system for real-time emotion detection in online learners. It measures engagement and concentration, offering lecturers new insights into student affective states.
Area of Science:
- Educational Technology
- Learning Analytics
- Affective Computing
Background:
- Student affective states significantly impact online learning quality.
- Traditional methods for assessing affective states lack real-time interaction and rely on self-reports.
- Lecturers face challenges in monitoring student engagement and concentration in virtual environments.
Purpose of the Study:
- To introduce MOEMO (Motion and Emotion), a novel learning analytics system.
- To automatically detect and visualize online learners' affective states, specifically engagement and concentration.
- To provide lecturers with real-time insights into student emotional states for timely intervention.
Main Methods:
- Utilizing a learning analytics system (MOEMO) that analyzes facial features from webcam-captured lecture videos.
- Employing an automated emotion detection process to extract emotion data in real-time and offline.
- Classifying learners into five engagement levels (strong, high, medium, low, disengagement) and two concentration levels (focused, distracted).
Main Results:
- MOEMO successfully measures online learners' engagement and concentration levels using emotion data.
- The system visualizes affective states on lecturers' screens in real-time.
- A dashboard provides insights into student emotional states, engagement clusters, and facilitates intervention.
Conclusions:
- MOEMO offers a novel, automated approach to understanding student affective states in online learning.
- Real-time emotion detection enhances lecturers' ability to monitor and support student learning.
- The system's dashboard aids in personalized interventions and provides comprehensive post-class reports.
Keywords:
AI in educationaffective states detectiondashboardemotionlearning analytics frameworklecture video analysisMore Related Videos
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