Evaluation of AI-Based Facial Expression Recognition for Student Emotional State Analysis in Nasarawa LGA, Nasarawa State
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- NGN 5000
Background of the Study
Emotional states play a crucial role in students’ academic performance and overall well-being. Understanding students' emotional states can help educators to provide better support, identify at-risk students, and improve the learning environment. Traditional methods for assessing student emotions, such as surveys or teacher observations, can be subjective and inaccurate. AI-based facial expression recognition systems offer an opportunity to monitor and analyze students' emotional states in real-time, providing a more objective and efficient approach. This study aims to evaluate the use of AI-based facial expression recognition systems to assess the emotional states of students in Nasarawa LGA, Nasarawa State.
Statement of the Problem
The traditional methods of assessing student emotional states can be unreliable and time-consuming, and they may not accurately reflect the emotions of students in the classroom. This study will address the gap by implementing AI-based facial expression recognition to monitor students’ emotions and explore the potential benefits for educators in understanding students’ emotional needs.
Objectives of the Study
1. To implement an AI-based facial expression recognition system to assess students' emotional states in real-time.
2. To evaluate the accuracy and reliability of the AI system in identifying different emotional states.
3. To assess the impact of AI-based emotional state analysis on student engagement and academic performance.
Research Questions
1. How accurate is the AI-based facial expression recognition system in identifying students’ emotional states?
2. How can emotional state analysis inform teaching strategies and student support services?
3. What is the impact of understanding emotional states on student engagement and academic performance?
Research Hypotheses
1. The AI-based facial expression recognition system will accurately identify students’ emotional states with a high degree of accuracy.
2. Understanding students' emotional states through AI-based systems will positively influence student engagement and performance.
3. The use of AI in emotional state analysis will help identify at-risk students and enable timely intervention.
Significance of the Study
This study will help to establish the potential of AI-based facial expression recognition for enhancing emotional state analysis in educational settings. It will provide insights into the role of emotional intelligence in student performance and offer new ways for educators to better support students' needs.
Scope and Limitations of the Study
The study will focus on the implementation and evaluation of the AI-based facial expression recognition system for students in Nasarawa LGA. Limitations include potential challenges in ensuring accuracy due to environmental factors (e.g., lighting), the need for appropriate hardware, and privacy concerns regarding emotional data.
Definitions of Terms
• AI-Based Facial Expression Recognition: A technology that uses AI to detect and analyze human facial expressions to determine emotional states.
• Emotional State: The mood or feelings a student experiences, which may impact their learning, motivation, and engagement.
• Student Engagement: The level of involvement and participation of students in the learning process.
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