Investigation into AI-Based Emotion Recognition for Student Engagement Analysis in Abubakar Tafawa Balewa University, Bauchi, Bauchi State
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- NGN 5000
Background of the Study
Student engagement is a key factor in determining academic success and enhancing the overall learning experience. While traditional metrics such as attendance and grades are often used to measure student engagement, they do not capture the emotional states that contribute significantly to students' involvement in their learning. Emotion recognition through AI has emerged as a potential tool for assessing students' emotional states in real-time, thus offering a more comprehensive approach to evaluating engagement. Abubakar Tafawa Balewa University, Bauchi, can benefit from the integration of AI-based emotion recognition systems in classroom settings. These systems use facial expression analysis, voice modulation, and body language detection to identify emotions such as excitement, frustration, or confusion. By leveraging these emotional cues, instructors can adjust their teaching strategies to improve student engagement and learning outcomes. This study explores the role of AI-based emotion recognition systems in assessing and enhancing student engagement at Abubakar Tafawa Balewa University.
Statement of the Problem
At Abubakar Tafawa Balewa University, Bauchi, traditional methods of evaluating student engagement fail to capture the emotional and psychological factors that influence academic participation. Existing techniques mainly focus on observable behaviors like attendance, which do not offer a full picture of student engagement. The lack of real-time feedback on students' emotional states means instructors may miss key opportunities to modify teaching approaches to better suit students’ needs. The introduction of AI-based emotion recognition systems could address this gap by providing instructors with real-time insights into students’ emotional responses, thereby allowing them to improve engagement and participation. This research investigates the potential of AI-based emotion recognition for analyzing student engagement at the university.
Objectives of the Study
1. To design and implement an AI-based emotion recognition system to analyze student engagement at Abubakar Tafawa Balewa University, Bauchi.
2. To evaluate the effectiveness of the AI-based emotion recognition system in identifying student engagement levels.
3. To assess how emotion recognition data can help instructors adjust teaching methods for enhanced student engagement.
Research Questions
1. How effective is the AI-based emotion recognition system in identifying students’ emotional states in the classroom?
2. How does real-time emotion recognition data impact student engagement and participation in class?
3. What are the perceptions of instructors and students regarding the use of AI-based emotion recognition systems for engagement analysis?
Research Hypotheses
1. The AI-based emotion recognition system significantly improves the accuracy of student engagement analysis compared to traditional methods.
2. Students whose emotional states are monitored by the AI system will demonstrate higher levels of engagement and participation in class.
3. Instructors using AI-based emotion recognition data will adapt their teaching strategies to enhance student engagement.
Significance of the Study
This study provides a novel approach to enhancing student engagement through AI-based emotion recognition, which can lead to more dynamic and responsive teaching methods. By identifying students' emotional states in real-time, the system can improve learning outcomes and academic success at Abubakar Tafawa Balewa University, Bauchi.
Scope and Limitations of the Study
The study will focus on implementing AI-based emotion recognition in classroom settings at Abubakar Tafawa Balewa University, Bauchi. Limitations include potential challenges with the accuracy of emotion detection in diverse classroom environments and resistance from students and instructors in adopting AI technology.
Definitions of Terms
• AI-Based Emotion Recognition: A system that uses artificial intelligence to analyze and identify students' emotions based on facial expressions, voice tone, and body language.
• Student Engagement: The level of interest, participation, and emotional investment that students exhibit in their academic activities.
• Real-Time Feedback: Immediate insights provided by the system based on the analysis of students’ emotional states during class.
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