Optimization of AI-Based Sentiment Analysis Models for Student Feedback in Kogi State University, Anyigba, Kogi State
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- NGN 5000
Background of the Study
Student feedback is essential for improving the quality of education and understanding the learning environment at universities. Traditionally, student feedback has been collected through surveys and questionnaires, which are often manually analyzed, leading to inefficiencies and delayed actions. AI-powered sentiment analysis models offer a solution by automating the analysis of student feedback and identifying key themes, issues, and sentiments expressed by students. These models can process large volumes of feedback data in real-time, providing educators and administrators with immediate insights into student satisfaction, concerns, and suggestions. This study focuses on the optimization of AI-based sentiment analysis models to analyze student feedback at Kogi State University, Anyigba, Kogi State, aiming to enhance the responsiveness of university management to student needs.
Statement of the Problem
At Kogi State University, collecting and analyzing student feedback is a crucial part of enhancing educational quality. However, manual analysis of large-scale feedback data can be time-consuming and inefficient, often resulting in delayed action and missed opportunities for improvement. Traditional methods of feedback analysis are limited in their ability to identify patterns in unstructured text data, such as student opinions and sentiments. An AI-based sentiment analysis model can automate this process and provide real-time insights into student feedback, enabling university administrators to address concerns more quickly and effectively. This study seeks to optimize sentiment analysis models for better accuracy, scalability, and usability in the context of student feedback analysis.
Objectives of the Study
1. To develop and optimize an AI-based sentiment analysis model for analyzing student feedback at Kogi State University.
2. To evaluate the effectiveness of the optimized sentiment analysis model in identifying key themes and sentiments in student feedback.
3. To assess the impact of AI-driven insights on the decision-making process and response times of university administration.
Research Questions
1. How effective is the AI-based sentiment analysis model in identifying key themes and sentiments from student feedback at Kogi State University?
2. What improvements can be made to optimize the accuracy and efficiency of the sentiment analysis model?
3. How does the AI-based sentiment analysis model influence the decision-making process and responsiveness of university administrators?
Research Hypotheses
1. The AI-based sentiment analysis model will provide more accurate and efficient insights into student feedback compared to traditional methods.
2. The optimized model will identify significant patterns and sentiments in student feedback, leading to actionable insights for university management.
3. The implementation of the AI-based model will improve the speed and quality of decision-making by university administrators in response to student feedback.
Significance of the Study
This study is significant as it will demonstrate the potential of AI in improving the efficiency and accuracy of sentiment analysis in educational settings. By optimizing the model, the university can gain more timely insights into student feedback, enabling faster and more effective improvements to the educational experience.
Scope and Limitations of the Study
The study will focus on optimizing the AI-based sentiment analysis model for student feedback at Kogi State University, Anyigba, Kogi State. Limitations include the availability of diverse and accurate feedback data, as well as the potential challenges in integrating the AI model with existing university systems.
Definitions of Terms
• AI-Based Sentiment Analysis: The use of artificial intelligence to analyze and interpret the emotional tone and sentiment expressed in text data, such as student feedback.
• Sentiment: The attitude, feelings, or opinions expressed in a piece of feedback, which can be positive, negative, or neutral.
• Feedback Analysis: The process of reviewing and interpreting student feedback to identify key insights and areas for improvement.
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