Analysis of AI-Based Sentiment Analysis in University Course Evaluations in Federal University, Lokoja, Kogi State
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- NGN 5000
Background of the Study
Course evaluations are an essential tool for assessing the quality of teaching, the effectiveness of course content, and the overall student experience. Traditionally, course evaluations are conducted through surveys, where students provide feedback on various aspects of the course, including the teaching style, materials, and learning outcomes. However, analyzing large volumes of textual feedback can be time-consuming and subjective, often limiting the ability to extract meaningful insights.
Artificial Intelligence (AI) offers a powerful solution in the form of sentiment analysis, which can automatically analyze and interpret the emotions and opinions expressed in students' feedback. By using AI-driven sentiment analysis, universities can quickly assess the overall sentiment of students towards a course and identify areas of improvement. In Federal University, Lokoja, Kogi State, the current process of evaluating course feedback is labor-intensive, and the insights gained are not always actionable. This study aims to explore the use of AI-based sentiment analysis to improve the analysis of course evaluations and enhance decision-making for course improvement.
Statement of the Problem
The traditional methods of analyzing course evaluations at Federal University, Lokoja, are limited in their ability to extract actionable insights from large volumes of textual feedback. Manual analysis can be time-consuming, prone to bias, and unable to detect subtle sentiments within the data. AI-based sentiment analysis offers a more efficient and objective approach, yet its potential in the context of course evaluation has not been fully explored. This study seeks to assess the feasibility and effectiveness of implementing AI-driven sentiment analysis to improve the analysis and interpretation of course evaluation data.
Objectives of the Study
1. To develop and implement an AI-based sentiment analysis system for analyzing course evaluation feedback at Federal University, Lokoja.
2. To assess the accuracy and effectiveness of the AI-based sentiment analysis system in identifying key sentiments and themes in course evaluations.
3. To evaluate the impact of AI-based sentiment analysis on improving decision-making for course improvement and academic quality.
Research Questions
1. How effective is the AI-based sentiment analysis system in accurately identifying key sentiments in course evaluations?
2. What impact does AI-based sentiment analysis have on the decision-making process for course improvement and academic quality at Federal University, Lokoja?
3. What challenges and opportunities exist in implementing AI-based sentiment analysis for course evaluations in higher education?
Research Hypotheses
1. The AI-based sentiment analysis system accurately identifies key sentiments and themes in course evaluation data compared to traditional methods.
2. The use of AI-based sentiment analysis improves decision-making processes for course improvement and academic quality.
3. The implementation of AI-based sentiment analysis faces challenges such as data privacy concerns, system integration, and the interpretation of complex sentiments.
Significance of the Study
This study will enhance the way course evaluations are analyzed, providing university administrators and lecturers with deeper insights into student feedback. By adopting AI-driven sentiment analysis, Federal University, Lokoja can streamline the evaluation process and improve the quality of teaching and learning.
Scope and Limitations of the Study
The study will focus on the development and implementation of an AI-based sentiment analysis system for course evaluations at Federal University, Lokoja, Kogi State. Limitations include potential biases in the training data, challenges in interpreting complex feedback, and issues related to data privacy and security.
Definitions of Terms
• AI-Based Sentiment Analysis: The use of AI algorithms to analyze and classify the sentiment expressed in textual data, such as course evaluations.
• Sentiment: The emotional tone or opinion expressed in a piece of text, which can be classified as positive, negative, or neutral.
• Course Evaluation: A process through which students provide feedback on their learning experience, including assessments of teaching effectiveness, course content, and overall satisfaction.
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