Exploring AI-Powered Sentiment Analysis in Online Student Feedback Systems in Federal University, Gusau, Zamfara State
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- NGN 5000
Background of the study
Student feedback plays a crucial role in enhancing the quality of education by providing valuable insights into the effectiveness of teaching methods, course content, and overall campus experience. Traditionally, feedback collection has relied on surveys and manual evaluations, which may have limited scope or subjectivity in interpretation. The introduction of artificial intelligence (AI)-powered sentiment analysis provides an innovative approach to processing large volumes of student feedback quickly and accurately. By analyzing the sentiment behind students' responses, AI can identify patterns in student satisfaction, concerns, and emotional engagement with courses or campus life. At Federal University, Gusau, Zamfara State, the integration of AI-powered sentiment analysis into online student feedback systems could revolutionize how feedback is interpreted and acted upon. This study explores the use of AI in analyzing student feedback to improve teaching strategies and student experiences.
Statement of the problem
At Federal University, Gusau, the process of collecting and analyzing student feedback often faces challenges such as low response rates, subjectivity in interpretation, and delayed feedback analysis. As a result, the institution may struggle to make timely improvements based on student input. While traditional methods of feedback analysis provide insights, they often fail to capture the emotional tone or subtle nuances in students' comments. AI-powered sentiment analysis, which can process and analyze text data in real-time, holds the potential to address these challenges. However, the effectiveness and feasibility of implementing such systems in the university’s online feedback platforms remain underexplored. This study seeks to investigate the application of sentiment analysis in student feedback systems at Federal University, Gusau, and its potential impact on enhancing educational outcomes.
Objectives of the study
1. To explore the effectiveness of AI-powered sentiment analysis in analyzing online student feedback at Federal University, Gusau.
2. To assess the impact of sentiment analysis on identifying key areas for improvement in teaching and student services.
3. To evaluate student satisfaction with AI-powered sentiment analysis-based feedback systems.
Research questions
1. How effective is AI-powered sentiment analysis in processing and interpreting online student feedback at Federal University, Gusau?
2. What key areas for improvement can be identified through sentiment analysis of student feedback?
3. What is the level of student satisfaction with the use of AI-powered sentiment analysis in feedback systems?
Research hypotheses
1. AI-powered sentiment analysis will provide more accurate and actionable insights from student feedback than traditional methods.
2. AI-based sentiment analysis will identify teaching and service areas that require improvement at Federal University, Gusau.
3. Students will express higher satisfaction with an AI-powered feedback system compared to traditional feedback systems.
Significance of the study
This study will contribute to the understanding of how AI can be leveraged to improve student feedback mechanisms, providing actionable insights for academic and administrative improvements. The findings could guide Federal University, Gusau, and other institutions in adopting AI-powered systems to enhance student engagement and academic quality.
Scope and limitations of the study
The study will focus on the implementation and evaluation of AI-powered sentiment analysis in online student feedback systems at Federal University, Gusau, Zamfara State. Limitations may include the availability of sufficient data, student willingness to participate, and potential technological barriers in implementing AI tools.
Definitions of terms
• Sentiment Analysis: The use of natural language processing (NLP) and AI techniques to analyze and interpret the emotional tone of textual data.
• Online Student Feedback System: A digital platform that collects students' opinions and feedback regarding their educational experiences.
• AI-Powered: Refers to the use of artificial intelligence to enhance or automate processes, such as analyzing data and providing insights.
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