An Investigation of Sentiment Analysis on Student Feedback for Course Improvement in Federal University Lokoja, Kogi State
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Background of the Study
Student feedback is an invaluable resource for enhancing course content and delivery, yet traditional methods of gathering and analyzing this feedback are often subjective and time-consuming. At Federal University Lokoja, Kogi State, sentiment analysis—a technique that employs natural language processing (NLP) and machine learning to classify opinions—can provide a more objective measure of student satisfaction and course effectiveness (Oluwaseun, 2023). By processing large volumes of feedback data from surveys, social media, and online forums, sentiment analysis can identify common themes, positive sentiments, and areas of concern regarding course content and teaching methods. This data-driven approach enables academic administrators to make informed decisions about curriculum modifications and pedagogical improvements. The continuous monitoring of student sentiments offers a real-time mechanism for detecting shifts in perceptions, allowing timely interventions that enhance learning outcomes. Furthermore, advanced sentiment analysis models can differentiate between nuanced expressions of satisfaction and dissatisfaction, offering a more granular understanding of student opinions (Ibrahim, 2024). The integration of sentiment analysis into the course review process has the potential to foster a culture of continuous improvement and accountability in higher education. As universities strive to meet the evolving expectations of students, leveraging digital analytics becomes imperative. This study seeks to explore the effectiveness of sentiment analysis on student feedback as a tool for driving course improvement. It will assess the accuracy of various sentiment analysis models and evaluate their utility in capturing actionable insights that can inform strategic academic decisions (Chinwe, 2025).
Statement of the Problem
Traditional methods of analyzing student feedback at Federal University Lokoja are limited by their reliance on manual interpretation and subjective assessments, leading to delays in identifying critical issues and implementing improvements (Abdullahi, 2023). This inefficiency hampers the university’s ability to respond promptly to student concerns and continuously refine course content. Moreover, the volume of feedback generated through digital channels often overwhelms administrative systems, resulting in valuable insights being overlooked. The absence of automated sentiment analysis tools means that qualitative data is not systematically leveraged to drive course enhancement. As a result, courses may continue to suffer from persistent issues such as unclear instruction, outdated materials, and ineffective teaching methods. The lack of a comprehensive, data-driven feedback system further exacerbates these challenges, contributing to student dissatisfaction and suboptimal learning outcomes. This study aims to address these shortcomings by investigating the use of sentiment analysis techniques to process and interpret student feedback. By automating the evaluation of textual data, the study seeks to identify key sentiment trends and correlate them with specific course attributes. The goal is to provide academic administrators with actionable recommendations that can facilitate targeted course improvements and enhance overall educational quality. Additionally, the research will explore potential challenges associated with sentiment analysis, including the handling of sarcasm, context dependency, and language variations, and propose strategies to mitigate these issues.
Objectives of the Study:
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To develop a sentiment analysis framework for processing student feedback on courses.
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To evaluate the impact of sentiment-derived insights on course improvement.
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To propose recommendations for integrating automated sentiment analysis into academic review processes.
Research Questions:
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How accurately can sentiment analysis capture student opinions on course quality?
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What common themes and issues are identified through sentiment analysis of feedback?
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How can sentiment analysis inform targeted improvements in course design and delivery?
Significance of the Study
This study is significant as it harnesses sentiment analysis to transform student feedback into actionable insights for course improvement at Federal University Lokoja. The research offers a systematic, data-driven approach to understanding student perceptions, ultimately contributing to enhanced academic quality and teaching effectiveness. The findings will guide educators and administrators in implementing automated feedback systems that lead to continuous curricular enhancement (Oluwaseun, 2023).
Scope and Limitations of the Study:
The study is limited to the application of sentiment analysis on student feedback for course improvement at Federal University Lokoja, Kogi State, and does not extend to other aspects of academic evaluation or institutions.
Definitions of Terms:
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Sentiment Analysis: The process of using NLP and machine learning to classify and interpret opinions expressed in textual data.
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Student Feedback: Information provided by students regarding their learning experiences and course satisfaction.
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Course Improvement: The process of making systematic changes to enhance the quality and effectiveness of academic courses.
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