Analysis of Student Sentiment on Learning Management Systems Using Big Data in Federal University Dutsin-Ma, Katsina State
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Background of the Study
Student sentiment analysis has emerged as a vital tool for understanding the effectiveness of digital learning platforms. At Federal University Dutsin-Ma, Katsina State, big data analytics is employed to gauge student perceptions regarding the use of learning management systems (LMS). By mining data from discussion forums, social media, feedback surveys, and system logs, researchers can identify trends and sentiments that provide insights into user satisfaction, engagement, and areas requiring improvement (Abdullahi, 2023). The integration of big data methodologies allows for a comprehensive evaluation of the LMS, going beyond mere performance metrics to include qualitative aspects such as user experience and emotional responses.
The analysis of student sentiment is crucial in optimizing LMS platforms to better cater to the needs of diverse student populations. Recent advancements in sentiment analysis tools have enabled the extraction of nuanced insights from unstructured data, facilitating real-time feedback that can inform system upgrades and instructional strategies (Bello, 2024). This approach not only supports continuous improvement in digital learning environments but also empowers institutions to make data-driven decisions that enhance overall educational outcomes. In addition, understanding student sentiment is key to identifying potential issues such as system usability problems, content relevance, and technical difficulties that may adversely affect the learning experience (Chukwu, 2025).
Furthermore, the application of big data in sentiment analysis represents a shift toward more holistic evaluations of educational technologies. By combining quantitative data with qualitative feedback, institutions can develop a balanced view of the effectiveness of their LMS. This comprehensive perspective is essential for fostering an inclusive and responsive digital learning environment that supports both academic success and student well-being. Consequently, this study aims to critically analyze the sentiment expressed by students regarding their LMS experience at Federal University Dutsin-Ma, identifying key factors that influence user satisfaction and providing recommendations for system improvement (Abdullahi, 2023).
Statement of the Problem
Despite the growing adoption of learning management systems, Federal University Dutsin-Ma faces challenges in fully understanding and addressing student sentiment regarding the LMS. One of the key issues is the fragmented nature of feedback, which is dispersed across various channels and formats, making it difficult to obtain a comprehensive view of user experiences (Abdullahi, 2023). Inconsistent data collection methods and the lack of standardized sentiment analysis frameworks further complicate the assessment of student satisfaction. Additionally, while quantitative metrics such as login frequency and assignment submission rates provide some insight, they fail to capture the emotional and qualitative dimensions of the student experience (Bello, 2024).
There is also a significant gap in the ability of current analytical tools to process and interpret unstructured data accurately. This limitation often leads to oversimplified conclusions that do not fully reflect the complexities of student sentiment. Moreover, issues related to data privacy and the ethical use of student feedback data have raised concerns among stakeholders, potentially inhibiting the willingness of students to share their genuine opinions (Chukwu, 2025). The lack of a unified platform to analyze sentiment data in real time hampers the university’s capacity to respond swiftly to emerging issues. This study seeks to address these challenges by employing advanced big data analytics and sentiment analysis techniques to provide a detailed and nuanced understanding of student perceptions of the LMS, thereby informing targeted improvements in system design and support mechanisms (Abdullahi, 2023).
Objectives of the Study:
- To analyze student sentiment on the learning management system using big data analytics at Federal University Dutsin-Ma.
- To identify key factors that influence student satisfaction and engagement with the LMS.
- To propose recommendations for enhancing the user experience based on sentiment analysis findings.
Research Questions:
- What are the predominant sentiments expressed by students regarding the LMS at Federal University Dutsin-Ma?
- Which factors most significantly affect student satisfaction with the digital learning platform?
- How can sentiment analysis data be used to improve the LMS and overall student engagement?
Significance of the Study
This study is significant as it employs big data analytics to assess student sentiment regarding the learning management system at Federal University Dutsin-Ma. By uncovering detailed insights into user experiences, the research provides valuable guidance for optimizing the LMS to better meet student needs. The findings will aid administrators and IT developers in implementing targeted improvements, ultimately enhancing user satisfaction, engagement, and overall educational outcomes (Bello, 2024).
Scope and Limitations of the Study:
This study is limited to the analysis of student sentiment on the learning management system at Federal University Dutsin-Ma, Katsina State, and does not extend to other digital platforms or institutions.
Definitions of Terms:
- Big Data Analytics: The process of examining large and varied data sets to uncover hidden patterns and insights (Abdullahi, 2023).
- Sentiment Analysis: A method of analyzing textual data to determine the emotional tone and opinions expressed (Bello, 2024).
- Learning Management System (LMS): A software platform used to deliver, track, and manage educational content and learning activities (Chukwu, 2025).
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