Evaluation of Data Science Techniques for Student Mental Health Monitoring in Federal Polytechnic Bauchi, Bauchi State
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- NGN 5000
Background of the Study
Student mental health has emerged as a critical issue in higher education, affecting academic performance, social interactions, and overall well-being. At Federal Polytechnic Bauchi, Bauchi State, the application of data science techniques offers an innovative approach to monitor and support student mental health. By analyzing data from various sources such as counseling records, academic performance, attendance, and even social media interactions, data science models can identify early warning signs of mental distress and predict trends that may require intervention (Ibrahim, 2023). These techniques leverage machine learning algorithms, natural language processing, and predictive analytics to process vast amounts of data, providing a proactive mechanism to address mental health issues before they escalate (Sani, 2024).
The integration of data science into mental health monitoring aligns with global trends in personalized healthcare and education, where technology is used to provide targeted support based on individual needs. Such an approach enables institutions to allocate resources effectively, tailor mental health programs, and create supportive environments that foster academic success and personal development. The use of data-driven insights can also help in destigmatizing mental health issues by promoting a culture of transparency and proactive care (Olaitan, 2025). Moreover, real-time monitoring and analysis facilitate timely interventions, ensuring that students receive the necessary support and counseling services promptly.
However, the implementation of data science techniques in mental health monitoring also presents challenges, particularly in terms of data privacy, ethical considerations, and the accuracy of predictive models. Ensuring that sensitive student information is handled securely and used solely for supportive purposes is paramount. This study aims to critically evaluate the effectiveness of various data science techniques in monitoring student mental health at Federal Polytechnic Bauchi, providing insights into best practices, potential challenges, and recommendations for policy and practice improvements (Ibrahim, 2023).
Statement of the Problem
Despite the potential benefits of data science in enhancing mental health monitoring, Federal Polytechnic Bauchi faces significant challenges in its effective implementation. A major problem is the lack of standardized data collection protocols across different departments, which leads to fragmented and inconsistent datasets that hinder comprehensive analysis (Ibrahim, 2023). Additionally, the sensitive nature of mental health data raises serious ethical and privacy concerns. Without robust data security measures and clear ethical guidelines, the risk of misuse or unauthorized access to personal information remains high, potentially deterring students from seeking help (Sani, 2024).
Another challenge lies in the accuracy and reliability of predictive models used to identify mental health issues. Inaccurate predictions can lead to false alarms or, conversely, missed cases of distress, thereby affecting the credibility of the monitoring system. Furthermore, there is often resistance from both students and staff regarding the collection and analysis of personal mental health data, largely due to fears of stigmatization and breaches of confidentiality (Olaitan, 2025). These challenges underscore the need for a critical evaluation of current data science methodologies and the development of a comprehensive framework that addresses issues of data integration, ethical use, and system accuracy. This study seeks to identify these key challenges and propose practical solutions to enhance the efficacy of mental health monitoring initiatives at Federal Polytechnic Bauchi (Ibrahim, 2023).
Objectives of the Study:
- To evaluate the effectiveness of data science techniques in monitoring student mental health at Federal Polytechnic Bauchi.
- To identify challenges related to data integration, privacy, and model accuracy in the mental health monitoring process.
- To propose a framework for the ethical and efficient application of data science in supporting student mental health.
Research Questions:
- How effective are current data science techniques in predicting and monitoring student mental health issues?
- What are the primary challenges in data collection, privacy, and accuracy affecting these techniques?
- What strategies can be implemented to improve the ethical application and effectiveness of mental health monitoring systems?
Significance of the Study
This study is significant as it evaluates the role of data science techniques in monitoring student mental health at Federal Polytechnic Bauchi. By identifying key challenges and proposing a comprehensive framework, the research aims to enhance the early detection and intervention of mental health issues. The findings will provide actionable insights for educators, mental health professionals, and policymakers, contributing to a safer and more supportive educational environment that prioritizes student well-being (Olaitan, 2025).
Scope and Limitations of the Study:
This study is limited to the evaluation of data science techniques for student mental health monitoring at Federal Polytechnic Bauchi, Bauchi State, and does not extend to other mental health interventions or institutions.
Definitions of Terms:
- Data Science Techniques: Methods and processes used to analyze large datasets to extract meaningful insights (Ibrahim, 2023).
- Mental Health Monitoring: The systematic process of tracking indicators related to psychological well-being (Sani, 2024).
- Predictive Analytics: The use of statistical algorithms and machine learning techniques to forecast future outcomes based on historical data (Olaitan, 2025).
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