Development of a Deep Learning Model for Early Detection of Academic Struggles: A Case Study of University of Maiduguri (Maiduguri LGA, Borno State)
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- NGN 5000
Background of the Study
The success of students in higher education is often linked to early intervention when academic struggles are detected. At the University of Maiduguri, located in Maiduguri LGA, Borno State, the university has a diverse student body with varying learning needs. Identifying students who may be at risk of academic failure or underperformance is crucial for timely intervention, as early support can prevent long-term academic consequences.
Deep learning models, a subset of artificial intelligence (AI), have demonstrated remarkable success in various domains, including education. These models are capable of analyzing large datasets, identifying patterns, and making predictions based on historical data. In the context of higher education, deep learning can be used to develop predictive models that detect academic struggles early. By analyzing student performance data, such as grades, attendance, and engagement levels, deep learning models can predict students who are at risk of failing, allowing educators to provide targeted interventions.
Statement of the Problem
University of Maiduguri lacks an effective system to identify students who are struggling academically at an early stage. Without early detection, at-risk students may continue to face challenges, which can negatively impact their academic performance and overall success. This gap in early intervention highlights the need for an AI-driven model that can analyze academic data and predict potential struggles before they become severe.
Objectives of the Study
1. To develop a deep learning model for the early detection of academic struggles at the University of Maiduguri.
2. To evaluate the accuracy of the deep learning model in predicting student performance and academic struggles.
3. To provide recommendations on how the university can use the deep learning model to improve student support services.
Research Questions
1. How can deep learning models be utilized to detect early signs of academic struggles in students at the University of Maiduguri?
2. How accurate is the deep learning model in predicting student academic struggles?
3. How can the deep learning model be integrated into the university’s academic support systems?
Research Hypotheses
1. The deep learning model will accurately predict students at risk of academic struggles at the University of Maiduguri.
2. Early detection using the deep learning model will lead to improved academic support for at-risk students.
3. The use of deep learning models in predicting academic struggles will enhance the overall academic performance of students at the University of Maiduguri.
Significance of the Study
This research will provide an innovative solution for early detection of academic struggles at the University of Maiduguri. By implementing a deep learning-based predictive model, the university can offer more personalized and timely interventions for students, ultimately improving academic outcomes and student retention rates.
Scope and Limitations of the Study
The study will focus on the development and evaluation of a deep learning model for detecting academic struggles at the University of Maiduguri, Maiduguri LGA, Borno State. It will be limited to undergraduate students across a range of disciplines and will not include postgraduate students or other universities.
Definitions of Terms
• Deep Learning Model: A type of machine learning model that uses neural networks to learn from large amounts of data and make predictions or decisions.
• Early Detection: The process of identifying potential academic struggles or challenges at an early stage, allowing for timely intervention.
• At-Risk Students: Students who are identified as being at a higher risk of academic failure based on performance metrics such as grades, attendance, and engagement.
• Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes.
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