Optimization of Quantum Machine Learning Techniques for Academic Performance Prediction in Federal University, Dutse, Jigawa State
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- NGN 5000
Background of the Study
Machine learning (ML) techniques have been widely used in educational institutions to analyze and predict students’ academic performance. However, traditional machine learning models are often limited in their ability to handle the complex, high-dimensional data typically associated with educational environments. Quantum machine learning (QML) is an emerging field that combines quantum computing and machine learning to enhance the capabilities of predictive models (Lloyd et al., 2023). By leveraging quantum computing’s ability to process large datasets and explore a wide solution space more efficiently, QML techniques can offer improved predictive accuracy compared to classical methods.
At Federal University, Dutse, Jigawa State, the application of quantum machine learning techniques for predicting academic performance could revolutionize the way the university assesses and supports student progress. Using quantum-enhanced models may provide more accurate predictions of students’ future performance, allowing for better-targeted interventions, personalized learning strategies, and improved overall educational outcomes.
Statement of the Problem
Current methods for predicting academic performance at Federal University, Dutse, are based on traditional statistical and machine learning techniques, which may not fully capture the complexity of student data. With the growing interest in quantum computing, there is a need to explore the optimization of quantum machine learning models for this task. The lack of quantum-based approaches in predicting academic performance at the university presents an opportunity to enhance prediction accuracy and provide better insights into students' learning patterns.
Objectives of the Study
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To explore the application of quantum machine learning techniques for predicting academic performance at Federal University, Dutse.
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To optimize quantum machine learning models to improve the accuracy of academic performance predictions.
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To evaluate the effectiveness of quantum machine learning models compared to traditional machine learning techniques in academic performance prediction.
Research Questions
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How can quantum machine learning techniques be applied to predict academic performance at Federal University, Dutse?
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How can quantum machine learning models be optimized to improve prediction accuracy?
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How do quantum machine learning models compare with traditional machine learning models in predicting academic performance?
Significance of the Study
This study will explore the potential of quantum machine learning to transform the prediction of academic performance at Federal University, Dutse. By developing optimized quantum models, the university will be able to make more accurate predictions, providing actionable insights to improve student support and outcomes. The findings will also contribute to the growing body of knowledge in quantum machine learning, showcasing its potential applications in higher education.
Scope and Limitations of the Study
The study will focus on the application and optimization of quantum machine learning techniques for academic performance prediction at Federal University, Dutse, Jigawa State. The research will be limited to the comparison of quantum and traditional machine learning models and will not address broader educational or technological issues.
Definitions of Terms
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Quantum Machine Learning: The integration of quantum computing and machine learning, where quantum computers are used to enhance machine learning algorithms.
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Academic Performance Prediction: The use of data analysis techniques to forecast students’ future academic outcomes based on various input factors.
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Optimization: The process of improving a model or algorithm to maximize its performance or efficiency, such as improving prediction accuracy.
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