Comparative Study of AI-Based Versus Traditional Student Performance Prediction Models in Federal University, Wukari, Taraba State
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- NGN 5000
Background of the study
Predicting student performance is a critical aspect of educational management, enabling institutions to provide targeted support and improve overall academic outcomes. Traditionally, student performance prediction has been based on historical data such as grades and attendance, with educators using statistical methods or intuition to make predictions. However, the advent of AI-based models has revolutionized this process by leveraging vast amounts of data and advanced algorithms to make more accurate and personalized predictions. In the context of Federal University, Wukari, Taraba State, this study will explore the effectiveness of AI-based prediction models compared to traditional methods. By using machine learning techniques, such as decision trees, neural networks, and regression models, AI systems can process large datasets and predict student performance with greater precision. This comparative study will provide insights into which methods are more effective in predicting student outcomes, allowing the university to adopt the best approach for academic planning and support.
Statement of the problem
At Federal University, Wukari, the current methods for predicting student performance mainly rely on traditional techniques that do not fully capitalize on the wealth of data available. These methods may lack the sophistication needed to provide accurate predictions and are often unable to account for the multitude of factors influencing student success. AI-based models, with their ability to process large datasets and identify complex patterns, could offer a more accurate and personalized approach to performance prediction. This study aims to compare the effectiveness of AI-based student performance prediction models with traditional models to determine which method is more accurate and beneficial for the university.
Objectives of the study
1. To compare the accuracy and efficiency of AI-based student performance prediction models and traditional models at Federal University, Wukari.
2. To identify the factors that significantly impact student performance as predicted by both AI-based and traditional models.
3. To recommend the most effective student performance prediction method for Federal University, Wukari.
Research questions
1. How do AI-based student performance prediction models compare with traditional models in terms of accuracy and efficiency?
2. What are the key factors influencing student performance as identified by both AI-based and traditional prediction models?
3. Which prediction model provides the most reliable results for academic planning at Federal University, Wukari?
Research hypotheses
1. AI-based student performance prediction models will provide more accurate predictions compared to traditional models.
2. There will be significant differences in the factors influencing student performance identified by AI-based and traditional prediction models.
3. AI-based models will offer better efficiency in predicting student performance at Federal University, Wukari.
Significance of the study
This research will provide valuable insights into the strengths and weaknesses of AI-based prediction models compared to traditional methods. The findings can inform the adoption of more effective prediction models in Federal University, Wukari, potentially enhancing student support services and improving overall academic outcomes.
Scope and limitations of the study
The study will focus on comparing AI-based and traditional student performance prediction models at Federal University, Wukari, Taraba State. Limitations include the availability of quality student data, potential biases in traditional methods, and the complexity of training accurate AI models for prediction.
Definitions of terms
• AI-Based Prediction Models: Machine learning algorithms used to predict student performance by analyzing large datasets and identifying patterns.
• Traditional Prediction Models: Conventional methods, often statistical, used to predict student performance based on historical data and educator judgment.
• Student Performance: The academic success of students, typically measured by grades, test scores, and overall achievement.
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