An Investigation of AI-Based Automated Grade Prediction Systems in Kogi State University, Anyigba, Kogi State
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- NGN 5000
Background of the Study
Grade prediction is an important aspect of academic performance analysis in universities, as it provides insights into students' potential academic success or failure. Traditionally, grade prediction has been based on past performance metrics, which may not fully account for factors like attendance, participation, and student behavior. AI-based automated grade prediction systems, which use machine learning algorithms to analyze large datasets, can provide more accurate predictions by considering a wide array of student data. These systems can also assist instructors in identifying students at risk of underperforming, allowing for early interventions to improve learning outcomes. This study aims to explore the design and implementation of an AI-based automated grade prediction system at Kogi State University, Anyigba, to enhance academic monitoring and support systems.
Statement of the Problem
At Kogi State University, Anyigba, traditional methods of grade prediction rely primarily on student assessments, which may not accurately reflect their overall academic performance due to the influence of various unmeasured factors. This gap in prediction accuracy may hinder timely interventions for struggling students. The introduction of an AI-based grade prediction system can automate this process and provide more reliable forecasts of students' academic success by analyzing a broader set of performance indicators. This study seeks to investigate the potential of such a system in improving grade predictions at the university.
Objectives of the Study
1. To design an AI-based automated grade prediction system for students at Kogi State University, Anyigba.
2. To implement the system and evaluate its accuracy in predicting students' academic grades.
3. To assess the effectiveness of the AI system in identifying students at risk of academic failure.
Research Questions
1. How accurate is the AI-based automated grade prediction system in forecasting students' final grades?
2. How does the AI-based system improve the prediction of academic success compared to traditional grading methods?
3. How can the grade prediction system support early intervention programs for students at risk of failure?
Research Hypotheses
1. The AI-based grade prediction system provides more accurate predictions of students' final grades than traditional methods.
2. The use of an AI-based system significantly improves early identification of students at risk of academic failure.
3. Students who receive early interventions based on AI predictions perform better academically than those who do not.
Significance of the Study
This study will help Kogi State University improve its academic monitoring and support systems through the implementation of an AI-based grade prediction system. By providing accurate and timely predictions, the system will allow for early interventions that can help improve student success and retention rates.
Scope and Limitations of the Study
The study will focus on the design and implementation of the AI-based grade prediction system within Kogi State University, Anyigba. Limitations include potential issues with data quality and the availability of historical academic performance records necessary for training the AI model.
Definitions of Terms
• AI-Based Automated Grade Prediction System: A machine learning-powered system that predicts students' final grades based on multiple data points such as past performance, attendance, and engagement.
• Academic Intervention: Actions taken by instructors or institutions to support students identified as at risk of underperforming.
• Machine Learning: A type of AI that enables systems to learn from data and make predictions or decisions based on that information.
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