Development of an AI-Based Performance Prediction Model for University Lecturers: A Case Study of Federal University, Lafia (Lafia LGA, Nasarawa State)
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- NGN 5000
Background of the Study
Predicting the performance of university lecturers can be a valuable tool for enhancing teaching quality and improving faculty development strategies. Federal University, Lafia, located in Lafia LGA, Nasarawa State, faces challenges in assessing and predicting the effectiveness of its lecturers. Traditional methods of evaluating lecturer performance are often subjective, based on student feedback or departmental reviews, which may not fully capture the multifaceted nature of teaching effectiveness.
This study will focus on developing an AI-based performance prediction model that leverages data from various sources, such as student feedback, teaching evaluations, and research outputs, to predict the performance of lecturers at Federal University, Lafia. The model will utilize machine learning techniques to provide more objective, data-driven insights into lecturer performance.
Statement of the Problem
There is a lack of a systematic, data-driven approach to predict lecturer performance at Federal University, Lafia. Traditional evaluation methods are limited in their ability to provide accurate and comprehensive assessments of teaching quality. AI-based performance prediction models could offer a more objective and reliable way to evaluate lecturers and inform faculty development programs.
Objectives of the Study
1. To develop an AI-based performance prediction model for university lecturers at Federal University, Lafia.
2. To evaluate the effectiveness of the model in predicting lecturer performance based on multiple data sources.
3. To assess the potential impact of AI-based performance predictions on faculty development and institutional decision-making.
Research Questions
1. How can AI-based models be developed to predict the performance of university lecturers?
2. What data sources are most effective for predicting lecturer performance using AI?
3. How accurate and reliable are AI-based performance prediction models compared to traditional evaluation methods?
Research Hypotheses
1. AI-based performance prediction models will provide more accurate and reliable predictions of lecturer performance compared to traditional methods.
2. Data from multiple sources, including student feedback, teaching evaluations, and research outputs, will improve the accuracy of the AI model in predicting lecturer performance.
3. The implementation of AI-based performance prediction models will positively influence faculty development programs and institutional decision-making.
Significance of the Study
This study will contribute to the development of more objective, data-driven methods for evaluating lecturer performance at Federal University, Lafia. The findings could help improve teaching quality, guide faculty development initiatives, and support more informed decision-making at the institutional level.
Scope and Limitations of the Study
The study will focus on the development of an AI-based performance prediction model for lecturers at Federal University, Lafia. It will not address broader aspects of faculty evaluation or AI applications in other university administrative processes. The study will also be limited by the availability of data and resources for developing and implementing the AI model.
Definitions of Terms
• Performance Prediction Model: A statistical or machine learning model that uses data to predict the future performance of individuals, in this case, university lecturers.
• Machine Learning: A branch of AI that allows systems to learn from data and make predictions or decisions without being explicitly programmed.
• Teaching Evaluations: Assessments made by students or other stakeholders to evaluate the quality of teaching, including course content, delivery, and lecturer performance.
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