Evaluating the Performance of Nigerian Lecturers using Multilayer Perceptron

TL;DR

This paper presents a web-based system utilizing a Multilayer Perceptron to evaluate Nigerian lecturers' performance with 91% accuracy, incorporating diverse metrics for a comprehensive assessment to improve fairness and decision-making.

Contribution

It introduces a novel holistic lecturer evaluation system using MLP with multiple performance metrics and analytical tools, enhancing accuracy and fairness over traditional methods.

Findings

Achieved 91% accuracy in performance prediction.

MLP model demonstrated high prediction accuracy with 96% estimated accuracy.

Reduced bias and supported data-driven decisions in lecturer evaluation.

Abstract

Evaluating the performance of a lecturer has been essential for enhancing teaching quality, improving student learning outcomes, and strengthening the institution's reputation. The absence of such a system brings about lecturer performance evaluation which was neither comprehensive nor holistic. This system was designed using a web-based platform, created a secure database, and by using a custom dataset, captured some performance metrics which included student evaluation scores, Research Publications, Years of Experience, and Administrative Duties. Multilayer Perceptron (MLP) algorithm was utilized due to its ability to process complex data patterns and generates accurate predictions in a lecturer's performance based on historical data. This research focused on designing multiple performance metrics beyond the standard ones, incorporating student participation, and integrating analytical tools to deliver a comprehensive and holistic evaluation of lecturers' performance and was developed using Object-Oriented Analysis and Design (OOAD) methodology. Lecturers' performance is evaluated by the model, and the evaluation accuracy is about 91% compared with actual performance. Finally, by evaluating the performance of the MLP model, it is concluded that MLP enhanced lecturer performance evaluation by providing accurate predictions, reducing bias, and supporting data-driven decisions, ultimately improving the fairness and efficiency of the evaluation process. The MLP model's performance was evaluated using Mean Squared Error (MSE) and Mean Absolute Error (MAE), achieved a test loss (MSE) of 256.99 and a MAE of 13.76, and reflected a high level of prediction accuracy. The model also demonstrated an estimated accuracy rate of approximately 96%, validated its effectiveness in predicting lecturer performance.