Lecturer Performance System Using Neural Network with Particle Swarm Optimization

TL;DR

This paper presents a performance assessment system for university lecturers using a neural network optimized with particle swarm optimization, achieving high accuracy and aiming to improve fairness and effectiveness in evaluations.

Contribution

It introduces a novel application of PSO-optimized neural networks for lecturer performance evaluation, enhancing accuracy and addressing fairness issues in traditional methods.

Findings

Achieved 98.28% accuracy in performance recognition

Collected and processed real datasets from Salahaddin University

Demonstrated improved assessment fairness and precision

Abstract

The field of analyzing performance is very important and sensitive in particular when it is related to the performance of lecturers in academic institutions. Locating the weak points of lecturers through a system that provides an early warning to notify or reward the lecturers with warned or punished notices will help them to improve their weaknesses, leads to a better quality in the institutions. The current system has major issues in the higher education at Salahaddin University-Erbil (SUE) in Kurdistan-Iraq. These issues are: first, the assessment of lecturers' activities is conducted traditionally via the Quality Assurance Teams at different departments and colleges at the university, second, the outcomes in some cases of lecturers' performance provoke a low level of acceptance among lectures, as these cases are reflected and viewed by some academic communities as unfair cases, and finally, the current system is not accurate and vigorous. In this paper, Particle Swarm Optimization Neural Network is used to assess performance of lecturers in more fruitful way and also to enhance the accuracy of recognition system. Different real and novel data sets are collected from SUE. The prepared datasets preprocessed and important features are then fed as input source to the training and testing phases. Particle Swarm Optimization is used to find the best weights and biases in the training phase of the neural network. The best accuracy rate obtained in the test phase is 98.28 %.