Assessing the Job Satisfaction of Teachers Using Deep PSO in Educational Institutions with Special Reference to Chennai District

Background: The degree of job satisfaction of teachers determines both the quality of instruction and the atmosphere of learning advantageous for them. Several factors have affected teachers’ job satisfaction in Chennai District: workload, salary, opportunity for professional development, and work-life balance. Although this is a serious issue, little systematic analysis using advanced computer methods is done. Sometimes conventional methods of gauging job satisfaction—such as questionnaires and qualitative interviews—show limited scalability and subjective bias. Novelty: This work aims to evaluate and investigate teacher job satisfaction in Chennai District’s educational institutions by means of a Deep Particle Swarm Optimisation (Deep PSO) technique, therefore overcoming these limitations. Objectives: The major objectives are to optimise enjoyment by means of targeted therapies and identify significant components affecting it. Findings: This work uses Deep PSO, a hybrid method combining Particle Swarm Optimisation (PSO) with deep learning methodologies, to evaluate data acquired from 500 teachers scattered over numerous educational institutions in Chennai District. While the Deep PSO model finds complex patterns and connections in the data using multi-layered neural networks, the PSO approach maximises the weights and biases of the model to increase forecast accuracy. The model estimates job satisfaction based on several criteria: workload, pay scale, professional development opportunities, and institutional support. Based on respective values of 0.43 and 0.31, respectively, the Deep PSO model’s findings reveal that job satisfaction’s most significant determinants are workload and professional development opportunities. With an $\mathbf{8 7. 6} \%$ prediction accuracy, the model beat accepted methods by 12.4%. Furthermore, projected to increase general job satisfaction by 18.7% are specific recommendations to minimise workload by 15% and increase chances for professional development by $\mathbf{2 0 \%}$.