Intelligent System to Verify the Effectiveness of Proposed Teacher Transfers Incorporating Human Factors

• Published in: 2022 2nd International Conference on Advanced Research in Computing (ICARC) pp. 1 - 6
• Main Authors: Karunanayake, Vathsala J., Wanniarachchi, Janith C., Karunanayake, Piumika N., Rajapaksha, U.U. Samantha
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23.02.2022
• Subjects: Computational modeling; Government policies; Human factors; Intelligent systems; Job Satisfaction; Logistics; Machine Learning; Random forests; Regression tree analysis; School System; Teacher Transfer
• DOI: 10.1109/ICARC54489.2022.9754089

Sri Lankan government school system, teachers are transferred every ten years/ five years, based on government policy and upon personal request of teachers. For teacher transfers based on government policy, the education ministry only considers factors such as the vacancies, subject the teacher teaches and the district of the transferred school. But according to several research studies it has been proven that the performance of an employee depends on human factors such as gender, organizational culture etc. Therefore, a question arises on job satisfaction of the teachers when they are transferred based on government policy. By conducting a survey, information on job satisfaction and other human factors which impact on satisfaction of teachers was gathered. With the analysis of the collected data using classifiers such as Logistic Regression, Decision Tree classifier, Random Forest classifier and Gradient Boost classifier this study attempts to identify whether a teacher will be satisfied with a new placement prior to being transferred. Result prove that Decision Tree classifier provides a better model with higher accuracy compared to other models in identifying job satisfaction of an employee with a new work place.