An analysis of effect and consequence associated with stress among technical vocational teachers in Malaysia using Artificial Intelligence (AI) machine learning

The white-collar vocational teaching is a profession at high risk for stress. This research attempted to predict and determine the effect and consequence associated with the stress among technical vocational teachers in Malaysia. A cross-sectional random sample was taken on seven (7) vocational college in Perak, which involve approximately 490 technical teaching staff. The questionnaire method by Depression, Anxiety and Stress Scale (DASS-42) and Job Content Questionnaire (JCQ) instrument used as a data to indicate stress level among teachers. These data consist of psychosocial factors contributing to stress, a simple and multiple linear regression analysis were carried out. The prediction element of Artificial Intelligence (AI) implements Support Vector Machine (SVM) method, ignites a few groups of stress teacher start to form among the others. As in the 2023 to 2025, expected a clear segregation between normal and stress teacher within the boundary. While in 2026-2029, expected a huge migration trends of normal to critical teacher dominate the chart. However, the AI system are still depending on several controlled variable, and the result are still expected to be, means there must be a room to improvise the situation of teacher mental health and etc.