JOB BURNOUT AND SATISFACTION AMONG SECONDARY TEACHERS: REGRESSION AND K-MEANS CLUSTERING ANALYSIS

Abstract

Burnout on a teaching job refers to exhaustion that leads to depression which can affect the teacher’s well-being and satisfaction. This article looks into the level of job burnout and satisfaction of high school teachers during modular distance learning and determines its association at different levels. Cross-sectional primary data were gathered from a random sample of secondary teachers in Ormoc City, Leyte, Philippines. The gathered data were job burnout and satisfaction scores and described using standard statistical metrics. Moreover, correlation and regression analysis were employed to analyze its association, and K-means clustering was used to identify homogeneous categories of job burnout and satisfaction scores with similar characteristics. On average, results showed that the high school teachers were still “satisfied” despite the “high burnout” they were facing during the modular distance learning amid the pandemic. Using the correlation analysis, it is revealed that teachers’ satisfaction and burnout are significantly and inversely correlated to each other. This implies that the burnout level adversely affects the satisfaction in teaching. Based on regression analysis, there is a decrease of 0.653 units in the satisfaction perception score for every 1 unit increase in the burnout perception score and it is significant at a 1% level. It is depicted in K-means clustering that teachers’ satisfaction and burnout levels are compact and converge to the interpretation that they were highly burnout yet satisfied in modular distance learning. Hence, the study suggests that teachers' tasks during distance education must be lessened to increase their well-being as educators.