WORK LIFE BALANCE AMONG WOMEN TEACHERS IN HIGHER EDUCATION USING CLUSTERING ALGORITHMS

Abstract

Work-life balance (WLB) has become a major subject matter in modern organizational research and especially in higher education. The main challenge that women teachers in higher learning institutions experience is the management of professional roles and personal and family roles at the same time. The study will examine how women teachers manage work and life through the K-means clustering algorithms at institutions of higher learning. The research is quantitative in nature with the amount of information obtained using surveys aimed at women teachers teaching in diverse colleges and universities. Most factors associated with work-life balance were identified (workload, support systems, work flexibility, family responsibilities, and job satisfaction) as features used to cluster. The K-means clustering algorithm was deployed to help identify the respondents into various clusters using these factors. The findings provide a good understanding of the various trends of work-life balance among women teachers, as it yields sensitive perceptions of the changing experiences and pressures experienced differently by women teachers. The results indicate that in spite of the diehard equality of some women in the work-life structure, there are women that take much pressure considering the lack of institutional and family related obligations. This paper has contributed to literature on gender-related issues in work-life balance and also gives suggestions on how the policy can be improved to ease work-life balance to women in academia. By applying clustering algorithms, it can be concluded that this is an interesting way to divide this complicated topic and offer higher education institutions to address the changes to various demands.