Design and Development of an optimal algorithm to assign applicants to suitable teaching positions

Abstract

Resource allocation has always been an area of interest in the area of computing. Areas such as machine learning provide many solutions to the problem of resource allocation. The issue addressed in this study is the issue of optimal allocation of applicants (teachers) to positions in schools where their area of specialization will be better applied. We develop an algorithm that is able to allocate applicants to schools based on the applicant qualifications and the school's needs. We use the principles of resource allocation and machine learning in order to create an application to allocate applicants to schools where their qualifications are most suited. Methods used include classification techniques in machine learning, regression and similarity comparison. For the identification of subjects an applicant in proficient in, various machine learning algorithms are tested to determine which machine learning algorithm was best. The actual process of identifying which applicant qualifies for a school position is also tested against sequential assignment if applicants to schools. The results of this was the algorithm based assignment of applicants to schools which produced more accurate assignment of applicants to schools than the sequential assignment of applicants. The aim of this algorithm is to provide a solution to that automatically identifies the needs (subjects) of a school, determine which needs have a higher priority, identify the qualifications of the applicants and assign the applicants to the school according to the school's needs and the applicant's qualifications.