Proposed Model for Predictive Mapping Graduates' Skills to Industry Roles using Machine Learning Techniques

Abstract

The main focus in training evaluation is not only to determine whether training objectives were achieved but also how to improve evaluation so as to enhance both employability of graduates and performance in the job. This is in response to challenges facing not only graduates in choosing industry jobs that befit their skills, but also employers in selecting graduates whose skills match to their needs. Problem solving is one of the skills acquired during training by graduates and strongly sought for by employers during evaluation to promote performance in the job. This paper presents a model for evaluating graduates’ by mapping their problem solving skills to industry jobs’ competence requirements and the potential of using machine learning techniques to train the model in predicting suitable industry jobs for new graduates from college. The paper outlines challenges facing both graduates and industry in selecting industry jobs and skilled graduates respectively, highlights trends, methods, and gaps in skill evaluation and prediction. A brief discussion is made of key strategies in skill evaluation and prediction that need to be undertaken and evaluation theories behind the key variables of the proposed model.