Machine learning based Collaborative Intelligent Closing Gap between Graduates and Labour Market Framework

Unemployment of college graduates is a major issue in many countries throughout the world. Nevertheless, rapid economic growth in many fields requires graduates with specialized skills, particularly in the Gulf region and especially in the Kingdom of Saudi Arabia, which has two main projects, Saudi Vision 2030 and NEOM. Such projects need graduates equipped with skills and experience to cope with economic growth, digital transformation, technological advancements, and future needs. Therefore, Saudi Vision is focusing on the second pillar of Saudi Vision 2030: creating a thriving economy to increase employment and reduce the unemployment rate from 11.6% to 7%. Some studies have sought to highlight or to solve graduate unemployment problems using traditional methods. Even though many of them are time-consuming and endeavouring, the outcome received is exceedingly faint. Hence, this study utilizes information technology to build a framework called Collaborative Intelligent Computer study Graduates and Labour Market Framework (CICCLM). CICCLM uses a machine learning approach and data science methods to identify the weaknesses to close the gap between university computing graduates and employer expectations through an analysis of the labour market requirements aligning them to the graduates `capabilities. Furthermore, CICCLM provides recommendations and visualization platform about the mismatch between academic education and industry requirements. Additionally, CICCLM provides recommendations to update the curriculum and course plan, while providing training courses for old graduate students.