Skill gap analysis and Personalized Learning Recommendation

Research areas


There is a need for developing a one stop solution for a student's forever learning. It would enable seamless omnichannel mechanisms to integrate and get data from several job sites, extract the skill requirements for such jobs. Using this information, it will access the knowledge graph (topics/concepts dependency graph) and the student learning graph to identify the learning gaps and suggest learning content that is most appropriate for the student to bridge the gap between her skills and the job requirements. The suggested path will be developed by using generative models that consider student past learning styles, the retention profiles, the engagement information and the assessments. The complexities of generative models and the learning content metadata will all be hidden from the user. The aim of this project is to develop an application (web or mobile) for skill gap analysis and personalized learning progress system. 

Updated:  1 June 2019/Responsible Officer:  Dean, CECS/Page Contact:  CECS Marketing