Vouch augmented Program Courses Recommendation System for E-Learning

Abstract

In the realm of remote working and online living, it is imperative for educational institutions to steer their teaching learning process toward virtual classrooms and online evaluations. However, the existing learning management systems offer online video conference tools and facilitate the stakeholders with recommendations based on hard coded rules. As a result, the participating students also are failing to consolidate the theoretical concepts and started shying away from the critical core courses which are highly an unwanted situation for the next generation knowledge building. This paper aspires to propose developing a cognizant system which continually collects the multi-modal data from diversified sources, integrate them with the emotions of the students, intuitions of the torch bearers of various fields and evolve authentic recommendations to the students. The proposed vouch augmented learning management system, which we refer as vLMS, offers a framework that does a deep poll in the background on various data sources, derives the semantic relations into cognizance and offers hard as well as soft recommendations for a student to rediscover himself. Finally, the article presents the detailed architecture, the suitable soft-computing models and the technology stack support for implementing the vLMS framework.