A Sentiment Analysis-Based Recommender Framework for Massive Open Online Courses Toward Education 4.0

The emergence and confluence of progressive technologies like artificial intelligence, Internet of things, and automation in Industry 4.0 have also driven parallel domains like the education sector. Today’s digital education aligns with the progressive dynamics of Industry 4.0, and with the increasing mix of information and communication technology (ICT), we have entered the era of Education 4.0. The ICT tools gather a lot of data content, which is generated through data generation in the form of text, audio, images, and video in online social networks (OSNs), blogs, posts, and many others. Usage of ICT has facilitated the conduction of open courses to masses of people connected through heterogeneous networked applications. Such courses termed as massive open online course (MOOC) platforms have grown significantly and have reaped high profits. However, users browsing for suitable courses in MOOC platforms are faced with challenges of selecting and filtering courses, based on current demands, effectiveness, and pre-requisite knowledge. Scientifically, it is observed that due to incorrect course selection, users are many times not satisfied with the MOOC course, which results in high dropouts. In the past, researchers have addressed the issue through recommender systems for users, but recommendation systems require effective filtering mechanisms for proper results. Thus, to address the research gap, in this paper, we propose an approach that is based on skills information from users’ LinkedIn profiles combined with ratings and review data of courses. For experimental validation, we consider a Udemy MOOC user public dataset and apply natural language processing (NLP) to contextually organize user reviews, skill-set keywords from LinkedIn and refine search keywords. The proposed results indicate the efficacy of the framework toward correct MOOC recommendations for active learners and users.