TY - GEN
T1 - A Sentiment Analysis-Based Recommender Framework for Massive Open Online Courses Toward Education 4.0
AU - Bhatia, Akhil
AU - Asthana, Anansha
AU - Bhattacharya, Pronaya
AU - Tanwar, Sudeep
AU - Singh, Arunendra
AU - Sharma, Gulshan
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Crowd mining
KW - Long short-term memory
KW - Massive open online courses
KW - Recommender systems
KW - Review mining
UR - http://www.scopus.com/inward/record.url?scp=85135078859&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-1142-2_64
DO - 10.1007/978-981-19-1142-2_64
M3 - Conference contribution
AN - SCOPUS:85135078859
SN - 9789811911415
T3 - Lecture Notes in Networks and Systems
SP - 817
EP - 827
BT - Proceedings of 3rd International Conference on Computing, Communications, and Cyber-Security, IC4S 2021
A2 - Singh, Pradeep Kumar
A2 - Wierzchoń, Sławomir T.
A2 - Tanwar, Sudeep
A2 - Rodrigues, Joel J.P.C.
A2 - Rodrigues, Joel J.P.C.
A2 - Ganzha, Maria
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Computing, Communications, and Cyber-Security, IC4S 2021
Y2 - 30 October 2021 through 31 October 2021
ER -
