Int J Performability Eng ›› 2023, Vol. 19 ›› Issue (5): 324-333.doi: 10.23940/ijpe.23.05.p4.324333

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Learner-Centric Hybrid Filtering-Based Recommender System for Massive Open Online Courses

Ramneet Kaura, Deepali Guptaa,*, and Mani Madhukarb   

  1. aChitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India;
    bGlobal University Programs, IBM India Private Limited, Greater Noida, India
  • Contact: * E-mail address:
  • About author:Ramneet Kaur is an Assistant Professor at Chitkara University, Punjab. Her research interests include sentiment analysis, e-learning, and machine learning.
    Deepali Gupta is a Professor at Chitkara University, Punjab. Her research interests include cloud computing, fog computing, e-learning, machine learning, artificial intelligence, and IoT.
    Mani Madhukar is a Global Program Manager at IBM India Private Limited, Greater Noida. His research interests include blockchain, machine learning, quantum computing, cloud, and IoT.

Abstract: Massive Open Online Courses (MOOCs) have significantly impacted the basic education industry since 2012. Online platforms enable learners to connect with the instructors present worldwide and reduce learning time by approximately 50-60%. Many universities have opted for their survival in the pandemic of COVID-19. During the pandemic, novice learners were not able to enroll in the relevant courses on these platforms, and instructors also faced challenges to satisfy their learners' needs. Each online forum has its own recommender system, and these systems only recommend courses from their own platforms. As a result, these platforms fail to satisfy the learners' educational needs and thereby increase the dropout ratio. The main objective of this study is to create a single platform for learners to search for courses from multiple platforms like Coursera, Udemy, EdX, Udacity, etc., and then recommend courses according to the learning behavior of a learner. A user profile is created in three ways, i.e., by registering, uploading their CV, or through their LinkedIn accounts. The recommender system then uses this user profile as input and recommends the relevant courses for user adaption. In this paper, demographic, content-based and collaboration-based recommender systems are used for recommendations. To validate, multi-model filtering, namely random, user-based collaboration, item-based collaboration, and matrix factorization, is used to obtain the values of the performance metrics such as RMSE, precision, and recall. On the basis of the results, the best result is obtained from user-based collaboration filtering on 6,000 dimensions of the dataset. The value of RMSE in the case of user-based collaboration filtering is 0.101, the value of precision is 0.82, and the value of recall is 0.822. Thus, the learner-centric hybrid filtering-based recommender system for MOOC platforms is implemented to enhance user adaptation.

Key words: massive open online courses (MOOCs), e-learning, user profile, recommender system, content-based filtering, machine learning