Collaborative E-Learning Application with Course Recommendation in Cloud Computing

N. Venkatesh Naik and K. Madhavi
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N. Venkatesh Naik: Department of CSE, JNTUA, Jawaharlal Nehru Technological University, Anantapur, Andhra Pradesh 515002, India
K. Madhavi: Department of CSE, JNTUACEA, Jawaharlal Nehru Technological University, Anantapur, Andhra Pradesh 515002, India

Journal of Information & Knowledge Management (JIKM), 2024, vol. 23, issue 06, 1-23

Abstract: Abstract.Cloud computing is quickly expanding, with applications in practically every industry, including education. E-learning systems often necessitate a large number of hardware and software resources. Many educational institutions cannot afford such investments, thus cloud computing is the best solution. Here, the Matrix Factorisation-based maximum rate recommendation system (MatFac-Maxirate RS) is utilised to recommend the courses for students to choose their career. According to user access, the e-learning application server is acquired from the E-Khool dataset which is subjected to learner or course agglomerative matrix calculation. The E-learning application server is executed based on Minkowski and Kumar Hasebrook’s distance to retrieve learner preference items. The recommended course having the maximum rating is considered which is forecasted with matrix factorisation considering the course ID and learner ID. The MatFac-Maxirate RS generated the finest efficacy with the best precision of 88.9%, recall of 88.2% and F-measure of 87.5%.

Keywords: Cloud services; collaborative E-learning; course recommendation; E-learning; deep fuzzy clustering (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219649224500886

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