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<p>In this rapidly growing field of education, the demand of E- learning has become increasingly evident. The digital era’s transformative impact on various sectors has necessitated a paradigm shift in traditional teaching methods. If not a demand, it did become an utter necessity after the COVID-19 pandemic. E-Learning emerged as a critical tool for ensuring continuity in education during periods of disruption, showcasing its indispensable role in the face of unforeseen challenges. Moreover, the need in the flexibility of learning schedules have led to the adoption of E-Learning. However, as the saying goes, “Quality over Quantity”, the successful implementation of E-Learning relies on addressing issues such as digital access disparities and ensuring the quality of online educational content. Recommendation Systems imhave been a matter of talk for the past 5–10 years. However, something which always has been the concern is improving the accuracy of recommendation to users. This paper explores the imperative of E-Learning, focusing on the evolving requirements in the educational landscape. It involves a case study which incorporates recommending based on user preferences and analyzes the factors which affects the user preferences. The F1 score of the designed model stands at around 0.8 over the traditional models which have a score in the range of 0.58–0.66. It focusses on the adoption of a smart recommender system which helps in improving the quality of E-Learning.</p>

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Smart Recommendation System in E-Learning Using Machine Learning and Data Analytics

  • 期刊论文

  • 发表日期：

    2025年7月29日

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  • Ankan Dey,
  • Ahan Ganguly,
  • Indranil Roy Banik,
  • Sourik Bhuiya,
  • Subhabrata Sengupta,
  • Rupayan Das

摘要

In this rapidly growing field of education, the demand of E- learning has become increasingly evident. The digital era’s transformative impact on various sectors has necessitated a paradigm shift in traditional teaching methods. If not a demand, it did become an utter necessity after the COVID-19 pandemic. E-Learning emerged as a critical tool for ensuring continuity in education during periods of disruption, showcasing its indispensable role in the face of unforeseen challenges. Moreover, the need in the flexibility of learning schedules have led to the adoption of E-Learning. However, as the saying goes, “Quality over Quantity”, the successful implementation of E-Learning relies on addressing issues such as digital access disparities and ensuring the quality of online educational content. Recommendation Systems imhave been a matter of talk for the past 5–10 years. However, something which always has been the concern is improving the accuracy of recommendation to users. This paper explores the imperative of E-Learning, focusing on the evolving requirements in the educational landscape. It involves a case study which incorporates recommending based on user preferences and analyzes the factors which affects the user preferences. The F1 score of the designed model stands at around 0.8 over the traditional models which have a score in the range of 0.58–0.66. It focusses on the adoption of a smart recommender system which helps in improving the quality of E-Learning.