Personalised eLearning Recommendation system

Personalised eLearning Recommendation system

eLearning, or online learning, has reached every corner of the globe in this era of digitization. As a result of the COVID-19 pandemic, the value of eLearning has increased substantially. In eLearning recommendation systems, information overload, personalised suggestion, sparsity, and accuracy are a...

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Hauptverfasser: Kulkarni, Pradnya Vaibhav Kulkarni, Rai, Sunil Rai, Sachdeo, Rajneeshkaur Sachdeo, Kale, Rohini Kale
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creator Kulkarni, Pradnya Vaibhav Kulkarni
Rai, Sunil Rai
Sachdeo, Rajneeshkaur Sachdeo
Kale, Rohini Kale
description eLearning, or online learning, has reached every corner of the globe in this era of digitization. As a result of the COVID-19 pandemic, the value of eLearning has increased substantially. In eLearning recommendation systems, information overload, personalised suggestion, sparsity, and accuracy are all major problems. The correct eLearning Recommendation System is necessary to tailor the course recommendation according to the user's needs. To create this model, dataset of the User Profile and User Rating is needed. The User Profile dataset is created by using the Calyxpod programme to collect student profiles. User requirements are available through these profiles. The dataset obtained by gathering student comments following course completion is in the range of 1 (lowest) to 5 (highest).
doi_str_mv 10.21227/prva-qc11
format Dataset
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