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E-learning is generally defined as learning through the use of electronic media and electronic devices such as computers, laptop, CDs / DVDs and nowadays smart phones also. E-learning was started in early 90’s and appeared as a tough competitor to traditional learning system (Ahmad, 2012). In the past few years, it has brought an unseen revolution in universities all around the world. The Learning Management System software application developed to support e-learning has been rated as an effective tool for E-learning purpose to handles all the aspects of the administering, teaching, and learning process. With LMS’ interest in the concept of e-learning has been growing strongly because of its various advantages and interactive web technology development, but the problem with all current e-learning systems is they are closed systems and presents same information to all types of users. Therefore, “one size fits all” approach of e-learning faces challenges in such situations their performance is either low or they cannot perform at all. The prominent among the problems are static course selection and lack of personalization feature.
Since e-learning is changing the educational scenario very quickly, therefore new learning management system trends are coming with new features. Nowadays, a trend has been witnessed towards cloud-based LMS with the limitation being low-speed internet connectivity, personalizing learning environment (PLE) using recommender system approach which is becoming an important part of LMS (Czerkawski, 2014) Personalization is to tailor the needs according to the learner’s requirements and in the form of recommendations personalization has gained popularity and significant interest in day-to-day life, as people trust and seek others recommendations for decision making. Recommender systems use efficient Information retrieval techniques are being adopted in various fields like social networks, e-commerce, e-health, YouTube, and e-commerce sites, reflecting this social behavior to support users in making decisions for making a wise choice with little efforts (Hazra, 2015). An example of recommendation generation from user view point are shown in Figure 1.
Figure 1.
Stages traversed in information retrieval from the user point of view
Recommender systems are basically a subset or a division of information filtering concept, which shows an immense potential to help its users to create a personal learning environment and to identify most relevant and interesting items from a large number of items by suggesting it to a user based on their interest, domain, preferences, and ratings of other learners (Hoffman, 2016) Integrating such recommender systems in learning systems will be beneficial for both learners as well as for E-learning tools as such systems provide high potential to achieve personalization and will increase the functionality and effectiveness of E-learning systems by mitigating their weakness. The aim of all recommender systems is to provide a recommendation that will be favorably evaluated and accepted by its users.