Over the years,due to the vulgarization of Information Technology,self-study took a major boost all over the academia.The Internet has gained a growing notoriety and it has made virtually,unlimited resources available for people to learn on their own and enhance their education.Selfeducation is becoming a popular way to engage students with what they are learning in class.These days,learners have access to so many resources that learning can happen anywhere,at any time-not just in the classroom.However,due to the seemingly limitless power of Information Technology in our era,learners tend to face some troubles in finding the accurate resources they need to sharpen their knowledge.This is due to an issue known as information overload.Information overload can be defined as the availability of a large number of information on a subject which can be at the origin of a lot of problems in learning such as lack of motivation,confusion,boredom etc.This issue led to the development of Recommender Systems（or Recommendation Systems）in order to provide accurate suggestions to users that might be of interest to them.Recommender Systems have been used successfully in E-Commerce and entertainment in the last decades.It is in this perspective,that we did this research in order to combine Recommender Systems and online learning.The goal behind this work is to analyze the requirements and to provide a design of a recommendation system based on its user profile in order to provide recommendations of books,tutorial videos and quizzes to self-taught learners to achieve their objectives.