Adaptive learning system recommending learning material in a more personalized manner

Abstract

The education system is drastically changing around the world, a digital approach to the provision of education has taken a huge stance and is helping students all around the world when It comes to studying any subject matter. There are several online learning platforms through which anyone can get the desired learning material and study. Throughout all the learning platforms online, personalisation of material has become a key factor when it comes to providing material to users. This helps the platform gain more usability and improve satisfactory levels of students as it gives them a personalised experience when it comes to studying. This research study tries to understand some of the added parameters that could be taken into consideration when it comes to further personalising the learning content that is provided to the student. Parameters such as “Subject Matter”, “Preferred Learning Style”, and “Current State of Knowledge” are taken into consideration in this project in order to make the process of looking for leaning material online more efficient and improving the personalisation experience to the students. For the purpose of this research study, a limited dataset that included the parameters mentioned above, and for the prototype, URL’s of learning material from online sources such as YouTube and W3 School were used in order to generate the output for the given requirements. It was deemed effective and time saving when It comes to adding further personalisation with the use of the parameters taken into consideration in this project.