A proposed e-learning system facilitating recommendation using content tagging and student learning styles

The incommensurate teacher-student ratio and inability of teachers to work on weaknesses of each student, calls for the demand of development of an adaptive learning platform which can analyze and visualize the student's performance to recommend educational concepts, thereby making the app personalized for each student. Every e-learning system is designed with a goal to help the student to establish his/her goals and help them when needed. But, the learning style of each student is different, consequently, learning progression and pattern varies among students. A learning style is a characteristic of psychological and cognitive behavior of a user under a learning phase. Hence, a good e-learning system is one which not only recommends concept based on knowledge level but also recommends the type of learning material which will help the student to learn or acquire a skill in the best way. This paper proposes the foundation and operation of an e-learning system which suggests educational concepts and type of learning materials to enrolled students, based on their performance, learning style and learning progression in the system.