Promoting education: A state of the art machine learning framework for feedback and monitoring E-Learning impact

A serious impediment in E-Learning is that these systems seem to have failed to consider the advantages of the supervision of a teacher. Teachers are able to monitor the progress made by several students, irrespective of their learning abilities and attempt to channel all students towards a common learning goal. E-Learning systems today don´t possess a monitoring component. Most approaches customize content to suit differing learning abilities, resulting in different learning goals. However, this study attempts to apply machine learning methods that customizes not the content, but the presentation of the content assuming almost common learning goals - just like how a teacher would modify the content presentation, if some aspects are not clear to students based on their feedback. The primary challenge towards developing such a monitoring system is to decide what aspects of the interaction are to be monitored and how these are to interpreted as feedback with actionable insights - that is, to decide the learning schema, and then apply learning algorithms to gauge the interest or disinterest of the learner in the content presented.