Monitoring a MOOC: The Analytics Module of a MOOC Management System

Abstract

Online courses offer interesting opportunities to observe student behavior and learning patterns. All the interactions of the student from login to logout are recorded. A proper analysis of this data can reveal the engagement level of the student with the course, with the course content and with other learners. In the absence of face-to-face-interactions with students, this is an invaluable feedback to the Instructor. mooKIT is a versatile MOOC Management System designed with a state-of-the-art learning analytics. mooKIT logs all events that happen during a user session and provides a powerful graphical user interface for the Instructor to make sense out of this. The analytics are presented at a lecture level (like what is the viewing pattern of a lecture), and at an individual student level, on a time scale. These can be used to figure out the difficulty level of each lecture and for each student. Analysis of the Forum activities, like volume of questions, interaction among students, the sentiment of the comments etc. can be used to get a good understanding of the social behavior and comfort level of the students. By being able to predict the students who are likely to drop out from the course, the system enables the Instructor to take remedial actions. One can use the predictive analytics to configure the machine for hosting. In this paper, we discuss the kind of learning analytics that are possible using mooKIT as an example. // Paper ID 543