Text Mining and Visual Analytics for MOOCs Conversations

The field of education is witnessing dramatic changes with the advent of Massive Open Online Courses (MOOCs), in which large numbers of students can take courses on the Web. However, in spite of their initial success, MOOC developers have been frustrated by the fact that many students do not complete the courses, because they often feel they are not sufficiently engaged. In this project, we aim to address this problem in two ways: on the one hand by providing online learners with a more meaningful and engaging dialogue experience in MOOC discussion forums; on the other hand by providing teachers with effective interfaces to better understand what students are discussing about in these forums. While working on this project, the intern will become familiar with existing tools for extracting information from conversations and for effectively visualizing the extracted information. He will also learn how to adapt and apply these tools in the new domain of MOOCs. As a result of the project, the partner organization will benefit through exposure to innovative approaches to textual analysis, and will thereby be able to provide its users with more useful information, further differentiating itself in the marketplace.

Faculty Supervisor:

Giuseppe Carenini

Student:

Jordon Johnson

Partner:

Prollster

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program: