Data mining to generate individualised feedback

Abstract

Intelligent Tutoring Systems can be very expensive and complex to design, build and maintain. We explore the feasibility of adding automatic personalised feedback to an existing online learning system, by mining the student data collected by the system. This work was carried out on a web site in which students are taught programming basics in Python. Using 2008 and live 2009 data, the 2009 system generated hints to help students in topic areas they were found to be struggling with. We found that students who used the hinting system achieved significantly better results (26% higher marks) than those who did not, and stayed active on the site longer. A qualitative survey also revealed positive feedback from the students. © 2010 Springer-Verlag.