I learn from you, you learn from me: How to make iList learn from students

We developed a new model for iList, our system that helps students learn linked list. The model is automatically extracted from past student data, and allows iList to track students’ problem-solving behavior in order to provide targeted feedback. We evaluated the new model both intrinsically and extrinsically. We show that the model can match most student actions after a relatively small sequence of observations, and that iList can effectively use the new student tracker to provide feedback and help students learn. Keywords. Knowledge Representation, Student Modeling, Feedback Generation

• Type: Conference
• Year: 2009
• Where: AIED
• Authors: Davide Fossati, Barbara Di Eugenio, Stellan Ohlsson, Christopher W. Brown, Lin Chen, David G. Cosejo
• Keywords: AIED 2009, Artificial Intelligence, Past Student Data, Student, Targeted Feedback