Automatic modeling of procedural knowledge and feedback generation in a computer science tutoring system
Barbara Di Eugenio, Davide Fossati · 2009
This research takes place in the larger context of the study of one-on-one tutoring, a form of instruction that has been shown to be very effective. I conducted a study of human tutoring in the domain of Computer Science data structures, to understand which features and strategies of human tutoring are important for learning. I developed an Intelligent Tutoring System, iList, that helps students learn linked lists. One of the main advances in iList is the presence of a Procedural Knowledge Model automatically extracted from student data. This model allows iList to provide effective reactive and proactive procedural feedback while a student is solving a problem. I tested five different versions of iList, differing in the level of feedback they can provide, in multiple classrooms, with a total of more than 200 students. The evaluation study showed that iList is effective in helping students learn; students liked working with the system; and the feedback generated by the most sophisticated versions of the system is helpful in keeping the students on the right path.