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Data Driven Automatic Feedback Generation in the iList Intelligent Tutoring System
Davide Fossati, Barbara Di Eugenio, Stellan Ohlsson, Christopher Brown and Lin Chen

Based on our empirical studies of effective human tutoring, we developed an Intelligent Tutoring System, iList, that helps students learn linked lists, a challenging topic in Computer Science education. The iList system can provide several forms of feedback to students. Feedback is automatically generated thanks to a Procedural Knowledge Model extracted from the history of interaction of students with the system. This model allows iList to provide effective reactive and proactive procedural feedback while a student is solving a problem. We 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 students on the right path.

Keywords: Intelligent tutoring systems, interactive learning environments, educational data mining, feedback generation, procedural knowledge modeling, system evaluation, computer science education.

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