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- Title
Data Driven Automatic Feedback Generation in the iList Intelligent Tutoring System.
- Authors
FOSSATI, DAVIDE; DI EUGENIO, BARBARA; OHLSSON, STELLAN; BROWN, CHRISTOPHER; LIN CHEN
- Abstract
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 ol leedback 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.
- Subjects
INTELLIGENT tutoring systems; COMPUTER science education; TUTORING services; PSYCHOLOGICAL feedback; SOCIAL learning; DATA structures
- Publication
Technology, Instruction, Cognition & Learning, 2015, Vol 10, Issue 1, p5
- ISSN
1540-0182
- Publication type
Article