Feedback Micro-engineering in EER-Tutor

Authors:
Konstantin Zakharov;Antonija Mitrovic;Stellan Ohlsson
Affiliations:
Intelligent Computer Tutoring Group, University of Canterbury, Christchurch, New Zealand;Intelligent Computer Tutoring Group, University of Canterbury, Christchurch, New Zealand;Department of Psychology, University of Illinois at Chicago
Venue:
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Year:
2005

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Abstract

Although existing educational systems are based on various learning theories, these theories are rarely used when developing feedback. Our research is based on the theory of learning from performance errors, which suggests that feedback should provide long and short-term learning advantages through revision of faulty knowledge in the context of learners' errors. We hypothesized that principled, theory-based feedback would have a positive impact on learning. To test the hypothesis we performed an experiment with EER-Tutor, an intelligent tutoring system that teaches database design. The results of the study support our hypothesis: the students who learned from theory-based feedback had a higher learning rate than their peers. We conclude that learning theories should be used to formulate design guidelines for effective feedback.