Noticing Relevant Feedback Improves Learning in an Intelligent Tutoring System for Peer Tutoring

Erin Walker, Nikol Rummel, Sean Walker, Kenneth R. Koedinger

Intelligent tutoring techniques can successfully improve student learning from collaborative activities, but little is known about why and under what contexts this support is effective. We have developed an intelligent tutor to improve the help that peer tutors give by encouraging them to explain tutee errors and provide more conceptual help. In previous work, we have shown that adaptive support from this “tutor” tutor improves student learning more than randomly selected support. In this paper, we examine this result, looking more closely at the feedback students received, and coding it for relevance to the current situation. Surprisingly, we find that the amount of relevant support students receive is not correlated with their learning; however, there is a positive correlation with learning and students noticing relevant support, and a negative correlation with learning and students ignoring relevant support. Designers of adaptive collaborative learning systems should focus not only on making support relevant, but also engaging.

The final publication is available at Springer via