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CMU-HCII-10-107 Human-Computer Interaction Institute School of Computer Science, Carnegie Mellon University
Automated Adaptive Support for Peer Tutoring Erin Walker October 2010 Ph.D. Thesis
This dissertation focuses on two broad research questions: (1) Where and how
can intelligent tutoring approaches be applied to the development of ACLS,
and (2) Are there benefits to using existing domain models developed as
part of individual intelligent tutoring systems in ACLS? I began by
implementing a learning environment for peer tutoring as an addition
to a successful intelligent tutoring system, the Cognitive Tutor Algebra,
and evaluating the benefits of peer tutoring without adaptive support
(Phase 1). I then added adaptive support for peer tutors in giving
tutees correct help, and discovered that while peer tutors benefit from
reflecting on their partner's errors, they need additional support in giving
tutees conceptual help ( This work makes both technological and learning sciences contributions. The technological contributions involve demonstrating how individual intelligent tutoring approaches can be used to model collaboration, and what role intelligent tutoring components can play in collaborative models. For example, I have shown that the automated classification of peer tutor behaviors can be improved using problemsolving features, and that collaborative skills can be traced in the same way as problem-solving skills. This work makes learning sciences contributions by increasing understanding of the effects of adaptive support on student collaboration and learning. In two studies I have demonstrated that adaptive support, compared to fixed support controls, improves the quality of the help peer tutors give and improves their domain learning. As part of this work, I add to understanding of the cognitive and motivational mechanisms by which different types of adaptive support impact student collaboration. Overall, this dissertation demonstrates that adaptive collaborative learning support is a promising research direction for improving collaboration quality and domain learning. 193 pages
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