Beyond the code-and-count analysis of tutoring dialogues

Authors:
Stellan Ohlsson;Barbara Di Eugenio;Bettina Chow;Davide Fossati;Xin Lu;Trina C. Kershaw
Affiliations:
University of Illinois at Chicago, IL, USA;University of Illinois at Chicago, IL, USA;University of Illinois at Chicago, IL, USA;University of Illinois at Chicago, IL, USA;University of Illinois at Chicago, IL, USA;University of Massachusetts at Dartmouth, MA, USA
Venue:
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Year:
2007

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Abstract

In this paper, we raise a methodological issue concerning the empirical analysis of tutoring dialogues: The frequencies of tutoring moves do not necessarily reveal their causal efficacy. We propose to develop coding schemes that are better informed by theories of learning; stop equating higher frequencies of tutoring moves with effectiveness; and replace ANOVAs and chi-squares with multiple regression. As motivation for our proposal, we will present an initial analysis of tutoring dialogues, in the domain of introductory Computer Science.