Exploring Effective Dialogue Act Sequences in One-on-one Computer Science Tutoring Dialogues

We present an empirical study of one-on-one human tutoring dialogues in the domain of Computer Science data structures. We are interested in discovering effective tutoring strategies, that we frame as discovering which Dialogue Act (DA) sequences correlate with learning. We employ multiple linear regression, to discover the strongest models that explain why students learn during one-on-one tutoring. Importantly, we define "flexible" DA sequence, in which extraneous DAs can easily be discounted. Our experiments reveal several cognitively plausible DA sequences which significantly correlate with learning outcomes.

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