Blair Lehman, Sidney D’Mello, Whitney Cade, Natalie Person
Expert human tutors are widely considered to be the gold standard for increasing student learning. While not every student has access to an expert tutor, it is possible to model intelligent tutoring systems after expert tutors. In an effort to achieve this goal, we have analyzed a corpus of 50 hours of one-to-one expert human tutoring sessions. This corpus was coded for speech acts (dialogue moves) and larger pedagogical strategies (dialogue modes). Using mixed-effects modeling, we found that expert tutors differentially used dialogue moves depending on the dialogue mode. Specifically, tutor posed questions, explanations, and motivational statements were predictive of different dialogue modes (e.g., Lecture, Scaffolding).
The final publication is available at Springer via https://doi.org/10.1007/978-3-642-30950-2_72.