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Automatic assessment of dialogic properties of classroom discourse would benefit several widespread classroom observation protocols. However, in classrooms with low incidences of dialogic discourse, assessment can be highly biased against detecting dialogic properties. In this paper, we present an approach to addressing this imbalanced class problem. Rather than perform classifications at the utterance level, we aggregate feature vectors to classify proportions of dialogic properties at the class-session level and achieve a moderate correlation with actual proportions, r(130) = 0.50, p < 0.001, CI[subscript 95][0.36, 0.61]. We show that this approach outperforms aggregating utterance level classifications, r(130) = 0.27, p = 0.001, CI[subscript 95][0.11, 0.43], is stable for both low and high dialogic classrooms, and is stable across both automatic speech recognition and human transcripts. [For the full proceedings, see ED596512.]

Mind wandering (MW) is a ubiquitous phenomenon characterized by an unintentional shift in attention from task-related to task-unrelated thoughts. MW is frequent during learning and negatively correlates with learning outcomes. Therefore, the next generation of intelligent learning technologies should benefit from mechanisms that detect and combat MW. As an initial step in this direction, we used eye-gaze and contextual information (e.g., time into session) to build an automated MW detector as students interact with GuruTutor -- an intelligent tutoring system (ITS) for biology. Students self-reported MW by responding to pseudorandom thought-probes during the tutoring session while a consumer-grade eye tracker monitored their eye movements. We used supervised machine learning techniques to discriminate between positive and negative responses to the probes in a student-independent fashion. Our best results for detecting MW (F[subscript 1] of 0.49) were obtained with an evolutionary approach to develop topologies for neural network classifiers. These outperformed standard classifiers (F[subscript 1] of 0.43 with a Bayes net) and a chance baseline (F[subscript 1] of 0.19). We discuss our results in the context of integrating MW detection into an attention-aware version of GuruTutor. [For the full proceedings, see ED592609.]

We investigate automatic detection of teacher questions from automatically segmented human-transcripts of teacher audio recordings collected in live classrooms. Using a dataset of audio recordings from 11 teachers across 37 class sessions, we automatically segment teacher speech into individual teacher utterances and code each as containing a teacher question or not. We trained supervised machine learning models to detect questions using high-level natural language features extracted from human transcriptions of a random subset of 1,000 segmented utterances. The models were trained and validated independently of the teacher to ensure generalization to new teachers. We are able to detect questions with a weighted F[subscript 1] score of 0.66, suggesting the feasibility of question detection on automatically segmented audio from noisy classrooms. We discuss the possibility of using automatic speech recognition to replace the human transcripts with an eye towards providing automatic feedback to teachers. [For the full proceedings, see ED592609.]