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.]