Document-level school lesson quality classification based on German transcripts

Analyzing large-bodies of audiovisual information with respect to discoursepragmatic categories is a time-consuming, manual activity, yet of growing importance in a wide variety of domains. Given the transcription of the audiovisual recordings, we propose to model the task of assigning discoursepragmatic categories as supervised machine learning task. By analyzing the effects of a wide variety of feature classes, we can trace back the discoursepragmatic ratings to low-level language phenomena and better understand their dependency. The major contribution of this article is thus a rich feature set to analyze the relationship between the language and the discoursepragmatic categories assigned to an analyzed audiovisual unit. As one particular application of our methodology, we focus on modelling the quality of lessons according to a set of discourse-pragmatic dimensions. We examine multiple lesson quality dimensions relevant for educational researchers, e.g. to which extent teachers provide objective feedback, encourage cooperation and pursue thinking pathways of students. Using the transcripts of real classroom interactions recorded in Germany and Switzerland, we identify a wide range of lexical, stylistic and discourse-pragmatic phenomena, which affect the perception of lesson quality, and we interpret our findings together with the educational experts. Our results show that especially features focusing on discourse and cognitive processes are beneficial for this novel classification task, and that this task has a high potential for automated assistance.