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A Pilot Study Investigating Stakeholder Subjectivity in Collaborative Dialog Analysis

Qualitative research in education relies on“ground truth" codes or labels generated by having a trained or expert coder code observations in data such as student dialog. Although rigorous validity checks are a part of the coding process, there is limited research investigating how and to what extent, this notion of the ground truth is influenced by inherent task subjectivity. This paper presents a pilot study of task subjectivity centered around the phenomenon of verbal off-task behavior. The context for this study is real-world small-group collaborative conversations among three to five students in a middle-school science classroom. To investigate how stakeholders such as teachers and students show subjectivity in approaching this task, we recruit five teachers from the Prolific online platform, and five students from local middle and high schools as annotators of off-task speech. We show that teachers, students, and expert coders differ in their perception of off-task speech, with some of these differences being systematic. Drawing upon recent research in machine learning and natural language processing, we then outline the potential benefits of collecting and modeling a range of codes that explicitly represent the subjective perspectives of a diverse set of coders.