Coding Bias in the Use of Behavior Management Technologies: Uncovering Socio-technical Consequences of Data-driven Surveillance in Classrooms

Abstract

Digital educational technologies have been employed in classrooms to collect students' behavioral data in the hope of supporting teachers in identifying and correcting undesirable behaviors, which raises the concern of heightened surveillance in classrooms. We present a qualitative study of 20 K-8 teachers to understand their experiences and practices of using ClassDojo, a data-driven classroom behavior management intervention. Our analysis reveals a series of unintended socio-technical effects resulting from the use of ClassDojo in practice. In particular, the use of ClassDojo runs the risk of measuring, codifying, and simplifying the nuanced psycho-social factors that may drive children's behavior and performance, thereby serving as a "Band-Aid"for deeper issues. We discuss how this process could perpetuate existing inequality and bias in education. With the goals of spurring future design and mitigating these unintended effects, we take on the reflexive-interventionist approach and propose three considerations for designing and using future educational technologies: 1) provide context, 2) expose bias, and 3) challenge and reimagine what is normal.