Tools to Support Data-driven Reflective Learning

Abstract

Reflection is a process of "critical review" of previous experiences to inform future action. Reflection has its origins in design and engineering but has gained traction in education as well. Reflective learning affords students the opportunity to reflect critically on their learning and develop metacognitive skills. Scaffolding is necessary as students adopt a reflective practice, but few tools support this process. Our prior work with teams suggests that students have difficulty estimating their turn-taking behaviors during peer learning activities and reflecting on such misconceptions might be detrimental to the development of social and metacognitive skills. I propose two tools that support data-driven student reflection: BloomMatrix and IneqDetect. BloomMatrix allows students to encode their perceived cognitive processes in an interactive version of Bloom's Taxonomy Matrix. This supports individual reflection, and an aggregated peer heatmap shows other students' perceptions. IneqDetect uses lapel microphones and signal processing to encode live conversations into turn-taking behaviors. In each case, students can reflect about themselves and also about others.