Identifying students' inquiry planning using machine learning
Orlando Montalvo, Ryan S. Baker, Michael São Pedro, Adam Nakama, Janice D. Gobert · 2010
Abstract. This research investigates the detection of student meta-cognitive planning processes in real-time using log tracing techniques. We use fine and coarse-grained data distillation, in combination with coarse-grained text replay coding, in order to develop detectors for students ’ planning of experiments in Science Assistments, an assessment and tutoring system for scientific inquiry. The goal is to recognize student inquiry planning behavior in real-time as the student conducts inquiry in a micro-world; the eventual goal is to provide real-time scaffolding of scientific inquiry. 1