Engineering Commons^™

Leveraging Machine-Learned Detectors Of Systematic Inquiry Behavior To Estimate And Predict Transfer Of Inquiry Skill, Ryan Baker, Michael Sao Pedro, Janice Gobert, Orlando Montalvo, Adam Nakama

Ryan S.J.d. Baker

We present work toward automatically assessing and estimating science inquiry skills as middle school students engage in inquiry within a physical science microworld. Towards accomplishing this goal, we generated machine‐learned models that can detect when students test their articulated hypotheses, design controlled experiments, and engage in planning behaviors using two inquiry support tools. Models were trained using labels generated through a new method of manually hand‐coding log files, “text replay tagging”. This approach led to detectors that can automatically and accurately identify these inquiry skills under student‐level cross‐validation. The resulting detectors can be applied at run‐time to drive scaffolding intervention. …