Machine Learning-Enabled Automated Feedback: Supporting Students’ Revision of Scientific Arguments Based on Data Drawn from Simulation
A design study was conducted to test a machine learning (ML)-enabled automated feedback system developed to support students’ revision of scientific arguments using data from published sources and simulations. This paper focuses on three simulation-based scientific argumentation tasks called Trap, Aquifer, and Supply. These tasks were part of an online science curriculum module addressing groundwater systems for secondary school students.
This paper focuses on three simulation-based scientific argumentation tasks called Trap, Aquifer, and Supply. These tasks were part of an online science curriculum module addressing groundwater systems for secondary school students.