Towards Scalable Assessment of Performance-Based Skills: Generalizing a Detector of Systematic Science Inquiry to a Simulation with a Complex Structure

摘要

There are well-acknowledged challenges to scaling computerized performance-based assessments. One such challenge is reliably and validly identifying ill-defined skills. We describe an approach that leverages a data mining framework to build and validate a detector that evaluates an ill-defined inquiry process skill, designing controlled experiments. The detector was originally built and validated for use with physical science simulations that have a simpler, linear causal structure. In this paper, we show that the detector can be used to identify demonstration of skill within a life science simulation on Ecosystems that has a complex underlying causal structure. The detector is evaluated in three ways: 1) identifying skill demonstration for a new student cohort, 2) handling the variability in how students conduct experiments, and 3) using it to determine when students are off-track before they finish collecting data.