Detecting Metacognitive Strategies through Performance Analyses in Open-­­Ended Learning Environments

Abstract

The detection and analysis of students’ domain-specific and metacognitive strategy use in Open-Ended Learning Environments (OELE) is a necessary step to support their learning and problem solving through contextualized scaffolding. We present an analysis of students’ performance from information captured in log files in UrbanSim, a turn-based simulation environment for counterinsurgency training. We illustrate the benefits of this approach within a task-model framework. Our overall goals are to implement a generalizable detection and adaptive scaffolding framework in an extended version of the GIFT tutoring system developed at ARL.