Model-driven assessment of learners in open-ended learning environments

Abstract

Open-ended learning environments (OELEs) provide students with opportunities to take part in authentic and complex problem-solving tasks. However, many students struggle to succeed in such complex learning endeavors. Without support, these students often use system tools incorrectly and adopt suboptimal learning strategies. However, providing adaptive support to students in OELEs poses significant challenges, and relatively few OELEs provide students with adaptive support. This paper presents the initial development of a systematic approach for interpreting and evaluating learner behaviors in OELEs called model-driven assessments, which uses a model of the cognitive and metacognitive processes important for completing the open-ended learning task. The model provides a means for both classifying and assessing students' learning behaviors while using the system. An evaluation of the analysis technique is presented in the context of Betty's Brain, an OELE designed to help middle school students learn about science.