Analyzing Frequent Sequential Patterns of Learning Behaviors in Concept Mapping

Computer-based concept mapping learning environments can produce large amounts of data on student interactions. The ability to automatically extract common interaction patterns and distinguish between effective and ineffective interactions creates opportunities for researchers to calibrate feedback and assistance to better support student learning. In this paper, we present an exploratory workflow that assesses and compares student learning behaviors with concept maps. This workflow employs a sequential pattern mining technique to classify interaction patterns among students and determine specific behavior patterns that lead to better learning outcomes.