SKETCHMINER: Mining Learner-Generated Science Drawings with Topological Abstraction

Mining learner-generated sketches holds significant potential for acquiring deep insight into learners ’ mental models. Drawing has been shown to benefit both learning outcomes and engagement, and learners ’ sketches offer a rich source of diagnostic information. Unfortunately, interpreting learners ’ sketches—even sketches comprised of semantically grounded symbols—poses significant computational challenges. In this paper we describe SKETCHMINER, an educational sketch mining framework that automatically maps learners ’ symbolic sketches to topology-based abstract representations that are then analyzed with graph similarity metrics to perform automated assessment and misconception discovery. SKETCHMINER has been used to mine a corpus of symbolic science sketches created by upper elementary students in inquiry-based drawing episodes as they interact with an intelligent science notebook in the domain of physical science. Results of a study with SKETCHMINER suggest that it can correctly assess learners ’ symbolic sketches