Automatic Analysis of Student Drawings in Chemistry Classes.

Automatic analyses of student drawings in chemistry education have the potential to support classroom teaching. To date, related work on handwritten chemical structures or formulas is limited to well-defined presentation formats, e.g., Lewis structures. However, the large variety of possible illustrations in student drawings in chemical education has not been addressed yet. In this paper, we present a novel approach to identify visual primitives in student drawings from chemistry classes. Since the field lacks suitable datasets for the given task, we introduce a method to synthetically create a dataset for visual primitives. We demonstrate how detected visual primitives can be used to automatically classify drawings according to a taxonomy of drawing characteristics in chemistry and physics. Our experiments show that (1) the detection of visual primitives in student drawings, and (2) the subsequent classification of chemistry- and physics-specific drawing characteristics is possible.