Toward large-scale automated scoring of scientific visual models

Chee Wee Leong, Lei Liu, Rutuja Ubale, Lei Chen

Abstract: Visual models of scientific concepts drawn by students afford expanded opportunities for showing their understanding beyond textual descriptions, but also introduce other elements characterized by artistic creativity and complexity. In this paper, we describe a standardized framework for evaluation of scientific visual models by human raters. This framework attempts to disentangle the interaction between the scientific modeling skills and artistic skills of representing real objects of students, and potentially provides a fair and valid way to assess understanding of scientific concepts e.g. structure and properties of Matter. Additionally, we report ongoing efforts to build automated assessment models based on the evaluation framework. Preliminary findings suggest the promise of such an automated approach.