Understanding Learning Progressions via Automatic Scoring of Visual Models

The modern reliance on technological advances has spurred a focus on improving scientific education. Fueled by this interest, novel methods of testing students’ understanding of scientific concepts have been developed. One of these is visual modeling, an assessment method which allows for non-textual evaluation that incorporates previously difficult factors to test,such as complexity and creativity. Although visual models have been shown to effectively measure conceptual understanding, there has been a logistical barrier of scaling due to the infeasibility of grading large amounts of them by hand. This thesis proposes a system that can solve this issue by automatically grading visual models. A host of unsupervised and supervised computer vision techniques are utilized in order to classify shapes in visual models, extract relevant features, and, ultimately, assign a Learning Progression score to each model. Examples of the techniques used are a novel way to determine the orientation of Arrows and a Cascaded Voting System for shape classification. The results of the automatic grading system proposed in this thesis outperform previous methods and lay the foundation for future improvements. The resulting findings show great promise for directly solving the scaling issue, thereby making visual model assessments a practical tool for widespread use.Master of ScienceData Science, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/154796/1/Ari Sagherian Final Thesis.pdfDescription of Ari Sagherian Final Thesis.pdf : Thesi