Interpretation and clustering of handwritten student responses

Abstract

This thesis presents an interpretation and clustering framework for handwritten student responses on tablet computers. The ink analysis system is able to capture and interpret digital ink strokes for many types of classroom exercises, including graphs, number lines, and fraction shading problems. By approaching the problem with both online and offline ink interpretation methods, relevant information is extracted from sets of ink strokes to produce a representation of a student's answer. A clustering algorithm is then used to group similar student responses. Overall, this approach makes it easier for teachers to view a set of responses and subsequently supply feedback to his or her students.