2026-09-20T17:13:40Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/770032022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Kimberle Koile.
Von Tish, Kelsey Leigh
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2013-02-14T15:37:13Z
2013-02-14T15:37:13Z
2012
2012
http://hdl.handle.net/1721.1/77003
825559392
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 81-82).
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.
by Kelsey Leigh Von Tish.
M.Eng.
82 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Interpretation and clustering of handwritten student responses
Thesis
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<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.</Abstract>
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