2026-09-20T00:39:37Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/664052022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Kimberle Koile.
Chao, Neil E
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2011-10-17T21:22:21Z
2011-10-17T21:22:21Z
2011
2011
http://hdl.handle.net/1721.1/66405
755081797
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 95-96).
Handwriting recognition for text and numbers has existed for Tablet PCs and specialty devices for almost a decade. The use of such software in a classroom can shorten the latency between answers to exercises and teacher feedback. While text and number inputs have already been well explored, graphical examples and math problems are a relatively new territory for recognition software. Under the guidance of the NSFfunded INK-12 Project, I explored the impact of structure on the ability of Ink-Analysis Handwriting Recognition to understand students' solutions math and science exercises, hoping to find the ideal balance between accurate grading and minimal human effort. I tested a prototype system aimed at supporinting "virtual-teacher-guided" learning in elementary school classrooms in the Greater Boston area.
by Neil E. Chao.
M.Eng.
131 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Analyzing the impact of structure in handwriting recognition software
Thesis
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Publication
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<Abstract>Handwriting recognition for text and numbers has existed for Tablet PCs and specialty devices for almost a decade. The use of such software in a classroom can shorten the latency between answers to exercises and teacher feedback. While text and number inputs have already been well explored, graphical examples and math problems are a relatively new territory for recognition software. Under the guidance of the NSFfunded INK-12 Project, I explored the impact of structure on the ability of Ink-Analysis Handwriting Recognition to understand students' solutions math and science exercises, hoping to find the ideal balance between accurate grading and minimal human effort. I tested a prototype system aimed at supporinting "virtual-teacher-guided" learning in elementary school classrooms in the Greater Boston area.</Abstract>
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