Thursday, June 20, 2019

SRL PhD Dissertation defense Paul Taele. Thursday July 18. Title: A Sketch Recognition-Based Intelligent Tutoring System for Richer Instructor-Like Feedback on Chinese Characters

Dissertation Defense
Friday, September 27, 2019
2:00pm Engineering Activities Building C, Room 112C

Title: A Sketch Recognition-Based Intelligent Tutoring System for Richer Instructor-Like Feedback on Chinese Characters 

Abstract: Students wishing to achieve strong fluency in East Asian languages such as Chinese and Japanese must master various language skills such as the reading and writing of those languages' non-phonetic symbols of Chinese characters. For such students with English fluency learning an East Asian language as a foreign language and with only primary fluency in English, mastery of such languages' written component is challenging due to vastly distinct linguistic differences in reading and writing. In this dissertation, I developed a sketch recognition-based intelligent tutoring system for providing richer assessment and feedback that emulates human language instructors, specifically for novice students' introductory course study of East Asian languages and their written Chinese characters. The system relies on various sketch recognition heuristics for evaluating the performance of students' writing technique of introductory Chinese characters through features such as metric scores and visual animations. From evaluating the proposed system from instructor feedback for classroom students and self-study learners, I provide a stylus-driven solution for novice language students to study and practice introductory Chinese characters with deeper assessment levels, so that they may have richer feedback to improve their writing performance.

Biography: Paul Taele is currently a doctoral candidate studying Computer Science at Texas A&M University, and a member of the Sketch Recognition Lab. He received his dual Bachelor of Science in Computer Sciences and in Mathematics at the University of Texas at Austin, and his Master of Science in Computer Science at Texas A&M University. His research interests lie at the intersection of human-computer interaction and artificial intelligence, specifically in intelligent tutoring systems for educational domains in languages, music, and engineering.

Adviser: Dr. Tracy Hammond

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