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

SRL MS Thesis Defense of Megha Yadav. Monday, June 3. Title: Mitigating Public Speaking Anxiety Using Virtual Reality and Population-Specific Models

Thesis Defense

Monday, June 3

Title:
Mitigating public speaking anxiety using virtual reality and population-specific models.

Abstract: Public speaking is essential in effectively exchanging ideas, persuading others, and making a tangible impact. Yet, public speaking anxiety (PSA) ranks as a top social phobia among many people. This research utilizes wearable technologies and virtual reality (VR) to expose individuals to PSA stimuli and quantify their PSA levels via group-based machine learning models. These machine learning models leverage common information across individuals and fine-tune their decisions based on specific individual and contextual factors. In this way, prediction decisions would be made for clusters of people with common individual-specific factors which would benefit the overall system accuracy. Findings of this study will enable researchers to better understand ante-decedents and causes of PSA contributing to behavioral interventions using VR.

Bio: Megha Yadav is pursuing her Master’s degree in Computer Science in the Department of Computer Science & Engineering. She received her Bachelor’s degree in Computer Science from Manipal Institute of Technology in India in 2013. Megha’s research interest lies in exploring machine learning and deep learning techniques within the health care domain.

Co-Advisor: Dr. Tracy Hammond

SRL MS Thesis Defense of Siddharth Subramaniyam. Monday June 3. Title: Sketch Recognition Based Clarification for Eye Movement Biometrics in Virtual Reality

Thesis Defense
Monday, June 3

Title:  Sketch Recognition Based Classification for Eye Movement Biometrics in Virtual Reality

Abstract: 
Biometrics is an active area of research in the HCI, pattern recognition, and machine learning communities. In addition to various physiological features such as fingerprint, DNA, and facial recognition, there has been an interest in using behavioral biometric modalities such as gait, eye movement patterns, keystroke dynamics signature, etc. In this work, we explore the effectiveness of using eye movement as a biometric modality by treating it as a sketch and develop features using sketch recognition techniques. For testing our methods, we built a system for authentication in virtual reality (VR) that combines eye movement biometric with passcode based authentication for an additional layer of security against spoofing attacks.


Bio:Siddharth is currently a Masters student in Computer Science at Texas A&M University, working in the Sketch Recognition Lab. His research interests are in computer human interaction, especially the application of statistics and machine learning to understand human perception and cognitive behavior.

Advisor: Dr. Tracy Hammond

SRL MS Thesis Defense of Sharmistha Maity. Thursday May 30. Title: Combining Paper -Pencil Techniques with Immediate Feedback for Learning Chemical Drawings

Thesis Defense
Thursday, May 30

Title:

Combining Paper-Pencil Techniques with Immediate Feedback for Learning Chemical Drawings

Abstract:
Introductory chemistry courses teach the process of drawing basic chemical molecules with the use of Lewis dot diagrams. Many beginner students, however, have difficulty in mastering these diagrams. While several computer applications are being developed to help students learn Lewis dot diagrams, there is a potential hidden benefit from paper and pencil that many students may not realize. Sketch
recognition has been used to identify advanced chemical diagrams, however using the recognition in an educational setting requires a focus beyond identifying the final drawing. The goal of this research is to infer whether paper-pencil techniques provide educational benefits for learning Lewis dot diagrams. An analysis of pre-post assessments
shows how combining sketch recognition of paper-pencil techniques and immediate feedback allows
greater benefits for students with a basic chemistry understanding.

Biography:
Sharmistha Maity is currently a Masters student studying Computer Science at Texas A&M University. She
received her Bachelors in Electrical Engineering at The University of Texas at Austin, and her research
interests include Human-Centered Computing, Artificial Intelligence, and Cognitive Science. As a graduate
student in the Sketch Recognition Lab, she is studying the integration of educational psychology and
computer science, and how it can be combined to improve education that effectively reaches a wider
audience and increases the motivation to learn.

Advisor: Dr. Tracy Hammond