Director: Dr. Tracy Anne Hammond

# SRL Dissertations and Theses

## Master's Theses

 2017 Tianshu Chu. 2017. "A Sketch-based Educational System for Learning Chinese Handwriting." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: December 2017. Advisor: Tracy Hammond. First Position: Microsoft Redmond. ISBN: ORCID id: 0000-0002-9497-058X. http://etd.tamu.edu/advisor/NFqESH4iG4/review Show Abstract: Learning Chinese as a Second Language (CSL) is a difficult task for students in English-speaking countries due to the large symbol set and complicated writing techniques. Traditional classroom methods of teaching Chinese handwriting have major drawbacks due to human experts' bias and the lack of assessment on writing techniques. In this work, we propose a sketch-based educational system to help CSL students learn Chinese handwriting faster and better in a novel way. Our system allows students to draw freehand symbols to answer questions, and uses sketch recognition and AI techniques to recognize, assess, and provide feedback in real time. Results have shown that the system reaches a recognition accuracy of 86% on novice learners' inputs, higher than 95% detection rate for mistakes in writing techniques, and 80.3% F-measure on the classification between expert and novice handwriting inputs. Show BibTex@mastersthesis{tianshuchu2017Master'sTheses, type = {{MS Master's Thesis}}, author = {Chu, Tianshu}, title = {A Sketch-based Educational System for Learning Chinese Handwriting}, year = {2017}, month = {December}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0002-9497-058X},} 2017 Jung In Koh. 2017. "Developing a Hand Gesture Recognition System for Mapping Symbolic Hand Gestures to Analogous Emoji in Computer-Mediated Communication." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: June 2017. Advisor: Tracy Hammond. First Position: TAMU Phd Student. Show Abstract: Recent trends in computer-mediated communications (CMC) have not only led to expanded instant messaging (IM) through the use of images and videos, but have also expanded traditional text messaging with richer content, so-called visual communication markers (VCM) such as emoticons, emojis, and stickers. VCMs could prevent a potential loss of subtle emotional conversation in CMC, which is delivered by nonverbal cues that convey affective and emotional information. However, as the number of VCMs grows in the selection set, the problem of VCM entry needs to be addressed. Additionally, conventional ways for accessing VCMs continues to rely on input entry methods that are not directly and intimately tied to expressive nonverbal cues. One such form of expressive nonverbal that does exist and is well-studied come in the form of hand gestures. In this work, I propose a user-defined hand gesture set that is highly representative to VCMs and a two-stage hand gesture recognition system (feature-based, shape based) that distinguishes the user-defined hand gestures. The goal of this research is to provide users to be more immersed, natural, and quick in generating VCMs through gestures. The idea is for users to maintain the lower-bandwidth online communication of text messaging to largely retain its convenient and discreet properties, while also incorporating the advantages of higher-bandwidth online communication of video messaging by having users naturally gesture their emotions that are then closely mapped to VCMs. Results show that the accuracy of user-dependent is approximately 86% and the accuracy of user independent is about 82%. Show BibTex@mastersthesis{junginkoh2017Master'sTheses, type = {{MS Master's Thesis}}, author = {Koh, Jung In}, title = {Developing a Hand Gesture Recognition System for Mapping Symbolic Hand Gestures to Analogous Emoji in Computer-Mediated Communication}, year = {2017}, month = {June}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond},} 2017 Seth Polsley. 2017. "Identifying outcomes of care from medical records to improve doctor-patient communication." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: June 2017. pp. 81. Advisor: Tracy Hammond. First Position: TAMU PhD Student. ISBN: ORCID id: 0000-0002-8805-8375. http://etd.tamu.edu/advisor/cySA4eYIljA/review Show Abstract: Between appointments, healthcare providers have limited interaction with their patients, but patients have similar patterns of care. Medications have common side effects; injuries have an expected healing time; and so on. By modeling patient interventions with outcomes, healthcare systems can equip providers with better feedback. In this work, we present a pipeline for analyzing medical records according to an ontology directed at allowing closed-loop feedback between medical encounters. Working with medical data from multiple domains, we use a combination of data processing, machine learning, and clinical expertise to extract knowledge from patient records. While our current focus is on technique, the ultimate goal of this research is to inform development of a system using these models to provide knowledge-driven clinical decision-making. Show BibTex@mastersthesis{sethpolsley2017Master'sTheses, type = {{MS Master's Thesis}}, author = {Polsley, Seth}, title = {Identifying outcomes of care from medical records to improve doctor-patient communication}, pages = {81}, year = {2017}, month = {June}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0002-8805-8375},} 2017 Josh Cherian. 2017. "Recognition of Everyday Activities through Wearable Sensors and Machine Learning." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: May 2017. Advisor: Tracy Hammond & Theresa Maldonado. First Position: TAMU PhD Student. ISBN: ORCID id: 0000-0002-7749-2109. http://etd.tamu.edu/advisor/cilo3pwJA/review Show Abstract: Over the past several years, the use of wearable devices has increased dramatically,primarily for fitness monitoring, largely due to their greater sensor reliability, increasedfunctionality, smaller size, increased ease of use, and greater affordability.These devices have helped many people of all ages live healthier lives and achieve their personal fitnessgoals, as they are able to see quantifiable and graphical results of their efforts every step of the way (i.e. in real-time). Yet, while these device systems work well within the fitnessdomain, they have yet to achieve a convincing level of functionality in the larger domainof healthcare. As an example, according to the Alzheimer’s Association, there are currently approxi-mately 5.5 million Americans with Alzheimer’s Disease and approximately 5.3 million ofthem are over the age of 65, comprising 10% of this age group in the U.S. The economictoll of this disease is estimated to be around $259 billion. By 2050 the number of Amer-icans with Alzheimer’s disease is predicted to reach around 16 million with an economictoll of over$1 trillion. There are other prevalent and chronic health conditions that arecritically important to monitor, such as diabetes, complications from obesity, congestiveheart failure, and chronic obstructive pulmonary disease (COPD) among others. The goal of this research is to explore and develop accurate and quantifiable sensingand machine learning techniques for eventual real-time health monitoring by wearabledevice systems. To that end, a two-tier recognition system is presented that is designed to identify health activities in a naturalistic setting based on accelerometer data of commonactivities. In Tier I a traditional activity recognition approach is employed to classify shortwindows of data, while in Tier II these classified windows are grouped to identify instancesof a specific activity. Everyday activities that were explored in this research include brushing one’s teeth, combing one’s hair, scratching one’s chin, washing one’s hands,taking medication, and drinking. Results show that an F-measure of 0.83 is achievablewhen identifying these activities from each other and an F-measure of of 0.82 is achievablewhen identifying instances of brushing teeth over the course of a day. Show BibTex@mastersthesis{joshcherian2017Master'sTheses, type = {{MS Master's Thesis}}, author = {Cherian, Josh}, title = {Recognition of Everyday Activities through Wearable Sensors and Machine Learning}, year = {2017}, month = {May}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond \& Theresa Maldonado, ISBN: ORCID id: 0000-0002-7749-2109},} 2017 Aqib Bhat. 2017. "Sketchography - Automatic grading of map sketches for geography education." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: May 2017. Advisor: Tracy Hammond. First Position: Amazon. ISBN: ORCID id: 0000-0003-2718-7736. http://hdl.handle.net/1969.1/161656 https://etd.tamu.edu/advisor/4J3pxntdZG4/review Show Abstract: Geography is a vital classroom subject that teaches students about the physical features of the planet we live on. Despite the importance of geographic knowledge, almost 75% of 8th graders scored below proficient in geography on the 2014 National Assessment of Educational Progress. Sketchography is a pen-based intelligent tutoring system that provides real-time feedback to students learning the locations, directions, and topography of rivers around the world. Sketchography uses sketch recognition and artificial intelligence to understand the user’s sketched intentions. As sketches are inherently messy, and even the most expert geographer will draw only a close approximation of the river’s flow, data has been gathered from both novice and expert sketchers. This data, in combination with professors’ grading rubrics and statistically driving AI-algorithms, provide real-time automatic grading that is similar to a human grader’s score. Results show the system to be 94.64% accurate compared to human grading. Show BibTex@mastersthesis{aqibbhat2017Master'sTheses, type = {{MS Master's Thesis}}, author = {Bhat, Aqib}, title = {Sketchography - Automatic grading of map sketches for geography education}, year = {2017}, month = {May}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0003-2718-7736, \url{http://hdl.handle.net/1969.1/161656}},} 2017 JorgeIvan Camara. 2017. "Flow2Code - From Hand-drawn Flowchart to Code Execution." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: May 2017. Advisor: Tracy Hammond. First Position: Yellowme & Lecturer at Universidad Autónoma de Yucatán. ISBN: ORCID id: 0000-0003-0922-5508. http://etd.tamu.edu/advisor/7hQjuEzCLVM/review Show Abstract: Flowcharts play an important role when learning to program by conveying algorithms graphically and making them easy to read and understand. When learning how to code with flowcharts and transitioning between the two, people often use computer based software to design and execute the algorithm conveyed by the flowchart. This requires the users to learn how to use the computer-based software first, which often leads to a steep learning curve. We claim that the learning curve can be decremented by using off-line sketch recognition and computer vision algorithms on a mobile device. This can be done by drawing the flowchart on a piece of paper and using a mobile device with a camera to capture an image of the flowchart. Flow2Code is a code flowchart recognizer that allows the users to code simple scripts on a piece of paper by drawing flowcharts. This approach attempts to be more intuitive since the user does not need to learn how to use a system to design the flowchart. Only a pencil, a notebook with white pages, and a mobile device are needed to achieve the same result. The main contribution of this thesis is to provide a more intuitive and easy-to-use tool for people to translate flowcharts into code and then execute the code. Show BibTex@mastersthesis{jorgecamara2017Master'sTheses, type = {{MS Master's Thesis}}, author = {Camara, JorgeIvan}, title = {Flow2Code - From Hand-drawn Flowchart to Code Execution}, year = {2017}, month = {May}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0003-0922-5508},} 2017 Nahum Villanueva. 2017. "ARCaching: Using Augmented Reality on Mobile Devices to Improve Geocacher Experience." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: May 2017. Advisor: Tracy Hammond. ISBN: ORCID id: 0000-0002-0451-4805. http://etd.tamu.edu/advisor/yuRW9IuVgkE/review Show Abstract: Geocaching is an outdoor treasure-hunting game that uses GPS and mobile devices to assist players in the quest of finding a geocache — a cleverly hidden physical container with a log and other items inside. The current game’s smartphone interface provides the GPS location of a geocache on a map that updates as the user gets closer to the hidden location. However, constantly checking in with the map to correct one’s location can substantially reduce situational awareness, which can become a quite a danger, as the user wanders through the woods or up a cliff to find a geocache. ARCaching is an Android-based augmented reality (AR) mobile application that facilitates navigation to a geocache and also increases situational awareness by combining environmental information gathered by the camera and overlapping it with rendered images to aid the players in their quest. ARCaching uses BeyondAR as an augmented reality browser to guide players to a cache while still providing pertinent information about the environment to help reduce risk. ARCaching was developed and evaluated against the original Geocaching.com application to determine how the user experience is affected by the AR technology. Results showed that AR while geocaching can facilitate the task of searching for caches and improves the user experience. Show BibTex@mastersthesis{nahumvillanueva2017Master'sTheses, type = {{MS Master's Thesis}}, author = {Villanueva, Nahum}, title = {ARCaching: Using Augmented Reality on Mobile Devices to Improve Geocacher Experience}, year = {2017}, month = {May}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0002-0451-4805},} 2016 Siddhartha Karthik. 2016. "Labeling by Example." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: August 2016. Advisor: Tracy Hammond. First Position: Uber. ISBN: ORCID id: 0000-0003-4445-8008. http://etd.tamu.edu/advisor/UtQM0gwp20/review Show Abstract: Sketch recognition is the computer understanding of hand drawn diagrams. Recognizing sketches instantaneously is necessary to build beautiful interfaces with real time feedback. There are various techniques to quickly recognize sketches into ten or twenty classes. However for much larger datasets of sketches from a large number of classes, these existing techniques can take an extended period of time to accurately classify an incoming sketch and require significant computational overhead. Thus, to make classification of large datasets feasible, we propose using multiple stages of recognition. In the initial stage, gesture-based feature values are calculated and the trained model is used to classify the incoming sketch. Sketches with an accuracy less than a threshold value, go through a second stage of geometric recognition techniques. In the second geometric stage, the sketch is segmented, and sent to shape-specific recognizers. The sketches are matched against predefined shape descriptions, and confidence values are calculated. The system outputs a list of classes that the sketch could be classified as, along with the accuracy, and precision for each sketch. This process both significantly reduces the time taken to classify such huge datasets of sketches, and increases both the accuracy and precision of the recognition. Show BibTex@mastersthesis{siddharthakarthik2016Master'sTheses, type = {{MS Master's Thesis}}, author = {Karthik, Siddhartha}, title = {Labeling by Example}, year = {2016}, month = {August}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0003-4445-8008},} 2016 Swarna Keshavabhotla. 2016. "PerSketchTivity: Recognition and Progressive Learning Analysis." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: August 2016. Advisor: Tracy Hammond. First Position: FactSet Research Systems. ISBN: ORCID id: 0000-0001-5892-858X. http://etd.tamu.edu/advisor/yrMuSURVpJ8/review Show Abstract: PerSketchTivity is a sketch-based tutoring system for design sketching that allows students to hone their skills in design sketching and self-regulated learning through real-time feedback. Students learn design-sketching fundamentals through drawing exercises of reference shapes starting from basic to complex shapes in all dimensions and subsequently receive real-time feedback assessing their performance. PerSketchTivity consists of a recognition system that evaluates the correctness of a student's sketch and provides real-time feedback, evaluating the sketch based on error (accuracy), smoothness, and speed. The focus of this thesis is to evaluate the performance of the system in terms of the recognition accuracy (does the system correctly understand what the student intended to draw) as well as the educational impact on the sketching abilities of the students practicing with this system. Each student's increase in sketching ability is measured in terms of the accuracy, smoothness, and the speed at which the strokes. Data analysis comparing the early to late sketches showed a statistically significant increase in sketching ability. Show BibTex@mastersthesis{swarnakeshavabhotla2016Master'sTheses, type = {{MS Master's Thesis}}, author = {Keshavabhotla, Swarna}, title = {PerSketchTivity: Recognition and Progressive Learning Analysis}, year = {2016}, month = {August}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0001-5892-858X},} 2016 Shalini Ashok Kumar. 2016. "Evaluation of Conceptual Sketches on Stylus-based Devices." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: May 2016. Advisor: Tracy Hammond. First Position: Google. ISBN: ORCID id: 0000-0002-6044-790X. http://etd.tamu.edu/advisor/HG1Q38drg8/review Show Abstract: Design Sketching is an important tool for designers and creative professionals to express their ideas and thoughts onto visual medium. Being a very critical and versatile skill for engineering students, this course is often taught in universities on pen and paper. However, this traditional pedagogy is limited by the availability of human instructors for their feedback. Also, students having low self-efficacy do not learn efficiently in traditional learning environment. Using intelligent interfaces this problem can be solved where we try to mimic the feedback given by an instructor and assess the student drawn sketches to give them insight of the areas they need to improve on. PerSketchTivity is an intelligent tutoring system which allows students to practice their drawing fundamentals and gives them real-time assessment and feedback. This research deals with finding the evaluation metrics that will enable us to grade students from their sketch data. There are seven metrics that we will work with to analyse how each of them contribute in deciding the quality of the sketches. The main contribution of this research is to identify the features of the sketch that can distinguish a good quality sketch from a poor one and design a grading metric for the sketches that can give a final score between 0 and 1 to the user sketches. Using these obtained features and our grading metric method, we grade all the sketches of students and experts. Show BibTex@mastersthesis{shaliniashokkumar2016Master'sTheses, type = {{MS Master's Thesis}}, author = {Ashok Kumar, Shalini}, title = {Evaluation of Conceptual Sketches on Stylus-based Devices}, year = {2016}, month = {May}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0002-6044-790X},} 2016 Purnendu Kaul. 2016. "Gaze Assisted Classification of On-Screen Tasks (by Difficulty Level) and User Activities (Reading, Writing/Typing, Image-Gazing)." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: May 2016. Advisor: Tracy Hammond. First Position: Walmart Technologies. ISBN: ORCID id: 0000-0002-7657-9616. http://etd.tamu.edu/advisor/FHQUFnadrA/review Show Abstract: Efforts toward modernizing education are emphasizing the adoption of Intelligent Tutoring Systems (ITS) to complement conventional teaching methodologies. Intelligent tutoring systems empower instructors to make teaching more engaging by providing a platform to tutor, deliver learning material, and to assess students’ progress. Despite the advantages, existing intelligent tutoring systems do not automatically assess how students engage in problem solving? How do they perceive various activities, while solving a problem? and How much time they spend on each discrete activity leading to the solution? In this research, we present an eye tracking framework that can assess how eye movements manifest students’ perceived activities and overall engagement in a sketch based Intelligent tutoring system, “Mechanix.” Mechanix guides students in solving truss problems by supporting user initiated feedback. Through an evaluation involving 21 participants, we show the potential of leveraging eye movement data to recognize students’ perceived activities, “reading, gazing at an image, and problem solving,” with an accuracy of 97.12%. We are also able to leverage the user gaze data to classify problems being solved by students as difficult, medium, or hard with an accuracy of more than 80%. In this process, we also identify the key features of eye movement data, and discuss how and why these features vary across different activities. Show BibTex@mastersthesis{purnendukaul2016Master'sTheses, type = {{MS Master's Thesis}}, author = {Kaul, Purnendu}, title = {Gaze Assisted Classification of On-Screen Tasks (by Difficulty Level) and User Activities (Reading, Writing/Typing, Image-Gazing)}, year = {2016}, month = {May}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0002-7657-9616},} 2016 Jaideep Ray. 2016. "Finding Similar Sketches." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: May 2016. Advisor: Tracy Hammond. First Position: Facebook. ISBN: ORCID id: 0000-0003-2266-576X. http://hdl.handle.net/1969.1/156837 http://etd.tamu.edu/advisor/BX5rtZ0EFh4/review Show Abstract: Searching is an important tool for managing and navigating the massive amounts of data available in today’s information age. While new searching methods have be-come increasingly popular and reliable in recent years, such as image-based searching, these methods are more limited than text-based means in that they don’t allow generic user input. Sketch-based searching is a method that allows users to draw generic search queries and return similar drawn images, giving more user control over their search content. In this thesis, we present Sketchseeker, a system for indexing and searching across a large number of sketches quickly based on their similarity. The system includes several stages. First, sketches are indexed according to eﬃcient and compact sketch descriptors. Second, the query retrieval subsystem considers sketches based on shape and structure similarity. Finally, a trained support vector machine classiﬁer provides semantic ﬁltering, which is then combined with median ﬁltering to return the ranked results. SketchSeeker was tested on a large set of sketches against existing sketch similarity metrics, and it shows signiﬁcant improvements in both speed and accuracy when compared to existing known techniques. The focus of this thesis is to outline the general components of a sketch retrieval system to ﬁnd near similar sketches in real time. Show BibTex@mastersthesis{jaideepray2016Master'sTheses, type = {{MS Master's Thesis}}, author = {Ray, Jaideep}, title = {Finding Similar Sketches}, year = {2016}, month = {May}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0003-2266-576X, \url{http://hdl.handle.net/1969.1/156837}},} 2015 Shiqiang (Frank) Guo. 2015. "ResuMatcher: A Personalized Resume-Job Matching System." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: May 2015. Advisor: Treacy Hammond. First Position: Amazon. ISBN: ORCID id: 0000-0002-6846-3234. http://hdl.handle.net/1969.1/154963 http://etd.tamu.edu/advisor/mwKYvmSftiU/review Show Abstract: Today, online recruiting web sites such as Monster and Indeed.com have become one of the main channels for people to find jobs. These web platforms have provided their services for more than ten years, and have saved a lot of time and money for both job seekers and organizations who want to hire people. However, traditional information retrieval techniques may not be appropriate for users. The reason is because the number of results returned to a job seeker may be huge, so job seekers are required to spend a significant amount of time reading and reviewing their options. One popular approach to resolve this difficulty for users are recommender systems, which is a technology that has been studied for a long time. In this thesis we have made an effort to propose a personalized job-résumé matching system, which could help job seekers to find appropriate jobs more easily. We create a finite state transducer based information extraction library to extract models from résumés and job descriptions. We devised a new statistical-based ontology similarity measure to compare the résumé models and the job models. Since the most appropriate jobs will be returned first, the users of the system may get a better result than current job finding web sites. To evaluate the system, we computed Normalized Discounted Cumulative Gain (NDCG) and precision@k of our system, and compared to three other existing models as well as the live result from Indeed.com. Show BibTex@mastersthesis{shiqiangguo2015Master'sTheses, type = {{MS Master's Thesis}}, author = {Guo, Shiqiang (Frank)}, title = {ResuMatcher: A Personalized Resume-Job Matching System}, year = {2015}, month = {May}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Treacy Hammond, ISBN: ORCID id: 0000-0002-6846-3234, \url{http://hdl.handle.net/1969.1/154963}},} 2014 Zhengliang Yin. 2014. "Chinese Calligraphist: A Sketch Based Learning Tool for Learning Written Chinese." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: August 2014. Advisor: Tracy Hammond. First Position: Amazon. ISBN: ORCID id: 0000-0003-2996-3639. http://hdl.handle.net/1969.1/153841 http://etd.tamu.edu/advisor/OPmrWjeWUIU/review Show Abstract: Learning Chinese as a foreign language is becoming more and more popular in western countries, however it is also very hard to be proficient, especially in writing. The involvement of the teachers in the process of learning Chinese writing is extremely necessary because they can give timely critiques and feedbacks as well as correct the students’ bad writing habits. However, it is inadequate and inefficient of the large class capacity therefore it is urgent and necessary to design a computer-based system to help students in practice Chinese writing, correct their bad writing habits early, and give feedback personally. The current written Chinese learning tools such as online tutorials emphasize writing rules including stroke order, but it could not provide practicing sessions and feedback. Hashigo, a novel CALL (Computer Assisted Language Learning) system, introduced the concept of sketch-based learning, but it’s low level recognizer is not proper for Chinese character domain. Therefore in order to help western students learn Chinese with better understanding, we adopted LADDER description language, machine learning techniques, and sketch recognition algorithms to improve handwritten Chinese stroke recognition rate. With our multilayer perceptron recognizer, it improved Chinese stroke recognition accuracy by 15.7% than the average of the four basic recognizer. In feature selection study we found that the most important features were “the aspect of the bounding box”, and the “density metrics”, and “curviness”. We chose 8 most important features after the recursive selecting stabilized. We discovered that in most situations, feature recognition is more important than template recognition. Since the writing technique is emphasized while they are taught, only 2 templates is enough. It worked as well as 20 templates, which improved recognition speed dramatically. In conclusion, in this thesis our contribution is that we (1) proposed a natural way to describe Chinese characters; (2) implemented a hierarchical Chinese character recognizer combining LADDER with the multilayer perceptron low level recognizer; (3) analyzed the performance of different recognition schemes; (4) designed a sketch-based Chinese writing learning tool, Chinese Calligraphist; and (5) find the best feature combination to recognize Chinese strokes while improving the recognition accuracy. Show BibTex@mastersthesis{zhengliangyin2014Master'sTheses, type = {{MS Master's Thesis}}, author = {Yin, Zhengliang}, title = {Chinese Calligraphist: A Sketch Based Learning Tool for Learning Written Chinese}, year = {2014}, month = {August}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0003-2996-3639, \url{http://hdl.handle.net/1969.1/153841}},} 2012 Hong-Hoe (Ayden) Kim. 2012. "Analysis of Children's Sketches to Improve Recognition Accuracy in Sketch-Based Applications." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: December 2012. pp. 105. Advisor: Tracy Hammond. First Position: TAMU PhD Student. ISBN: ORCID id: 0000-0002-1175-8680. http://hdl.handle.net/1969.1/156963 https://etd.tamu.edu/advisor/2fd238b2ad95f9c4377b6e524052c985/review Show Abstract: The current education systems in elementary schools are usually using traditional teaching methods such as paper and pencil or drawing on the board. The benefit of paper and pencil is their ease of use. Researchers have tried to bring this ease of use to computer-based educational systems through the use of sketch-recognition. Sketch-recognition allows students to draw naturally while at the same time receiving automated assistance and feedback from the computer. There are many sketch-based educational systems for children. However, current sketch-based educational systems use the same sketch recognizer for both adults and children. The problem of this approach is that the recognizers are trained by using sample data drawn by adults, even though the drawing patterns of children and adults are markedly different. We propose that if we make a separate recognizer for children, we can increase the recognition accuracy of shapes drawn by children. By creating a separate recognizer for children, we improved the recognition accuracy of children’s drawings from 81.25% (using the adults’ threshold) to 83.75% (using adjusted threshold for children). Additionally, we were able to automatically distinguish children’s drawings from adults’ drawings. We correctly identified the drawer’s age (age 3, 4, 7, or adult) with 78.3%. When distinguishing toddlers (age 3 and 4) from matures (age 7 and adult), we got a precision of 95.2% using 10-fold cross validation. When we removed adults and distinguished between toddlers and 7 year olds, we got a precision of 90.2%. Distinguishing between 3, 4, and 7 year olds, we got a precision of 86.8%. Furthermore, we revealed that there is a potential gender difference since our recognizer was more accurately able to recognize the drawings of female children (91.4%) than the male children (85.4%). Finally, this paper introduces a sketch-based teaching assistant tool for children, EasySketch, which teaches children how to draw digits and characters. Children can learn how to draw digits and characters by instructions and feedback. Show BibTex@mastersthesis{honghoekim2012Master'sTheses, type = {{MS Master's Thesis}}, author = {Kim, Hong-Hoe (Ayden)}, title = {Analysis of Children's Sketches to Improve Recognition Accuracy in Sketch-Based Applications}, pages = {105}, year = {2012}, month = {December}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0002-1175-8680, \url{http://hdl.handle.net/1969.1/156963}},} 2012 Drew Logsdon. 2012. "Arm-Hand-Finger Video Game Interaction." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: December 2012. pp. 108. Advisor: Tracy Hammond. First Position: IBM. http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10567 https://etd.tamu.edu/advisor/fe7208b2f494766758d7d5ede8e845c6/review Show Abstract: Despite the growing popularity and expansion of video game interaction techniques and research in the area of hand gesture recognition, the application of hand gesture video game interaction using arm, hand, and finger motion has not been extensively explored. Most current gesture-based approaches to video game interaction neglect the use of the fingers for interaction, but inclusion of the fingers will allow for more natural and unique interaction and merits further research. To implement arm, hand and finger-based interaction for the video game domain, several problems must be solved including gesture recognition, segmentation, hand visualization, and video game interaction that responds to arm, hand, and finger input. Solutions to each of these problems have been implemented. The potential of this interaction style is illustrated through the introduction of an arm, hand, and finger controlled video game system that responds to players' hand gestures. It includes a finger-gesture recognizer as well as a video game system employing various interaction styles. This consists of a first person shooter game, a driving game, and a menu interaction system. Several users interacted with and played these games, and this form of interaction is especially suitable for real time interaction in first-person games. This is perhaps the first implementation of its kind for video game interaction. Based on test results, arm, hand, and finger interaction a viable form of interaction that deserves further research. This implementation bridges the gap between existing gesture interaction methods and more advanced virtual reality techniques. It successfully combines the solutions to each problem mentioned above into a single, working video game system. This type of interaction has proved to be more intuitive than existing gesture controls in many situations and also less complex to implement than a full virtual reality setup. It allows more control by using the hands' natural motion and allows each hand to interact independently. It can also be reliably implemented using today's technology. This implementation is a base system that can be greatly expanded on. Many possibilities for future work can be applied to this form of interaction. Show BibTex@mastersthesis{drewlogsdon2012Master'sTheses, type = {{MS Master's Thesis}}, author = {Logsdon, Drew}, title = {Arm-Hand-Finger Video Game Interaction}, pages = {108}, year = {2012}, month = {December}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, \url{http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10567}},} 2012 George Lucchese. 2012. "Sketch Recognition on Mobile Devices." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: December 2012. pp. 54. Advisor: Tracy Hammond. First Position: IBM. http://hdl.handle.net/1969.1/148264 https://etd.tamu.edu/advisor/94c8cb8783ab2f205db8a4a93e3a4da8/review Show Abstract: Sketch recognition allows computers to understand and model hand drawn sketches and diagrams. Traditionally sketch recognition systems required a pen based PC interface, but powerful mobile devices such as tablets and smartphones can provide a new platform for sketch recognition systems. We describe a new sketch recognition library, Strontium (SrL) that combines several existing sketch recognition libraries modified to run on both personal computers and on the Android platform. We analyzed the recognition speed and accuracy implications of performing low-level shape recognition on smartphones with touch screens. We found that there is a large gap in recognition speed on mobile devices between recognizing simple shapes and more complex ones, suggesting that mobile sketch interface designers limit the complexity of their sketch domains. We also found that a low sampling rate on mobile devices can affect recognition accuracy of complex and curved shapes. Despite this, we found no evidence to suggest that using a finger as an input implement leads to a decrease in simple shape recognition accuracy. These results show that the same geometric shape recognizers developed for pen applications can be used in mobile applications, provided that developers keep shape domains simple and ensure that input sampling rate is kept as high as possible. Show BibTex@mastersthesis{georgelucchese2012Master'sTheses, type = {{MS Master's Thesis}}, author = {Lucchese, George}, title = {Sketch Recognition on Mobile Devices}, pages = {54}, year = {2012}, month = {December}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, \url{http://hdl.handle.net/1969.1/148264}},} 2012 Wenzhe Li. 2012. "Acoustic Based Sketch Recognition." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: August 2012. pp. 91. Advisor: Tracy Hammond. First Position: USC PhD Student, Goldman Sachs. http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11880 https://etd.tamu.edu/advisor/cf5d2775b7d1f7e410a506ae6aca75d5/review Show Abstract: Sketch recognition is an active research field, with the goal to automatically recognize hand-drawn diagrams by a computer. The technology enables people to freely interact with digital devices like tablet PCs, Wacoms, and multi-touch screens. These devices are easy to use and have become very popular in market. However, they are still quite costly and need more time to be integrated into existing systems. For example, handwriting recognition systems, while gaining in accuracy and capability, still must rely on users using tablet-PCs to sketch on. As computers get smaller, and smart-phones become more common, our vision is to allow people to sketch using normal pencil and paper and to provide a simple microphone, such as one from their smart-phone, to interpret their writings. Since the only device we need is a single simple microphone, the scope of our work is not limited to common mobile devices, but also can be integrated into many other small devices, such as a ring. In this thesis, we thoroughly investigate this new area, which we call acoustic based sketch recognition, and evaluate the possibilities of using it as a new interaction technique. We focus specifically on building a recognition engine for acoustic sketch recognition. We first propose a dynamic time wrapping algorithm for recognizing isolated sketch sounds using MFCC(Mel-Frequency Cesptral Coefficients). After analyzing its performance limitations, we propose improved dynamic time wrapping algorithms which work on a hybrid basis, using both MFCC and four global features including skewness, kurtosis, curviness and peak location. The proposed approaches provide both robustness and decreased computational cost. Finally, we evaluate our algorithms using acoustic data collected by the participants using a device's built-in microphone. Using our improved algorithm we were able to achieve an accuracy of 90% for a 10 digit gesture set, 87% accuracy for the 26 English characters and over 95% accuracy for a set of seven commonly used gestures. Show BibTex@mastersthesis{wenzheli2012Master'sTheses, type = {{MS Master's Thesis}}, author = {Li, Wenzhe}, title = {Acoustic Based Sketch Recognition}, pages = {91}, year = {2012}, month = {August}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, \url{http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11880}},} 2012 Francisco Vides. 2012. "TAYouKi: A Sketch-Based Tutoring System for Young Kids." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: August 2012. pp. 129. Advisor: Tracy Hammond. First Position: PayPal. http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11497 https://etd.tamu.edu/advisor/b02bcb5c8326980a7a547e0222b97f5b/review Show Abstract: Intelligent tutoring systems (ITS) have proven to be effective tools for aiding in the instruction of new skills for young kids; however, interaction methods that employ traditional input devices such as the keyboard and mouse may present barriers to children who have yet learned how to write. Existing applications which utilize pen-input devices better mimic the physical act of writing, but few provide useful feedback to the users. This thesis presents a system specifically designed to serve as a useful tool in teaching children how to draw basic shapes, and helping them develop basic drawing and writing skills. The system uses a combination of sketch recognition techniques to interpret the handwritten strokes from sketches of the children, and then provides intelligent feedback based on what they draw. Our approach provides a virtual coach to assist teachers teaching the critical skills of drawing and handwriting. We do so by guiding children through a set of exercises of increasing complexity according to their progress, and at the same time keeping track of students' performance and engagement, giving them differentiated instruction and feedback. Our system would be like a virtual Teaching Assistant for Young Kids, hence we call it TAYouKi. We collected over five hundred hand-drawn shapes from grownups that had a clear understanding of what a particular geometric shape should look like. We used this data to test the recognition of our system. Following, we conducted a series of case studies with children in age group three to six to test the interactivity efficacy of the system. The studies served to gain important insights regarding the research challenges in different domains. Results suggest that our approach is appealable and engaging to children and can help in more effectively teach them how to draw and write. Show BibTex@mastersthesis{franciscovides2012Master'sTheses, type = {{MS Master's Thesis}}, author = {Vides, Francisco}, title = {TAYouKi: A Sketch-Based Tutoring System for Young Kids}, pages = {129}, year = {2012}, month = {August}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, \url{http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11497}},} 2010 Paul Taele. 2010. "Freehand Sketch Recognition for Computer-Assisted Language Learning of Written East Asian Languages." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: December 2010. pp. 96. Advisor: Tracy Hammond. First Position: TAMU PhD Student. ISBN: ORCID id: 0000-0002-1271-0574. http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8977 https://etd.tamu.edu/advisor/b9237ba3efe8aaa5dfa1aa9e2641ed6d/review Show Abstract: One of the challenges students face in studying an East Asian (EA) language (e.g., Chinese, Japanese, and Korean) as a second language is mastering their selected language’s written component. This is especially true for students with native fluency of English and deficient written fluency of another EA language. In order to alleviate the steep learning curve inherent in the properties of EA languages’ complicated writing scripts, language instructors conventionally introduce various written techniques such as stroke order and direction to allow students to study writing scripts in a systematic fashion. Yet, despite the advantages gained from written technique instruction, the physical presence of the language instructor in conventional instruction is still highly desirable during the learning process; not only does it allow instructors to offer valuable real-time critique and feedback interaction on students’ writings, but it also allows instructors to correct students’ bad writing habits that would impede mastery of the written language if not caught early in the learning process. The current generation of computer-assisted language learning (CALL) applications specific to written EA languages have therefore strived to incorporate writing-capable modalities in order to allow students to emulate their studies outside the classroom setting. Several factors such as constrained writing styles, and weak feedback and assessment capabilities limit these existing applications and their employed techniques from closely mimicking the benefits that language instructors continue to offer. In this thesis, I describe my geometric-based sketch recognition approach to several writing scripts in the EA languages while addressing the issues that plague existing CALL applications and the handwriting recognition techniques that they utilize. The approach takes advantage of A Language to Describe, Display, and Editing in Sketch Recognition (LADDER) framework to provide users with valuable feedback and assessment that not only recognizes the visual correctness of students’ written EA Language writings, but also critiques the technical correctness of their stroke order and direction. Furthermore, my approach provides recognition independent of writing style that allows students to learn with natural writing through size- and amount-independence, thus bridging the gap between beginner applications that only recognize single-square input and expert tools that lack written technique critique. Show BibTex@mastersthesis{paultaele2010Master'sTheses, type = {{MS Master's Thesis}}, author = {Taele, Paul}, title = {Freehand Sketch Recognition for Computer-Assisted Language Learning of Written East Asian Languages}, pages = {96}, year = {2010}, month = {December}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0002-1271-0574, \url{http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8977}},} 2010 Aaron Wolin. 2010. "Segmenting Hand-Drawn Strokes." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: May 2010. pp. 160. Advisor: Tracy Hammond. First Position: Credera. http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-7869 http://etd.tamu.edu/advisor/c870703512a90fd318267e67cac64907/review Show Abstract: Pen-based interfaces utilize sketch recognition so users can create and interact with complex, graphical systems via drawn input. In order for people to freely draw within these systems, users' drawing styles should not be constrained. The low-level techniques involved with sketch recognition must then be perfected, because poor low-level accuracy can impair a user's interaction experience. Corner finding, also known as stroke segmentation, is one of the first steps to free-form sketch recognition. Corner finding breaks a drawn stroke into a set of primitive symbols such as lines, arcs, and circles, so that the original stoke data can be transformed into a more machine-friendly format. By working with sketched primitives, drawn objects can then be described in a visual language, noting what primitive shapes have been drawn and the shapes? geometric relationships to each other. We present three new corner finding techniques that improve segmentation accuracy. Our first technique, MergeCF, is a multi-primitive segmenter that splits drawn strokes into primitive lines and arcs. MergeCF eliminates extraneous primitives by merging them with their neighboring segments. Our second technique, ShortStraw, works with polyline-only data. Polyline segments are important since many domains use simple polyline symbols formed with squares, triangles, and arrows. Our ShortStraw algorithm is simple to implement, yet more powerful than previous polyline work in the corner finding literature. Lastly, we demonstrate how a combination technique can be used to pull the best corner finding results from multiple segmentation algorithms. This combination segmenter utilizes the best corners found from other segmentation techniques, eliminating many false negatives (missed primitive segmentations) from the final, low-level results. We will present the implementation and results from our new segmentation techniques, showing how they perform better than related work in the corner finding field. We will also discuss limitations of each technique, how we have sought to overcome those limitations, and where we believe the sketch recognition subfield of corner finding is headed. Show BibTex@mastersthesis{aaronwolin2010Master'sTheses, type = {{MS Master's Thesis}}, author = {Wolin, Aaron}, title = {Segmenting Hand-Drawn Strokes}, pages = {160}, year = {2010}, month = {May}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, \url{http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-7869}},} 2009 Daniel Dixon. 2009. "A Methodology for Using Assistive Sketch Recognition For Improving a Person’s Ability to Draw." MS Master's Thesis. Texas A&M University (TAMU). College Station, TX, USA: December 2009. pp. 114. Advisor: Tracy Hammond. First Position: ReelFX. http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7289 https://etd.tamu.edu/advisor/f1fb7b6700be3a420ed67e57a142eeeb/review Show Abstract: When asked to draw, most people are hesitant because they believe themselves unable to draw well. A human instructor can teach students how to draw by encouraging them to practice established drawing techniques and by providing personal and directed feedback to foster their artistic intuition and perception. This thesis describes the first methodology for a computer application to mimic a human instructor by providing direction and feedback to assist a student in drawing a human face from a photograph. Nine design principles were discovered and developed for providing such instruction, presenting reference media, giving corrective feedback, and receiving actions from the student. Face recognition is used to model the human face in a photograph so that sketch recognition can map a drawing to the model and evaluate it. New sketch recognition techniques and algorithms were created in order to perform sketch understanding on such subjective content. After two iterations of development and user studies for this methodology, the result is a computer application that can guide a person toward producing his/her own sketch of a human model in a reference photograph with step-bystep instruction and computer generated feedback. Show BibTex@mastersthesis{danieldixon2009Master'sTheses, type = {{MS Master's Thesis}}, author = {Dixon, Daniel}, title = {A Methodology for Using Assistive Sketch Recognition For Improving a Person’s Ability to Draw}, pages = {114}, year = {2009}, month = {December}, address = {College Station, TX, USA}, school = {Texas A\&M University ({TAMU})}, note = {Advisor: Tracy Hammond, \url{http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7289}},} 2001 Tracy Hammond. 2001. "Ethnomathematics: Concept Definition and Research Perspectives." MA Master's Thesis. Columbia University. New York, NY, USA: February 2001. pp. 57. Advisor: Ellen Marakowitz. First Position: MIT PhD Student. Show Abstract: Although the term ethnomathematics has been in use in the anthropological literature for quite sometime now, a standard definition of the construct has yet to emerge. More than one definition exists, causing confusion and inhibiting systematic research on the subject. Most definitions loosely refer to it as the study of mathematical ideas of non-literate peoples (e.g., Ascher and Ascher, 1997), thereby ignoring or underplaying its profound relationship to culture. More importantly, current definitions are restrictive and too narrow to adequately explain phenomena that rightfully fall within its realm. Providing a conceptually grounded definition is a necessary first step to galvanize the thinking and investigative activity on the subject. My aim in this thesis is to offer such a definition and to descriptively examine its relevance for theory building and research on ethnomathematics. I start with a brief review of the current definitions of ethnomathematics, highlighting their parochial nature. I then propose an over-arching definition that derives its grounding from interaction and reciprocity-based models. My definition suggests ethnomathematics as the study of the evolution of mathematics that has shaped, and in turn shaped by, the values of groups of people. I then use this definition to historically examine how mathematics, despite its universality and constancy themes, suffers from culture-based disparities and has been influenced in its development by various social groups over time. Specifically, I examine the role of culture in the learning and use of math, gender capabilities in math, and how even racism has played a significant part in the evolution of math. Show BibTex@mastersthesis{tracyhammond2001Master'sTheses, type = {{MA Master's Thesis}}, author = {Hammond, Tracy}, title = {Ethnomathematics: Concept Definition and Research Perspectives}, pages = {57}, year = {2001}, month = {February}, address = {New York, NY, USA}, school = {Columbia University}, note = {Advisor: Ellen Marakowitz},}

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@mastersthesis{stephanievalentine2016Dissertations,
type = {{PhD Doctoral Dissertation}},
author = {Valentine, Stephanie},
title = {Design, Deployment, Identity, \& Conformity: An Analysis of Children's Online Social Networks},
year = {2016},
month = {August},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0003-1956-8125},
}

@mastersthesis{folamialamudun2016Dissertations,
type = {{PhD Doctoral Dissertation}},
author = {Alamudun, Folami},
title = {Analysis of Visuo-cognitive Behavior in Screening Mammography},
year = {2016},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0002-0803-4542, \url{http://hdl.handle.net/1969.1/157040}},
}

@mastersthesis{honghoekim2016Dissertations,
type = {{PhD Doctoral Dissertation}},
author = {Kim, Hong-Hoe (Ayden)},
title = {A Fine Motor Skill Classifying Framework to Support Children's Self-regulation Skills and School Readiness},
year = {2016},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0002-1175-8680},
}

type = {{PhD Doctoral Dissertation}},
title = {Designing Tactile Interfaces for Abstract Interpersonal Communication, Pedestrian Navigation and Motorcyclists Navigation},
pages = {183},
year = {2014},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0002-3554-2614},
}

@mastersthesis{daniellecummings2013Dissertations,
type = {{PhD Doctoral Dissertation}},
author = {Cummings, Danielle},
title = {Multimodal Interaction for Enhancing Team Coordination on the Battlefield},
pages = {201},
year = {2013},
month = {August},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, \url{http://hdl.handle.net/1969.1/151044}},
}

@mastersthesis{sashikanthdamaraju2013Dissertations,
type = {{PhD Doctoral Dissertation}},
author = {Damaraju, Sashikanth},
title = {An Exploration of Multi-touch Interaction Techniques},
pages = {145},
year = {2013},
month = {August},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, \url{http://hdl.handle.net/1969.1/151366}},
}

@mastersthesis{brandonpaulson2010Dissertations,
type = {{PhD Doctoral Dissertation}},
author = {Paulson, Brandon},
title = {Rethinking pen input interaction: Enabling freehand sketching through improved primitive recognition},
pages = {217},
year = {2010},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, \url{http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-7808}},
}

@mastersthesis{tracyhammond2007Dissertations,
type = {{PhD Doctoral Dissertation}},
author = {Hammond, Tracy},
title = {LADDER: A Perceptually-Based Language to Simplify Sketch Recognition User Interface Development},
pages = {495},
year = {2007},
month = {February},
school = {Massachusetts Institute of Technology ({MIT})},
}

@mastersthesis{tianshuchu2017Master'sThesis,
type = {{MS Master's Thesis}},
author = {Chu, Tianshu},
title = {A Sketch-based Educational System for Learning Chinese Handwriting},
year = {2017},
month = {December},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0002-9497-058X},
}

@mastersthesis{junginkoh2017Master'sThesis,
type = {{MS Master's Thesis}},
author = {Koh, Jung In},
title = {Developing a Hand Gesture Recognition System for Mapping Symbolic Hand Gestures to Analogous Emoji in Computer-Mediated Communication},
year = {2017},
month = {June},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
}

@mastersthesis{sethpolsley2017Master'sThesis,
type = {{MS Master's Thesis}},
author = {Polsley, Seth},
title = {Identifying outcomes of care from medical records to improve doctor-patient communication},
pages = {81},
year = {2017},
month = {June},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0002-8805-8375},
}

@mastersthesis{joshcherian2017Master'sThesis,
type = {{MS Master's Thesis}},
author = {Cherian, Josh},
title = {Recognition of Everyday Activities through Wearable Sensors and Machine Learning},
year = {2017},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond \& Theresa Maldonado, ISBN: ORCID id: 0000-0002-7749-2109},
}

@mastersthesis{aqibbhat2017Master'sThesis,
type = {{MS Master's Thesis}},
author = {Bhat, Aqib},
title = {Sketchography - Automatic grading of map sketches for geography education},
year = {2017},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0003-2718-7736, \url{http://hdl.handle.net/1969.1/161656}},
}

@mastersthesis{jorgecamara2017Master'sThesis,
type = {{MS Master's Thesis}},
author = {Camara, JorgeIvan},
title = {Flow2Code - From Hand-drawn Flowchart to Code Execution},
year = {2017},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0003-0922-5508},
}

@mastersthesis{nahumvillanueva2017Master'sThesis,
type = {{MS Master's Thesis}},
author = {Villanueva, Nahum},
title = {ARCaching: Using Augmented Reality on Mobile Devices to Improve Geocacher Experience},
year = {2017},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0002-0451-4805},
}

@mastersthesis{siddharthakarthik2016Master'sThesis,
type = {{MS Master's Thesis}},
author = {Karthik, Siddhartha},
title = {Labeling by Example},
year = {2016},
month = {August},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0003-4445-8008},
}

@mastersthesis{swarnakeshavabhotla2016Master'sThesis,
type = {{MS Master's Thesis}},
author = {Keshavabhotla, Swarna},
title = {PerSketchTivity: Recognition and Progressive Learning Analysis},
year = {2016},
month = {August},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0001-5892-858X},
}

@mastersthesis{shaliniashokkumar2016Master'sThesis,
type = {{MS Master's Thesis}},
author = {Ashok Kumar, Shalini},
title = {Evaluation of Conceptual Sketches on Stylus-based Devices},
year = {2016},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0002-6044-790X},
}

@mastersthesis{purnendukaul2016Master'sThesis,
type = {{MS Master's Thesis}},
author = {Kaul, Purnendu},
title = {Gaze Assisted Classification of On-Screen Tasks (by Difficulty Level) and User Activities (Reading, Writing/Typing, Image-Gazing)},
year = {2016},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0002-7657-9616},
}

@mastersthesis{jaideepray2016Master'sThesis,
type = {{MS Master's Thesis}},
author = {Ray, Jaideep},
title = {Finding Similar Sketches},
year = {2016},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0003-2266-576X, \url{http://hdl.handle.net/1969.1/156837}},
}

@mastersthesis{shiqiangguo2015Master'sThesis,
type = {{MS Master's Thesis}},
author = {Guo, Shiqiang (Frank)},
title = {ResuMatcher: A Personalized Resume-Job Matching System},
year = {2015},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Treacy Hammond, ISBN: ORCID id: 0000-0002-6846-3234, \url{http://hdl.handle.net/1969.1/154963}},
}

@mastersthesis{zhengliangyin2014Master'sThesis,
type = {{MS Master's Thesis}},
author = {Yin, Zhengliang},
title = {Chinese Calligraphist: A Sketch Based Learning Tool for Learning Written Chinese},
year = {2014},
month = {August},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0003-2996-3639, \url{http://hdl.handle.net/1969.1/153841}},
}

@mastersthesis{honghoekim2012Master'sThesis,
type = {{MS Master's Thesis}},
author = {Kim, Hong-Hoe (Ayden)},
title = {Analysis of Children's Sketches to Improve Recognition Accuracy in Sketch-Based Applications},
pages = {105},
year = {2012},
month = {December},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0002-1175-8680, \url{http://hdl.handle.net/1969.1/156963}},
}

@mastersthesis{drewlogsdon2012Master'sThesis,
type = {{MS Master's Thesis}},
author = {Logsdon, Drew},
title = {Arm-Hand-Finger Video Game Interaction},
pages = {108},
year = {2012},
month = {December},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, \url{http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10567}},
}

@mastersthesis{georgelucchese2012Master'sThesis,
type = {{MS Master's Thesis}},
author = {Lucchese, George},
title = {Sketch Recognition on Mobile Devices},
pages = {54},
year = {2012},
month = {December},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, \url{http://hdl.handle.net/1969.1/148264}},
}

@mastersthesis{wenzheli2012Master'sThesis,
type = {{MS Master's Thesis}},
author = {Li, Wenzhe},
title = {Acoustic Based Sketch Recognition},
pages = {91},
year = {2012},
month = {August},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, \url{http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11880}},
}

@mastersthesis{franciscovides2012Master'sThesis,
type = {{MS Master's Thesis}},
author = {Vides, Francisco},
title = {TAYouKi: A Sketch-Based Tutoring System for Young Kids},
pages = {129},
year = {2012},
month = {August},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, \url{http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11497}},
}

@mastersthesis{paultaele2010Master'sThesis,
type = {{MS Master's Thesis}},
author = {Taele, Paul},
title = {Freehand Sketch Recognition for Computer-Assisted Language Learning of Written East Asian Languages},
pages = {96},
year = {2010},
month = {December},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, ISBN: ORCID id: 0000-0002-1271-0574, \url{http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8977}},
}

@mastersthesis{aaronwolin2010Master'sThesis,
type = {{MS Master's Thesis}},
author = {Wolin, Aaron},
title = {Segmenting Hand-Drawn Strokes},
pages = {160},
year = {2010},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, \url{http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-7869}},
}

@mastersthesis{danieldixon2009Master'sThesis,
type = {{MS Master's Thesis}},
author = {Dixon, Daniel},
title = {A Methodology for Using Assistive Sketch Recognition For Improving a Person’s Ability to Draw},
pages = {114},
year = {2009},
month = {December},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, \url{http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7289}},
}

@mastersthesis{tracyhammond2001Master'sThesis,
type = {{MA Master's Thesis}},
author = {Hammond, Tracy},
title = {Ethnomathematics: Concept Definition and Research Perspectives},
pages = {57},
year = {2001},
month = {February},
address = {New York, NY, USA},
school = {Columbia University},
}

author = {Leland, Jake},
title = {Recognizing Seatbelt-Fastening Activity Using Wearable Technology},
pages = {43},
year = {2017},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy HammondCoauthored with Ellen Stanfill},
}

author = {Stanfill, Ellen},
title = {Recognizing Seatbelt-Fastening Activity Using Wearable Technology},
pages = {43},
year = {2017},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy HammondCoauthored with Jake Leland},
}

author = {Brhlik, David},
title = {Enhancing Blind Navigation with the Use of Wearable Sensor Technology},
year = {2016},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
}

author = {Otuyelu, Temiloluwa},
title = {Enhancing Blind Navigation with the Use of Wearable Sensor Technology},
year = {2016},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
}

title = {Enhancing Blind Navigation with the Use of Wearable Sensor Technology},
year = {2016},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond \& Theresa MaldonadoCoauthored with Temiloluwa Otuyelu and David Bhrlik},
}

author = {Regmi, Sarin},
title = {Haptigo Tactile Navigation System},
year = {2012},
month = {May},
address = {College Station, TX, USA},
school = {Texas A\&M University ({TAMU})},
note = {Advisor: Tracy Hammond, \url{http://hdl.handle.net/1969.1/154397}},
}

author = {Valentine, Stephanie},
title = {A Shape Comparison Technique for Use in Sketch-based Tutoring Systems},
year = {2011},
month = {May},
school = {St. Mary's University of Minnesota},
note = {Advisor: Ann Smith \& Tracy Hammond},
}

Show All Latex Include

\subsection{Dissertations and Theses}

\subsubsection{PhD Dissertations}
\begin{enumerate}
\item \bibentry{stephanievalentine2016PhD}
\item \bibentry{folamialamudun2016PhD}
\item \bibentry{honghoekim2016PhD}
\item \bibentry{daniellecummings2013PhD}
\item \bibentry{sashikanthdamaraju2013PhD}
\item \bibentry{brandonpaulson2010PhD}
\item \bibentry{tracyhammond2007PhD}
\end{enumerate}

\subsubsection{Master's Theses}
\begin{enumerate}
\item \bibentry{tianshuchu2017MS}
\item \bibentry{junginkoh2017MS}
\item \bibentry{sethpolsley2017MS}
\item \bibentry{joshcherian2017MS}
\item \bibentry{aqibbhat2017MS}
\item \bibentry{jorgecamara2017MS}
\item \bibentry{nahumvillanueva2017MS}
\item \bibentry{siddharthakarthik2016MS}
\item \bibentry{swarnakeshavabhotla2016MS}
\item \bibentry{shaliniashokkumar2016MS}
\item \bibentry{purnendukaul2016MS}
\item \bibentry{jaideepray2016MS}
\item \bibentry{shiqiangguo2015MS}
\item \bibentry{zhengliangyin2014MS}
\item \bibentry{honghoekim2012MS}
\item \bibentry{drewlogsdon2012MS}
\item \bibentry{georgelucchese2012MS}
\item \bibentry{wenzheli2012MS}
\item \bibentry{franciscovides2012MS}
\item \bibentry{paultaele2010MS}
\item \bibentry{aaronwolin2010MS}
\item \bibentry{danieldixon2009MS}
\item \bibentry{tracyhammond2001MA}
\end{enumerate}