Matching Items (2)
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- All Subjects: Tutor Systems
- Creators: Gupta, Sandeep
Description
\English is a programming language, a method of allowing programmers to write instructions such that a computer may understand and execute said instructions in the form of a program. Though many programming languages exist, this particular language is designed for ease of development and heavy optimizability in ways that no other programming language is. Building on the principles of Assembly level efficiency, referential integrity, and high order functionality, this language is able to produce extremely efficient code; meanwhile, programmatically defined English-based reusable syntax and a strong, static type system make \English easier to read and write than many existing programming languages. Its generalization of all language structures and components to operators leaves the language syntax open to project-specific syntactical structuring, making it more easily applicable in more cases. The thesis project requirements came in three parts: a compiler to compile \English code into NASM Assembly to produce a final program product; a standard library to define many of the basic operations of the language, including the creation of lists; and C translation library that would utilize \English properties to compile C code using the \English compiler. Though designed and partially coded, the compiler remains incomplete. The standard library, C translation library, and design of the language were completed. Additional tools regarding the language design and implementation were also created, including a Gedit syntax highlighting configuration file; usage documentation describing in a tutorial style the basic usage of the language; and more. Though the thesis project itself may be complete, the \English project will continue in order to produce a new language capable of the abilities possible with the design of this language.
ContributorsDavey, Connor (Author) / Gupta, Sandeep (Thesis director) / Bazzi, Rida (Committee member) / Calliss, Debra (Committee member) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Languages, specially gestural and sign languages, are best learned in immersive environments with rich feedback. Computer-Aided Language Learning (CALL) solu- tions for spoken languages have successfully incorporated some feedback mechanisms, but no such solution exists for signed languages. Computer Aided Sign Language Learning (CASLL) is a recent and promising field of research which is made feasible by advances in Computer Vision and Sign Language Recognition(SLR). Leveraging existing SLR systems for feedback based learning is not feasible because their decision processes are not human interpretable and do not facilitate conceptual feedback to learners. Thus, fundamental research is needed towards designing systems that are modular and explainable. The explanations from these systems can then be used to produce feedback to aid in the learning process.
In this work, I present novel approaches for the recognition of location, movement and handshape that are components of American Sign Language (ASL) using both wrist-worn sensors as well as webcams. Finally, I present Learn2Sign(L2S), a chat- bot based AI tutor that can provide fine-grained conceptual feedback to learners of ASL using the modular recognition approaches. L2S is designed to provide feedback directly relating to the fundamental concepts of ASL using an explainable AI. I present the system performance results in terms of Precision, Recall and F-1 scores as well as validation results towards the learning outcomes of users. Both retention and execution tests for 26 participants for 14 different ASL words learned using learn2sign is presented. Finally, I also present the results of a post-usage usability survey for all the participants. In this work, I found that learners who received live feedback on their executions improved their execution as well as retention performances. The average increase in execution performance was 28% points and that for retention was 4% points.
In this work, I present novel approaches for the recognition of location, movement and handshape that are components of American Sign Language (ASL) using both wrist-worn sensors as well as webcams. Finally, I present Learn2Sign(L2S), a chat- bot based AI tutor that can provide fine-grained conceptual feedback to learners of ASL using the modular recognition approaches. L2S is designed to provide feedback directly relating to the fundamental concepts of ASL using an explainable AI. I present the system performance results in terms of Precision, Recall and F-1 scores as well as validation results towards the learning outcomes of users. Both retention and execution tests for 26 participants for 14 different ASL words learned using learn2sign is presented. Finally, I also present the results of a post-usage usability survey for all the participants. In this work, I found that learners who received live feedback on their executions improved their execution as well as retention performances. The average increase in execution performance was 28% points and that for retention was 4% points.
ContributorsPaudyal, Prajwal (Author) / Gupta, Sandeep (Thesis advisor) / Banerjee, Ayan (Committee member) / Hsiao, Ihan (Committee member) / Azuma, Tamiko (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020