Development of sign language learning system/ Caryl Ann O. Eusoya, Justin Lloyd P. Magat, Raffy A. Morada, Jecele A. Sombese, and Danica C. Valera .--

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Eusoya, Caryl Ann O

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TextPublication details: Manila: Technological University of the Philippines, 2024.Description: x, 156pages: 29cm. +1 CD-ROM (4 3/4in.)Content type:

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BTH QA 76.9 E97 2024

Dissertation note: College of Industrial Technology.-- Bachelor of Engineering Technology major in Computer Engineering Technology: Technological University of the Philippines, 2024. Summary: This thesis presents the development of a sign language learning system to improve American Sign Language (ASL) education for both hearing and non-hearing individuals in the Philippines. The barriers to deaf education and difficulties in accessing ASL instruction motivated this research. The system was designed using Python, TypeScript, Tailwind, Next.js, Flask, LSTM neural networks, and MediaPipe for motion tracking. It utilizes a camera for gesture recognition, a monitor for interactive video lessons, and assessments to track progress. Testing was conducted with hearing and non-hearing students, as well as experts, using ISO 25010 quality standards. Results showed 88.89% passed test cases in the second iteration, indicating high system reliability. Overall effectiveness evaluations revealed excellent ease of use, quality of output, accuracy, and performance. Twenty four (24) respondents evaluated the project using Likert’s scale and the descriptive interpretation of the mean for functionality (x̄= 4.63), usability (x̄= 4.73), efficiency (x̄= 4.69), credibility (x̄= 4.48), and cost-effectiveness (x̄= 4.94), with an over all mean of 4.69 that indicates a descriptive rating of strongly agree. The learning system enables an immersive ASL education experience tailored to beginner and intermediate skills. It facilitates inclusive learning spaces while promoting awareness. This research successfully demonstrates the potential for motion-based technologies to transform sign language pedagogy through adaptive, engaging multi-media tools. The system can be expanded through advanced vocabulary, optimized tracking, and personalized assessments to spread accessible ASL literacy.

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Item type	Current library	Shelving location	Call number	Copy number	Status	Notes	Date due	Barcode
Bachelor's Thesis CIT	TUP Manila Library	Thesis Section-2nd floor	BTH QA 76.9 E97 2024 (Browse shelf(Opens below))	c.1.	Not for loan	For library use only		BTH0004741

Thesis (undergraduate)

College of Industrial Technology.-- Bachelor of Engineering Technology major in Computer Engineering Technology: Technological University of the Philippines, 2024.

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This thesis presents the development of a sign language learning system to improve
American Sign Language (ASL) education for both hearing and non-hearing individuals
in the Philippines. The barriers to deaf education and difficulties in accessing ASL
instruction motivated this research. The system was designed using Python, TypeScript,
Tailwind, Next.js, Flask, LSTM neural networks, and MediaPipe for motion tracking. It
utilizes a camera for gesture recognition, a monitor for interactive video lessons, and
assessments to track progress. Testing was conducted with hearing and non-hearing
students, as well as experts, using ISO 25010 quality standards. Results showed 88.89%
passed test cases in the second iteration, indicating high system reliability. Overall
effectiveness evaluations revealed excellent ease of use, quality of output, accuracy, and
performance. Twenty four (24) respondents evaluated the project using Likert’s scale and
the descriptive interpretation of the mean for functionality (x̄= 4.63), usability (x̄= 4.73),
efficiency (x̄= 4.69), credibility (x̄= 4.48), and cost-effectiveness (x̄= 4.94), with an over
all mean of 4.69 that indicates a descriptive rating of strongly agree. The learning system
enables an immersive ASL education experience tailored to beginner and intermediate
skills. It facilitates inclusive learning spaces while promoting awareness. This research
successfully demonstrates the potential for motion-based technologies to transform sign
language pedagogy through adaptive, engaging multi-media tools. The system can be
expanded through advanced vocabulary, optimized tracking, and personalized assessments
to spread accessible ASL literacy.

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