| 000 -LEADER |
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| fixed length control field |
01880nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
ft8864 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251210152818.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
251210b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
T58 F56 2025 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Flores, Neil A.; Legaste, Miloudanne G.; Tiatcho, James O. |
| 245 ## - TITLE STATEMENT |
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| Title |
Smart tutoring: Subject with targeted tutor matching and real-time emotion engagement analysis |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
c8864 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Other physical details |
Capstone Project: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
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| Source |
text |
| Content type term |
text |
| Content type code |
text |
| 337 ## - MEDIA TYPE |
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| Source |
unmediated |
| Media type term |
unmediated |
| Media type code |
unmediated |
| 338 ## - CARRIER TYPE |
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| Source |
volume |
| Carrier type term |
volume |
| Carrier type code |
volume |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
ABSTRACT: This research investigates the development of a “smart tutoring” system that incorporates real-time emotion recognition and personalized tutor matching to enhance learning experiences. The system aims to address diverse student needs by offering flexible and adaptive learning environments. Key features include automated scheduling, virtual tutoring with emotion detection, and personalized tutor recommendations, utilizing Convolutional Neural Networks (CNN) for emotion recognition and a content-based filtering algorithm for tutor matching. The system monitors students emotional states and learning preferences, allowing for real-time adjustments in teaching strategies. Evaluated using ISO 25010 criteria, the system showed positive results, with mean scores ranging from 3.82 to 4.33 on a 5-point Likert scale. While the system met its core objectives, there remains potential for further improvement. This study demonstrates the potential of using CNN and content-based filtering algorithm to create more personalized, emotionally responsive, and adaptable learning environments. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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| Classification |
Filipiniana |
| 655 ## - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Source of classification or shelving scheme |
|
| Item type |
Thesis/Dissertation |