000			01880nam a22002417a 4500
003			ft8864
005			20251210152818.0
008			251210b ||||| |||| 00| 0 eng d
041			_aengtag
050			_aT58 F56 2025
082			_a.
100	1		_aFlores, Neil A.; Legaste, Miloudanne G.; Tiatcho, James O.
245			_aSmart tutoring: Subject with targeted tutor matching and real-time emotion engagement analysis
264		1	_a. _b. _cc8864
300			_bCapstone Project: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2025
336			_2text _atext _btext
337			_2unmediated _aunmediated _bunmediated
338			_2volume _avolume _bvolume
505			_aABSTRACT: 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			_aF
655			_aacademic writing
942			_2lcc _cMS
999			_c37326 _d37326