000			02194nam a22001577a 4500
082			_a629.8
100			_aPoonja, Hasnain Ali _9119889
245			_aFocus and Engagement Level Detection Using Computer Vision and Machine Learning in a Classroom Environment / _cHasnain Ali Poonja
264			_aIslamabad : _bSMME- NUST; _c2023.
300			_a64p. _bSoft Copy _c30cm
500			_aDue to Covid 19, the global education system has changed toward online learning, which has a high dropout rate. Therefore, it is vital that students maintain their level of interest. Therefore, detection of engagement level alone is insufficient for analyzing and improving learning and teaching techniques. To promote student engagement in STEM and online learning environments, technologies such as AR/VR and Haptics should be implemented. Utilizing facial emotion, body pose, and head rotation, a web-based computer vision system is developed and implemented to identify student involvement levels using webcams during tasks such as online classrooms, haptic interaction, and augmented reality. In addition, an AR and Haptics-based World Map is being designed and developed. To evaluate and compare three types of learning scenarios, namely (1) Traditional, (2) Augmented Reality-based, and (3) Haptics-based, two methods are employed: (1) Trained Computer Vision models are tested for 3 scenarios, and (2) A user study is conducted using the Positive and Negative Affect Schedule (PANAS) Questionnaire and NASA-Task Load Index, from which conclusions are drawn. The results of a comparison of Traditional, Augmented reality, and Haptics-based learning indicate that Haptics and Augmented Reality-based learning are the most immersive and increase levels of engagement during online learning and STEM training, whereas Traditional learning methods are the least effective during online classes. User studies and computer vision models are utilized to validate the results.
650			_aMS Robotics and Intelligent Machine Engineering _9119486
700			_aSupervisor : Dr. Muhammad Jawad Khan _9119689
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/33689
942			_2ddc _cTHE
999			_c607417 _d607417