This research focuses on the learning challenges that both students and teachers face during the learning process. It addresses the different techniques and methods used for face recognition. The proposed VTA model uses the convolutional neural networks to recognize the identities of the student. It gathers the facial expressions and body poses of each student in the classroom and predicts the attention level of that student, thus determining his/her learning capabilities. This research will help the students achieve their learning objectives by being able to get an accurate and real evaluation of their contribution and attention during the classes. Also, the proposed VTA model helps the teacher get some insight into his/her teaching methodologies during the class as the model will observe and record the attentiveness of the students. This research will have a significant positive impact on student success and on effective lecturing.
This research focuses on deaf students in the United Arab Emirates. The proposed classroom assessment using sign language communicator (CASC) for special needs students (SN) in the United Arab Emirates is based on artificial intelligence (AI) tools. This research provides essential services for teaching evaluations, learning outcome assessments, and the development of learning environments. CASC model is composed of two models. The first model converts the speech to a sign language, which contains a speech recognizer, sign language recognizer. The second model converts the sign language to written text. This model generates a report for students' understanding and class evaluation in advance before ending the course based on the sign language recognition and image processing tools. This model will have a significantly positive impact on SN students' success and on effective lecturing and optimizing teaching and learning in the classroom. The accuracy of the model is 92%. The analysis of the student's feedback in real-time provides effective instructional strategies.
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