TSN’s Emotionally Immersive Tele-Learning (EITLTM) framework incorporates novel computer vision and machine learning approaches to technologies used in online education and webinars. Current online teaching systems lack the means by which instructors can gauge their students' level of engagement because these students are not visible. Student behavior and lecturer oversight in the traditional classroom is known to modulate study behaviors and impact performance and learning outcomes, but cannot at present be managed for students in a virtual audience. Quantifying and automatically measuring student engagement during lectures in a scalable and accessible manner for these students is essential for improving academic success, but has not been studied widely in natural distance learning environments.
EITL measures the student’s willingness to participate in the learning process (i.e., behavioral engagement) and his/her emotional attitude towards learning (i.e., emotional engagement). The proposed technology captures the student via video and tracks the student’s face through the video’s frames. Different features are extracted from the student’s face e.g., facial fiducial points, head pose, eye gaze, and Gabor-based features. These features which are used to detect action units (AU’s) as defined by the Facial Action Coding System (FACS), decompose facial expressions in terms of the fundamental actions of individual muscles or groups of muscles. Then these action units are used to identify facial expressions as well as the behavioral and the emotional engagements of the student. This technology allows the lecturer to receive real-time feedback from facial features, gaze and other body kinesics, which when averaged across the virtual classroom, provide feedback related to the reception of information delivery.
The purpose of this technology is to give the instructor feedback about the engagement or attentiveness of his/her virtual students. This graphic shows the instructor at his console teaching a virtual audience. On the screen to the far right, the instructor can see the percentage level of attentiveness of each student in the audience.
Our EIT serves as the crucial component of our core application used in online learning and content-centric instructional systems. While participation in on-line training and education has been rapidly adopted universally in an attempt to reduce education costs, in the world of education, existing online systems for lectures, are yet to be effective or considered equal in value to traditional classroom teaching. The purpose and commercial potential of this EIT technology is to significantly transform on-line education making it more effective than in the past.