Student Awareness Model based on student affective response and generic profiles

This paper outlines strategies intended to develop a comprehensive Student Awareness Model. In order to develop the model, a combination of two approaches was used to extract students' profile information. The first approach was an automatic approach, in which more emphasis was placed on students' affective responses. In order to obtain this information, a facial expression recognition application was developed to record positive or negative affective responses, as well as an application that utilized Microsoft Kinect Sensors to record changes in students' sitting positions during real-time learning. Alongside this approach, supporting information obtainable through data utilization of university's information system database was examined, such as the student's generic profile data. With such comprehensive data, developing a Student Awareness Model, the main driver in developing a Personalized Teaching Strategy, will be easier to achieve.