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.