an approach for identifying affective states through behavioral patterns in web-based learning management systems
In a learning environment, the students experience different affective states. Learning environments that takes into account the students' affective state enhance the students' learning, gain and experience. Therefore, it is crucial to provide students with different learning material and activities according to different affective states. To provide learning that considers students' affective states, the primary step is the detection of affective states of a student. In this paper, we present an approach for the detection of affective states from the patterns of students' behavior observed during an online course. By calculating the affective states and then filling that affective state data into the student model of a learning management system a basis for adaptivity is provided.
affective states adaptive learning systems confusion human computer interaction effort confidence independence