Implementation of motivational tactics in tutoring systems
Journal of Artificial Intelligence in Education
Motivation Diagnosis in Intelligent Tutoring Systems
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
Informing the Detection of the Students' Motivational State: An Empirical Study
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Role-Based Access Controls: Status, Dissemination, and Prospects for Generic Security Mechanisms
Electronic Commerce Research
Off-task behavior in the cognitive tutor classroom: when students "game the system"
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Probabilistic Combination of Multiple Modalities to Detect Interest
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Affective Learning — A Manifesto
BT Technology Journal
Evaluating affective interfaces: innovative approaches
CHI '05 Extended Abstracts on Human Factors in Computing Systems
An Approach for Detecting Learning Styles in Learning Management Systems
ICALT '06 Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies
Evaluating Bayesian networks' precision for detecting students' learning styles
Computers & Education
Understanding student confidence as it relates to first year achievement
FIE '98 Proceedings of the 28th Annual Frontiers in Education - Volume 01
ICALT '08 Proceedings of the 2008 Eighth IEEE International Conference on Advanced Learning Technologies
The Dynamics of Affective Transitions in Simulation Problem-Solving Environments
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Assessment of motivation in online learning environments
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Using learner focus of attention to detect learner motivation factors
UM'05 Proceedings of the 10th international conference on User Modeling
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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.