Mood Inference Machine: Framework to Infer Affective Phenomena in ROODA Virtual Learning Environment
This article presents a mechanism to infer mood states, aiming to provide virtual learning environments (VLEs) with a tool able to recognize the student’s motivation. The inference model has as its parameters personality traits, motivational factors obtained through behavioral standards and the affective subjectivity identified in texts made available in the communication functionalities of the VLE. In the inference machine, such variables are treated under probability reasoning, more precisely by Bayesian networks.
Affective modeling; Bayesian networks; virtual learning environments
International Journal of Advanced Corporate Learning. ISSN: 1867-5565