Mood Inference Machine: Framework to Infer Affective Phenomena in ROODA Virtual Learning Environment

Abstract

Abstract-This article presents a mechanism to infer mood states, aiming to provide virtual learning environments (VLE) 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.Index Terms-Affective modeling, Bayesian networks, virtual learning environments.

Conversational Agents in Distance Education: Comparing Mood States with Students’ Perception

“…Some participants attributed some responsibility to themselves in what concerns improvements in the CA, in a collaborative or solidary view about the system. Supporting this assertion, Longhi et al (2012) stated that the Enthusiastic mood expresses positivity to face the challenges of learning, which lead to collaboration and cooperation.…”

“…Still, the dissatisfied mood was present in 6% of the logs. According to Longhi et al (2012), this mood state is evident when there is expression of anger, contempt, disgust and/or envy. In this context, it may indicate moments where the CA responses end up leading the students to externalize a negative emotion, which in turn may lead them to quit the CA environment and, when recurrent, reflected on their general behavior.…”

“…1) Appraisal Extraction: method used by Longhi et al (2012), classifying words that have affective connotations in the groups of emotions that determine the student mood state. For example, the emotion family of "happy" comprises terms like "fond", "elated", "caring", "cheerful" and "delighted", among many others.…”

“…Studies in the area of Affective Computing have shown that it is possible to recognize the mood states of a student in a VLE by means of a model to correlate variables that can influence it (e.g. personality traits, motivational factors, and affective subjectivity identified in texts), which can be used to assist teachers and promote better teaching practices (Longhi et al, 2012).…”