Gendered Socialization with an Embodied Agent: Creating a Social and Affable Mathematics Learning Environment for Middle-Grade Females

This study examined whether or not embodied-agent-based learning would help middle-grade females have more positive mathematics learning experiences. The study used an explanatory mixed-methods research design. First, a classroom-based experiment was conducted with one hundred and twenty 9th-graders learning introductory algebra (53% male and 47% female; 51% Caucasian and 49% Latino). The results revealed that learner gender was a significant factor in the learners’ evaluations of their agent (η2 = .07), the learners’ task-specific attitudes (η2 = .05), and their task-specific self-efficacy (η2 = .06). In-depth interviews were then conducted with 22 students selected from the experiment participants. The interviews revealed that Latina and Caucasian females built a different type of relationship with their agent and reported more positive learning experiences as compared to Caucasian males. The females’ favorable view of the agent-based learning was largely influenced by their everyday classroom experiences, implying that students’ learning experience in real and virtual spaces was interconnected

A Satisfaction-based Model for Affect Recognition from Conversational Features in Spoken Dialog Systems

Detecting user affect automatically during real-time conversation is the main challenge towards our greater aim of infusing social intelligence into a natural-language mixed-initiative High-Fidelity (Hi-Fi) audio control spoken dialog agent. In recent years, studies on affect detection from voice have moved on to using realistic, non-acted data, which is subtler. However, it is more challenging to perceive subtler emotions and this is demonstrated in tasks such as labelling and machine prediction. This paper attempts to address part of this challenge by considering the role of user satisfaction ratings and also conversational/dialog features in discriminating contentment and frustration, two types of emotions that are known to be prevalent within spoken human-computer interaction. However, given the laboratory constraints, users might be positively biased when rating the system, indirectly making the reliability of the satisfaction data questionable. Machine learning experiments were conducted on two datasets, users and annotators, which were then compared in order to assess the reliability of these datasets. Our results indicated that standard classifiers were significantly more successful in discriminating the abovementioned emotions and their intensities (reflected by user satisfaction ratings) from annotator data than from user data. These results corroborated that: first, satisfaction data could be used directly as an alternative target variable to model affect, and that they could be predicted exclusively by dialog features. Second, these were only true when trying to predict the abovementioned emotions using annotator?s data, suggesting that user bias does exist in a laboratory-led evaluation