Automatic Approach to Evaluate Collaborative Interaction in Virtual Environments

Automatic Approach to Evaluate Collaborative Interaction in Virtual Environments

Luis Casillas (University of Guadalajara, Mexico), Adriana Peña (University of Guadalajara, Mexico) and Alfredo Gutierrez (University of Guadalajara, Mexico)
Copyright: © 2019 |Pages: 23
DOI: 10.4018/978-1-5225-6367-9.ch001
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Virtual environments for multi-users, collaborative virtual environments (CVE), support geographical distant people to experience collaborative learning and team training. In this context, monitoring collaboration provides valuable, and in time, information regarding individual and group indicators, helpful for human instructors or intelligent tutor systems. CVE enable people to share a virtual space, interacting with an avatar, generating nonverbal behavior such as gaze-direction or deictic gestures, a potential means to understand collaboration. This chapter presents an automated model and its inference mechanisms to evaluate collaboration in CVE based on expert human rules of nonverbal participants' activity. The model is a multi-layer analysis that includes data filtering, fuzzy classification, and rule-based inference producing a high-level assessment of group collaboration. This approach was applied to a task-oriented session, where two participants assembled cubes in a CVE to create a figure.
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Nonverbal behavior has been broadly studied in the real world and for the creation of artificial behavior in robots or animation (Breazeal, Kidd, Thomaz, Hoffman, & Berlin, 2005). However, there are few studies of the nonverbal cues people display in CVE through their avatars. In some cases, the developed CVE are focused on the automatic generation and scripting of nonverbal behaviors for autonomous agents; and in others on a real-time interaction of human users with the primary goal to offer a tool that allows sending basic emotional nonverbal messages.

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