**1.1 Collaborative virtual environments for learning**

Advances on technology, engineering, and instruction have enabled to diversify education and training support computer systems −see Table 1. Initially, the development of this kind of systems adopted the Computer Aided Instruction paradigm and was subsequently refined with Artificial Intelligence techniques implemented in the Computer Aided Intelligent Instruction paradigm. From the viewpoint of Artificial Intelligence, systems have been developed based on two rather divergent instructional approaches: Intelligent Tutoring Systems and Learning Environments (Aguilar, et al., 2010).

Computer Supported Collaborative Learning (CSCL) is probable the last of the paradigms emerged in the late nineteenth century. Koschmann (1996) referred to it as associated with instructional technology: "*This developing paradigm, for which the acronym CSCL has been coined, focuses on the use of technology as a mediational tool within collaborative methods of instruction*".


Table 1. Advances on Technology, Engineering and Instruction

CSCL basis is the Socio-Constructivism theory, in which core idea is that human knowledge is constructed upon the foundation of previous learning and within the society. People, as social creatures, are highly influenced by the interaction with their socio-cultural environment, in such a way that this interaction contributes to the formation of the individuals.

CVEs for learning, the computer systems developed under the CSCL paradigm, can be described as a conceptual space in which a user contacts or interacts, in possibly different time and space conditions, with other users or their representation, or with elements of the environment such as data or objects. Thinking in CVEs in this way includes a conceptual asynchronous character that Churchill & Snowdon (1998) did not take into account.

According with the interface offered to the user, CVEs could be classified as:


 Three-dimensional (3D) environments – also known as Virtual Reality (VR) environments.

However, nowadays it is hard to imagine a multi-user VE without a graphical representation.

VR environments offer to their users different immersion degrees covering a wide range of possibilities that goes from the less immersive systems using only traditional desktop devices such as keyboard, mouse and monitor, to the highly immersive that use VR specific devices such as head-mounted displays (HMD), data gloves, or the CAVETM .

The intend in using a CVE for instruction is to promote particular forms of interaction among the students inside the environment, by means of creating, encouraging, or enriching situations that would trigger learning mechanisms in the way Dillenbourg (1999) proposed.

CVEs provide the learner with a diversified set of computational features as well as a powerful context for learning in which time, scale and physics can be controlled; where participants can get new capabilities such as the ability to fly, or to observe the environment from different perspectives as an object or with any other virtual embodiment.

CVEs offer a space that brings remote people and remote objects together into a spatial and social proximity creating a natural interaction, which allows better communication awareness (Wolff et al., 2005) and where users are likely to be engaged in interaction with the virtual world and with other inhabitants through verbal and nonverbal channels. These characteristics make them a proper scenario for knowledge construction, concurrent with the socio-constructivist theory, as well as a proper tool for training in socio-technical tasks (e.g. in coordinated situation such as rescue operations or enterprise logistic).

For the multiuser condition, 3D CVEs represent a communication technology on their own right due to its highly visual and interactive interface character. They offer a learning context that may allow the trainees to practice skills and abilities, and to get knowledge in a situation that approximates the conditions under which they will be used in real life, but using a safe and flexible environment where materials do not break or wear out.

CVEs can be used to train one or more students in the execution of a certain task, mostly in situations in which training in the real environments is either impossible or undesirable because it is costly or dangerous.

#### **1.2 Intelligent CVEs**

70 Virtual Reality and Environments

Advances on technology, engineering, and instruction have enabled to diversify education and training support computer systems −see Table 1. Initially, the development of this kind of systems adopted the Computer Aided Instruction paradigm and was subsequently refined with Artificial Intelligence techniques implemented in the Computer Aided Intelligent Instruction paradigm. From the viewpoint of Artificial Intelligence, systems have been developed based on two rather divergent instructional approaches: Intelligent

Computer Supported Collaborative Learning (CSCL) is probable the last of the paradigms emerged in the late nineteenth century. Koschmann (1996) referred to it as associated with instructional technology: "*This developing paradigm, for which the acronym CSCL has been coined, focuses on the use of technology as a mediational tool within collaborative methods of* 

Mainframes Monolithic

**Technology Engineering Instruction** 

Programming

Object Oriented Paradigm

Virtual Reality Agents Paradigm Collaborative

CSCL basis is the Socio-Constructivism theory, in which core idea is that human knowledge is constructed upon the foundation of previous learning and within the society. People, as social creatures, are highly influenced by the interaction with their socio-cultural environment, in such a way that this interaction contributes to the formation of the

CVEs for learning, the computer systems developed under the CSCL paradigm, can be described as a conceptual space in which a user contacts or interacts, in possibly different time and space conditions, with other users or their representation, or with elements of the environment such as data or objects. Thinking in CVEs in this way includes a conceptual

One-dimensional environments – based on text or text in combination with some

Two-dimensional environments – based on text and complemented with figures (e.g.

asynchronous character that Churchill & Snowdon (1998) did not take into account.

According with the interface offered to the user, CVEs could be classified as:

Personal Computers Structured Paradigm Cognitive

Behavioral Approach

Approach

Constructivist Approach

Learning Approach

**1.1 Collaborative virtual environments for learning** 

Networks & Peripheral Devices

Table 1. Advances on Technology, Engineering and Instruction

*instruction*".

T

I

M

E

individuals.

comics).

symbols (e.g. emoticons).

Tutoring Systems and Learning Environments (Aguilar, et al., 2010).

In the Computer Aided Intelligent Instruction paradigm, there is a growing interest on the research aim of knowledge such as Intelligent Virtual Environments (IVE). VEs may incorporate in different degrees, characteristics of learning environments through an Intelligent Tutoring Systems (ITS). Where the intelligence skills generally fall into a Pedagogical Virtual Agent (PVA) to engage and motivate students along their learning process.

The traditional architecture for the ITS consists of four modules: the expert or domain module, containing the information to be taught; the student module, which maintains individualized information of the students; the tutoring module, which provides a model of the teaching process; and, the interactions with the learner controlled by the communication

The Users' Avatars Nonverbal Interaction in Collaborative Virtual Environments for Learning 73

mentors, assistances (Giraffa & Viccari, 1999), learning peers (Chou, Chan, & Lin, 2003) or as proposed in here, as a collaborative facilitator with the aim of enhancing the collaborative

Broadly defined, nonverbal behavior might include most of what we do; it even includes certain characteristics of verbal behavior by distinguishing the content, or meaning of speech, from paralinguistic cues such as loudness, tempo, pitch or intonation (Patterson, 1983). Moreover, the use of certain objects like our decided outfit, or the physical environment when used to communicate something, without saying it, has traditionally being considered as NVC. Nonverbal behavior can be used to substitute, complement,

In real life, nonverbal interaction involves three factors (Knapp & Hall, 2007): *environmental conditions, physical characteristics of the communicators,* and *behaviors of communicators*, all of

The *environmental conditions* that will probably affect the most during interaction in a computer scenario are given by the architecture and virtual objects around, what Hall (1968) defined as fixed-features, space organized by unmoving boundaries such as a room, and

In a computerized environment, the *physical characteristics* of the interactants will be given by the users' avatar both appearance and body movements. While the appearance typically is given by a set of characteristics for the user to choose, like male or female avatar, and maybe a set of different cloths, skin or hair colors for example. As of body movements, Kujanpää & Manninen (2003) presented a considerable set of possible elements an avatar

The avatar's body movements are usually restricted mainly due to their associated technology cost. Typically, in CVEs the users' avatars are naturalistic (Salem & Earle, 2000), with a low-level details approach and humanoid-like, they can display some basic humans'

Other important consideration is that the means offered by the CVE to the user, in order to transmit NVC to his/her avatar, interfere with its spontaneity and therefore its revealing. The three different approaches to transmit nonverbal behavior from the users to his/her

The *behaviors of communicators* relay on the context that in a CVE will be given by its purpose. For example, in a video game, the users' interaction will be controlled by their intention on getting the goals of the game, while in a social CVE the participants interaction

2. user-guided, when the user guides the avatar defining tasks and movements; and 3. semi-autonomous, where the avatar has an internal state that depends on its goals and its environment, and this state is modified by the user. For example, when in a video

game, the player achieves a goal and his/her avatar celebrates it.

will be more likely to be directed to those they feel socially attracted −see Table 2.

process; as mentioned, by the analysis of the nonverbal behavior of the users' avatars.

accent, regulate or even contradict the spoken message (Knapp & Hall, 2007).

semi-fixed features, the arrangement of moveable objects such as a chair.

them clearly restricted to the computer scenario conditions.

can include to manage the transmitting of NVC.

actions or expressions.

avatar in a VE (Capin et al., 1997) are:

1. directly controlled with sensors attached to the user;

**2. Nonverbal interaction in CVEs** 

module. The ITS architecture adapted for an Intelligent Collaborative Virtual Environment (ICVE) (Aguilar et al., 2010) is shown in Figure 1.

Fig. 1. ICVE Architecture

The systems for training developed to date may be classified depending on the issue they emphasize as: simulation processes, generation of believable environments, and collaboration processes; three aspects that can be integrated in a single system, an ICVE for learning.

The component in charge of the domain of the application has the capacity to execute the simulations of the task to be trained as they are executed in the present systems. VR technology allows recreating environments with believable features (Youngblut, 1998) offering the possibility of being trained in tasks that may be expensive, risky, or even impossible to reproduce in the reality.

Regarding the collaboration process, in an ICVE by including virtual agents to replace team members (Rickel, 2001), the trainees can have a team training experience when the complete human team is not available. It is also possible to integrate into the environment personified PVAs to offer them support, in the same way an instructor or a human peer would do.

Within VR technology, a PVA may assume a 3D representation similar to that used by a human in the environment; they can be embodied through an avatar. The PVAs personification seems to generate positive effects in the perception of the apprentices during their learning experiences (Lester et al., 1997), with two key advantages over earlier work: they increase the bandwidth of communication between students and computers; and they increase the computer's ability to engage and motivate students. Some early empirical results on PVAs embodied effectiveness are on the topics of (W. L. Johnson, Rickel, & Lester, 2000): interactive demonstrations, navigational guidance, gaze and gesture as attentional guides, nonverbal feedback, conversational signals, conveying and eliciting emotion, and virtual teammates.

A PVA can be defined as an intelligent agent that makes decisions about how to maximize the student learning process. PVAs as a result of its goal can function within the ICVE as tutors, mentors, assistances (Giraffa & Viccari, 1999), learning peers (Chou, Chan, & Lin, 2003) or as proposed in here, as a collaborative facilitator with the aim of enhancing the collaborative process; as mentioned, by the analysis of the nonverbal behavior of the users' avatars.
