3-D environment model enhances collaboration during learning

There are two sides to collaborative learning: collaborating to learn and learning to collaborate. For this reason, learners may require guidance on both how to perform a task and on questions concerning collaboration.

The proposed model is based on analysing non-verbal communication about collaborative interaction that takes place while a task is performed. An avatar personifies the tutor in the learning process, which materializes in the visual framework provided by virtual environments, thereby supporting the collaboration process.

The model proposes a schema that identifies what non-verbal communication signals are likely to be useful for this purpose, and how to measure and relate these signals to particular effective collaborative learning indicators.

Feasible and adaptable model

During the research, the tutor was implemented in a prototype application running on Maevif, a platform for developing intelligent multi-user virtual environments for education and entertainment.

The autonomous tutoring agent used text messages to give advice to learners as they completed the set task, which involved handling objects. The messages were activated when learners were diagnosed as not having satisfactorily attained the indicators of effective collaborative learning. The application validated the feasibility of the model.

Part of the research focused on developing guidelines for relating effective collaborative learning indicators to particular non-verbal communication signals that can be automatically gathered from the virtual collaborative environment.

Although the model was initially defined for a collaborative learning environment, there is no reason why it should not be adapted to monitor other types of activities based in virtual environments, such as training or virtual meetings, but other indicators would have to be investigated in this case.

The methodology was developed and applied throughout the research. Exploratory studies were conducted to empirically check if not all then some of what were considered to be the most representative possibilities.

The model was developed as part of Adriana Peña Pérez Negrón's PhD thesis research, supervised by Angélica de Antonio Jiménez, associate professor of the Universidad Politécnica de Madrid Facultad de Informática's Department of Computer Languages and Systems and Software Engineering.