Meaningful Interaction in Virtual Learning Environments

There is still a great deal of opportunity for research on contextual interactive immersion in virtual heritage environments. The general failure of virtual environment technology to create engaging and educational experiences may be attributable not just to deficiencies in technology or in visual fidelity, but also to a lack of contextual and performative-based interaction, such as that found in games. However, there is little written so far on exactly how game-style interaction can help improve virtual learning environments.

Talking to children: The within-classroom nature of everyday adult language in the early childhood educational environment

Group-size effects, as changes in the adult language when speaking to individual or multiple children in two- and three-year-olds' Australian childcare centre classrooms were investigated. The language addressed to children by 21 staff members was coded for social (e.g., non-verbal, inferential and pragmatic), and linguistic (e.g., morphological, lexical, syntactic and referential) features. In the two-year-olds' classrooms, minimal differences were found between the language used in dyads (addressed to a single child) and polyads (addressed to more than one child). More extensive group-size effects, particularly in syntactic complexity, were found in the three-year-olds' classrooms. Explanations for the constancy of the adult language input in the younger classrooms, and the changes noted in the older rooms will be discussed in terms of plurality (i.e., more than one listener), methodology, and group-size effects that may be specific to the early childhood educational setting.

A conceptual model of personalized virtual learning environments

The Virtual Learning Environment (VLE) is one of the fastest growing areas in educational technology research and development. In order to achieve learning effectiveness, ideal VLEs should be able to identify learning needs and customize solutions, with or without an instructor to supplement instruction. They are called Personalized VLEs (PVLEs). In order to achieve PVLEs success, comprehensive conceptual models corresponding to PVLEs are essential. Such conceptual modeling development is important because it facilitates early detection and correction of system development errors. Therefore, in order to capture the PVLEs knowledge explicitly, this paper focuses on the development of conceptual models for PVLEs, including models of knowledge primitives in terms of learner, curriculum, and situational models, models of VLEs in general pedagogical bases, and particularly, the definition of the ontology of PVLEs on the constructivist pedagogical principle. Based on those comprehensive conceptual models, a prototyped multiagent-based PVLE has been implemented. A field experiment was conducted to investigate the learning achievements by comparing personalized and non-personalized systems. The result indicates that the PVLE we developed under our comprehensive ontology successfully provides significant learning achievements. These comprehensive models also provide a solid knowledge representation framework for PVLEs development practice, guiding the analysis, design, and development of PVLEs. (c) 2005 Elsevier Ltd. All rights reserved.

Intelligent agent supported personalization for virtual learning environments

Virtual learning environments (VLEs) are computer-based online learning environments, which provide opportunities for online learners to learn at the time and location of their choosing, whilst allowing interactions and encounters with other online learners, as well as affording access to a wide range of resources. They have the capability of reaching learners in remote areas around the country or across country boundaries at very low cost. Personalized VLEs are those VLEs that provide a set of personalization functionalities, such as personalizing learning plans, learning materials, tests, and are capable of initializing the interaction with learners by providing advice, necessary instant messages, etc., to online learners. One of the major challenges involved in developing personalized VLEs is to achieve effective personalization functionalities, such as personalized content management, learner model, learner plan and adaptive instant interaction. Autonomous intelligent agents provide an important technology for accomplishing personalization in VLEs. A number of agents work collaboratively to enable personalization by recognizing an individual's eLeaming pace and reacting correspondingly. In this research, a personalization model has been developed that demonstrates dynamic eLearning processes; secondly, this study proposes an architecture for PVLE by using intelligent decision-making agents' autonomous, pre-active and proactive behaviors. A prototype system has been developed to demonstrate the implementation of this architecture. Furthemore, a field experiment has been conducted to investigate the performance of the prototype by comparing PVLE eLearning effectiveness with a non-personalized VLE. Data regarding participants' final exam scores were collected and analyzed. The results indicate that intelligent agent technology can be employed to achieve personalization in VLEs, and as a consequence to improve eLeaming effectiveness dramatically.

Virtual training environments to improve train driver's crisis decision making

This paper reports on a current research project in which virtual reality simulators are being investigated as a means of simulating hazardous Rail work conditions in order to allow train drivers to practice decision-making under stress. When working under high stress conditions train drivers need to move beyond procedural responses into a response activated through their own problem-solving and decision-making skills. This study focuses on the use of stress inoculation training which aims to build driver’s confidence in the use of new decision-making skills by being repeatedly required to respond to hazardous driving conditions. In particular, the study makes use of a train cab driving simulator to reproduce potentially stress inducing real-world scenarios. Initial pilot research has been undertaken in which drivers have experienced the training simulation and subsequently completed surveys on the level of immersion experienced. Concurrently drivers have also participated in a velocity perception experiment designed to objectively measure the fidelity of the virtual training environment. Baseline data, against which decision-making skills post training will be measured, is being gathered via cognitive task analysis designed to identify primary decision requirements for specific rail events. While considerable efforts have been invested in improving Virtual Reality technology, little is known about how to best use this technology for training personnel to respond to workplace conditions in the Rail Industry. To enable the best use of simulators for training in the Rail context the project aims to identify those factors within virtual reality that support required learning outcomes and use this information to design training simulations that reliably and safely train staff in required workplace accident response skills.