Learning for the next generation: Predicting the usage of synthetic learning environments
Arthur William Evans, University of Central Florida, United States
University of Central Florida . Awarded
The push to further the use of technology in learning has broadened the attempts of many to find innovated ways to aid the new, technologically savvy generation of learners, in acquiring the knowledge needed for their education and training. A critical component to the success of these initiatives is the proper application of the science of learning (Cannon-Bowers and Bowers, 2009). One technological initiative that can benefit from this application is the use of synthetic learning environments (SLEs). SLEs are instructional systems embedded within virtual worlds. These worlds can be simulations of some task, for instance a simulation that may be completed as part of a military training to mimic specific situations, or they could be in the form of a video game, for example, a game designed to maintain the attention of school children while teaching mathematics. The important components to SLEs are a connection to the underlying task being trained and a set of goals for which to strive toward.
SLEs have many unique characteristics which separate them from other forms of education. Two of the most salient characteristics are the instructorless nature of SLEs (most of the learning from SLEs happens without instructor interaction) and the fact that in many cases SLEs are actually fun and engaging, thus motivating the learner to participate more and allowing them to experience a more immersive interaction. Incorporating the latter of these characteristics into a model originally introduced by Davis (1989) and adapted by Yi and Hwang (2003) for use with web applications, an expanded model to predict the effects of enjoyment, goal orientation, ease of use, and several other factors on the overall use of SLEs has been created. Adapting the Davis and Yi and Hwang models for the specific use of SLEs provides a basis understanding how each of the critical input variables effect the use and thus effectiveness of learning tools based on SLEs. In particular, performance goal orientation has been added to the existing models to more accurately reflect the performance characteristics present in games.
Results of this study have shown that, in fact, performance goal orientation is a significant factor in the SLE Use and Learning model. However, within the model it is important to distinguish that the two varieties of performance goal orientation (prove and avoid) play different roles. Prove performance goal orientation has been shown to have significant relationships with several other critical factors while avoid performance goal orientation is only accounted for in its significant correlation with prove performance goal orientation.
With this understanding, training developers can now have a better understanding of where their resources should be spent to promote more efficient and effective learning. The results of this study allow developers to move forward with confidence in the fact that their new learning environments will be effective in a number of realms, not only limited to classroom, business, or military training.
Evans, A.W. Learning for the next generation: Predicting the usage of synthetic learning environments. Ph.D. thesis, University of Central Florida.
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