Using modern game engines to provide a realistic virtual environment for on-the-job industrial training and education
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This thesis is based on work done at The Department of Computer and Information Science (IDI) at The Norwegian University of Tech- nology and Science (NTNU) in collaboration with the Serious Gam- ing Initiative at Statoil. The motivation for the work was to imple- ment an industrial game for the purposes of collecting data, which was used towards answering questions related to how different educa- tional theories, age groups and graphics fidelity affects learning effect in industrial games. To accomplish this, an investigation was made in to which third party game engines were available in the market, and which suited our needs best. The decision fell on Unity3D because of outstanding documentation and developer community.The game was continuously developed over several months, with three major development and test iterations. Each iteration consisted of two runs a couple of days apart using the same test group consisting of real users of various ages. Data was collected from both in-game sources and through an electronic survey for each iteration. By comparing these data points, we were able to extract some useful indications used to answer our research questions.The conclusion made by the authors was that inductive learning is overall more suitable for industrial games than deductive learning. However, indications were found that suggests that participants in the age group ≥ 40 learn slightly better from deductive learning, and participants in the age group < 40 learn better from inductive learn- ing. At the same time, the data suggests that graphics fidelity has a positive effect on learning effect up to a certain threshold, at which point the increase in learning effect plateaus.