Abstract
Teaching procedural skills is relevant for a broad range of applications, from IT administration to automotive repair to medical diagnostics. Virtual learning environments reduce the cost, time, and risk, and increase the availability of such training. We introduce ontologies and rules to characterize the objects in the learning environment, and the actions that the user can perform on them. These semantic models are used as the basis for automated reasoning about a student’s actions and their effects, and guide automated assessment and feedback to the student. We describe our system and models in the context of weapon skills such as disassembling and assembling a rifle.
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In OWL, one might instead use qualified cardinality restrictions. Other ways of modeling also exist in Flora.
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Acknowledgements
This material is based upon work supported by the United States Government under Contract No. W911QY-14-C-0023. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Government. Development of the Sunflower IDE was funded in part by the U.S. Office of the Assistant Secretary of Defense for Readiness under the Open Netcentric Interoperability for Training and Testing (ONISTT) project, and by TRMC (Test Resource Management Center) T&E/S&T (Test and Evaluation/Science and Technology) Program under the NST Test Technology Area. We are also indebted to the research community for developing and maintaining the open source language and software components on which Sunflower depends, especially Flora-2 (a.k.a. Ergo Lite), XSB Prolog, and InterProlog.
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Elenius, D., Denker, G., Kim, M. (2016). Semantically Enhanced Virtual Learning Environments Using Sunflower. In: Garoufallou, E., Subirats Coll, I., Stellato, A., Greenberg, J. (eds) Metadata and Semantics Research. MTSR 2016. Communications in Computer and Information Science, vol 672. Springer, Cham. https://doi.org/10.1007/978-3-319-49157-8_7
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DOI: https://doi.org/10.1007/978-3-319-49157-8_7
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