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Trade-off between interoperability and data collection performance when designing an architecture for learning analytics
The heterogeneity of external systems that can be connected in an e-learning environment can impose interoperability and performance requirements for recording and storing the learning data. Web-based protocols have been developed to improve e-learning systems’ interoperability and capability to per...
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Published in: | Future generation computer systems 2017-03, Vol.68, p.31-37 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | The heterogeneity of external systems that can be connected in an e-learning environment can impose interoperability and performance requirements for recording and storing the learning data. Web-based protocols have been developed to improve e-learning systems’ interoperability and capability to perform meaningful analytics. The present paper describes a web-based learning environment aimed at training how to command and control unmanned autonomous vehicles, provided with analytic capabilities. It integrates an external web content management system and a simulation engine that present different performance requirements for recording all significant events that occur during the learning process. Its record store construction, based on standard interoperability protocols, is explored here from the performance viewpoint. The tests that were conducted to assess regular data stores used for learning analytics show that performance should not be overlooked when constructing and deploying learning analytics systems.
•A learning environment aimed at training how to control unmanned autonomous vehicles.•The environment uses e-learning standard specifications, such as IMS LTI and xAPI.•Integrates a learning management system with a simulation engine.•Different hardware and software configurations for storing the simulation data.•An ad-hoc embedded server for improving the performance of the learning records. |
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ISSN: | 0167-739X 1872-7115 |
DOI: | 10.1016/j.future.2016.06.040 |