Capturing the Semantics of Simulation Learning with Linked Data

Capturing the Semantics of Simulation Learning with Linked Data

Irene Celino (Politecnico di Milano, Italy) and Daniele Dell’Aglio (Politecnico di Milano, Italy)
DOI: 10.4018/978-1-4666-4655-1.ch011
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Abstract

Knowledge-rich learning environments like simulation learning sessions call for the adoption of knowledge technologies to effectively manage information and data related to the learning supply and to the observation analysis. In this chapter, the authors illustrate the benefits and the challenges from the adoption of Linked Data and Semantic Web technologies to model, store, update, collect, and interpret learning data in simulation environments. The experience gained in applying this approach to a Simulation Learning system based on Serious Games proves the feasibility and the advantages of knowledge technologies in addressing and solving the issues faced by trainers and teachers in their daily practice.
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Simulation Learning, also known as Instructional Simulation or Educational Simulation, consists in a system or environment that mimics a reality or a realistic situation; simulations usually come with features and elements that help the learner exploring, focusing, trying, experimenting and gaining new knowledge about a system or a procedure. In education, simulations are often used to replicate human decision-making processes, helping learners focus on key behaviours, concepts or principles (Gärdenfors &Johansson, 2005). In order to be engaging and effective, Simulation Learning is usually conducted with the help of computer-assisted systems; sometimes virtual learning environments (VLE) are employed to recreate the simulated situation and increase the degree of immersion of the user within the environment.

The adoption of digital solutions for Simulation Learning (Edyburn & Basham, 2008) has a twofold effect: on the one hand, it helps in the delivery of the simulation and the timing of the events and inputs during the sessions; on the other hand, it enables an easier collection of the observational learning evidence, thus improving their monitoring and analysis. Indeed, collecting data during learning delivery has always been a challenge for teachers and trainers, since the dual and contemporary processing of watching and recording is a complex and demanding task even for the most experienced instructor. Thus, in order to collect and code observational data, simple recording systems can be put in place, so to trace any new development while observing learners’ behaviour. In this case, the use of computer-based solutions greatly adds to the reliability of observational measurements (Johnson, 1995). The most popular commercial solution of this kind is Noldus Observer XT (http://www.noldus.com/human-behavior-research/products/2/the-observer-xt), also largely employed in learning research (Snell, 2011).

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