Article

A standard-based architecture to support learning interoperability: A practical experience in gamification

Journal ar
Software - Practice and Experience
  • Volumen: 48
  • Número: 6
  • Fecha: 01 June 2018
  • Páginas: 1238-1268
  • ISSN: 1097024X 00380644
  • Source Type: Journal
  • DOI: 10.1002/spe.2572
  • Document Type: Article
  • Publisher: John Wiley and Sons Ltd Southern Gate Chichester, West Sussex PO19 8SQ vgorayska@wiley.com
Copyright © 2018 John Wiley & Sons, Ltd. Creating quality online content requires a great deal of effort from teachers. In addition to issues specific to the design and creation of the elements of a course, teachers must face technical hurdles so as to perform common tasks, such as deploying the same content on different e-learning platforms and integrating content into external tools, or acquiring the ability to analyze tracking data generated during learner interactions. These problems principally arise owing to the very limited level of interoperability provided by content creation tools. In order to facilitate the creation of interoperable contents, the Digital Content Production Center at the Polytechnic University of Cartagena (Spain) has developed the UPCTforma tool, whose main architectural driver has been interoperability. More specifically, the tool takes advantage of the Learning Tools Interoperability and Caliper interoperability specifications to provide several types of quality regarding three key aspects of content production: tool interoperability, learning analytics, and motivation. In this paper, we provide a detailed description of the component-based architecture proposed and present a validation of the requirements elicited through the use of a UPCTforma gamification activity created for a real project involving approximately 4000 students. One of the novel aspects of this architecture is the transformation of tracking data into ¿learning analysis models¿ that represent the information in the tracked learning activities at a higher level of abstraction. These models are used to provide activity-specific learning analytics and motivation. Platform independency with respect to data analytics technologies, messaging systems, and communication protocols is achieved by using adapters.

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