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A standard‐based architecture to support learning interoperability: A practical experience in gamification
Author(s) -
PérezBerenguer Daniel,
GarcíaMolina Jesús
Publication year - 2018
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2572
Subject(s) - interoperability , computer science , analytics , architecture , plug in , learning analytics , world wide web , data science , multimedia , art , visual arts , programming language
Summary 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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