Collecting Experience Data from Remotely Hosted Learning Applications

  • Félix J. García ClementeEmail author
  • Luis de la Torre
  • Sebastián Dormido
  • Christophe Salzmann
  • Denis Gillet
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 22)


The ability to integrate multiple learning applications from different organizations allows sharing resources and reducing costs in the deployment of learning systems. In this sense, Learning Tools Interoperability (LTI) is the main current leading technology for integrating learning applications with platforms like Learning Management Systems (LMS). On the other hand, the integration of learning applications also benefits from data collection, which allows learning systems to implement Learning Analytics (LA) processes. Tin Can API is a specification for learning technology that makes this possible. Both learning technologies, LTI and Tin Can API, are supported by nowadays LMS, either natively or through plugins. However, there is no seamless integration between these two technologies in order to provide learning systems with experience data from remotely hosted learning applications. Our proposal defines a learning system architecture ready to apply advanced LA techniques on experience data collected from remotely hosted learning applications through a seamless integration between LTI and Tin Can API. In order to validate our proposal, we have implemented a LRS proxy plug-in in Moodle that stores learning records in a SCORM Cloud LRS service, and a basic online lab based on Easy JavaScript Simulation (EjsS). Moreover, we have tested our implementation using resources located in three European universities.


Learning Management System Learning Tool Interoperability Experience API Learning Analytics 


  1. 1.
    Dodero, J.M., González-Conejero, E.J., Gutiérrez-Herrera, G., Peinado, S., Tocino, J.T., Ruiz-Rube, I.: Trade-off between interoperability and data collection performance when designing an architecture for learning analytics. Future Gener. Comput. Syst. 68, 31–37 (2017)CrossRefGoogle Scholar
  2. 2.
    edX. Open edX: Open Courseware Development Platform. Accessed 31 Oct 2016
  3. 3.
    EPFL React Group: Grassp project. Accessed 31 Oct 2016
  4. 4.
    Clemente, F.J.G., Esquembre, F.: EjsS: A JavaScript library and authoring tool which makes computational-physics education simpler. In: Poster Presented at the XXVI IUPAP Conference on Computational Physics (CCP), Boston, USA (2014)Google Scholar
  5. 5.
    HT2 Labs: Learning locker. Accessed 31 Oct 2016
  6. 6.
    IMS Global Learning Consortium: IMS global learning information services best practice and implementation guide. Accessed 31 Oct 2016
  7. 7.
    IMS Global Learning Consortium: Learning tools interoperability. Accessed 31 Oct 2016
  8. 8.
    Learning Analytics Technologies for Education: KlassData. Accessed 31 Oct 2016
  9. 9.
    Rustici Software: SCORM cloud. Accessed 31 Oct 2016
  10. 10.
    Rustici Software: Tin Can API. Accessed 31 Oct 2016
  11. 11.
    Sierra, A.J., Martín-Rodríguez, A., Ariza, T., Muñoz-Calle, J., Fernández-Jiménez, J.J.: LTI for interoperating e-Assessment tools with LMS. In: Methodologies and Intelligent Systems for Technology Enhanced Learning, 6th International Conference, pp. 173–181. Springer, Switzerland (2016)Google Scholar
  12. 12.
    UNED Labs: Moodle LRS proxy. Accessed 31 Oct 2016

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Félix J. García Clemente
    • 1
    Email author
  • Luis de la Torre
    • 2
  • Sebastián Dormido
    • 2
  • Christophe Salzmann
    • 3
  • Denis Gillet
    • 3
  1. 1.Departament of Computer Engineering and TechnologyUniversity of MurciaMurciaSpain
  2. 2.Departament of Informatics and Automatics, Computer Science SchoolUNEDMadridSpain
  3. 3.Institute of Electrical EngineeringSwiss Federal Institute of Technology Lausanne (EPFL)LausanneSwitzerland

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