A scalable architecture for the dynamic deployment of multimodal learning analytics applications in smart classrooms

Alberto Huertas Celdrán, José A. Ruipérez-Valiente, Félix J.García Clemente, María Jesús Rodríguez-Triana, Shashi Kant Shankar, Gregorio Martínez Pérez

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)

    Abstract

    The smart classrooms of the future will use different software, devices and wearables as an integral part of the learning process. These educational applications generate a large amount of data from different sources. The area of Multimodal Learning Analytics (MMLA) explores the affordances of processing these heterogeneous data to understand and improve both learning and the context where it occurs. However, a review of different MMLA studies highlighted that ad-hoc and rigid architectures cannot be scaled up to real contexts. In this work, we propose a novel MMLA architecture that builds on software-defined networks and network function virtualization principles. We exemplify how this architecture can solve some of the detected challenges to deploy, dismantle and reconfigure the MMLA applications in a scalable way. Additionally, through some experiments, we demonstrate the feasibility and performance of our architecture when different classroom devices are reconfigured with diverse learning tools. These findings and the proposed architecture can be useful for other researchers in the area of MMLA and educational technologies envisioning the future of smart classrooms. Future work should aim to deploy this architecture in real educational scenarios with MMLA applications.

    Original languageEnglish
    Article number2923
    JournalSensors (Switzerland)
    Volume20
    Issue number10
    DOIs
    Publication statusPublished - 02 May 2020

    Keywords

    • Educational technology
    • Internet of things
    • Multimodal learning analytics
    • Multisensorial networks
    • Smart classrooms

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