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The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 2/2021

27.05.2020

Automatic tutoring system to support cross-disciplinary training in Big Data

verfasst von: Xavier Solé-Beteta, Joan Navarro, David Vernet, Agustín Zaballos, Ricardo Torres-Kompen, David Fonseca, Alan Briones

Erschienen in: The Journal of Supercomputing | Ausgabe 2/2021

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Abstract

During the last decade, Big Data has emerged as a powerful alternative to address latent challenges in scalable data management. The ever-growing amount and rapid evolution of tools, techniques, and technologies associated to Big Data require a broad skill set and deep knowledge of several domains—ranging from engineering to business, including computer science, networking, or analytics among others—which complicate the conception and deployment of academic programs and methodologies able to effectively train students in this discipline. The purpose of this paper is to propose a learning and teaching framework committed to train masters’ students in Big Data by conceiving an intelligent tutoring system aimed to (1) automatically tracking students’ progress, (2) effectively exploiting the diversity of their backgrounds, and (3) assisting the teaching staff on the course operation. Obtained results endorse the feasibility of this proposal and encourage practitioners to use this approach in other domains.
Vorheriger Artikel Adoption of human metabolic processes as Data Quality Based Models
Nächster Artikel Neighborhood search-based job scheduling for IoT big data real-time processing in distributed edge-cloud computing environment

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Fußnoten
1
Sagittarius is an open-source project from La Salle Campus Barcelona—Universitat Ramon Llull than can be freely used and downloaded from https://​github.​com/​xavier-sole-beteta/​sagittarius.​git.
 
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Metadaten
Titel
Automatic tutoring system to support cross-disciplinary training in Big Data
verfasst von
Xavier Solé-Beteta
Joan Navarro
David Vernet
Agustín Zaballos
Ricardo Torres-Kompen
David Fonseca
Alan Briones
Publikationsdatum
27.05.2020
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 2/2021
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-020-03330-x

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