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

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—r...

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Bibliographic Details
Published in:The Journal of supercomputing 2021-02, Vol.77 (2), p.1818-1852
Main Authors: Solé-Beteta, Xavier, Navarro, Joan, Vernet, David, Zaballos, Agustín, Torres-Kompen, Ricardo, Fonseca, David, Briones, Alan
Format: Article
Language:English
Subjects:
Big Data
Compilers
Computer Science
Data management
Domains
Interpreters
Processor Architectures
Programming Languages
Students
Supercomputing Education: Thinking in Parallel
Tutoring
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Summary: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.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-020-03330-x