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

• 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
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-020-03330-x

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.