TY - JOUR
T1 - Automatic tutoring system to support cross-disciplinary training in Big Data
AU - Solé-Beteta, Xavier
AU - Navarro, Joan
AU - Vernet, David
AU - Zaballos, Agustín
AU - Torres-Kompen, Ricardo
AU - Fonseca, David
AU - Briones, Alan
N1 - Funding Information:
Authors acknowledge the help and collaboration of Guillem Villa on the restyling of the graphical artworks of this paper. This research was partially supported by Secretaria d’Universitats i Recerca of the Department of Business and Knowledge of the Generalitat de Catalunya under grants 2017-SGR-934 and 2017-SGR-977. Also, authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.
Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2021/2
Y1 - 2021/2
N2 - 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.
AB - 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.
KW - Big Data training
KW - Intelligent tutoring system
KW - Master as a Service
KW - Virtual Learning Environment
UR - http://www.scopus.com/inward/record.url?scp=85085518011&partnerID=8YFLogxK
U2 - 10.1007/s11227-020-03330-x
DO - 10.1007/s11227-020-03330-x
M3 - Article
AN - SCOPUS:85085518011
SN - 0920-8542
VL - 77
SP - 1818
EP - 1852
JO - Journal of Supercomputing
JF - Journal of Supercomputing
IS - 2
ER -