2026-04-17T11:53:21Zhttps://recercat.cat/oai/requestoai:recercat.cat:20.500.14342/32742026-03-10T00:12:33Zcom_2072_482405com_2072_183628col_2072_482415
RECERCAT
author
Solé-Beteta, Xavier
author
Navarro, Joan
author
Vernet, David
author
Zaballos, Agustin
author
Torres Kompen, Ricardo
author
Fonseca, David
author
Briones, Alan
2021-02
http://hdl.handle.net/20.500.14342/3274
https://doi.org/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.
eng
© L'autor/a. Tots el drets reservats
Ensenyament universitari
Dades massives
Automatic tutoring system to support cross-disciplinary training in Big Data
info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion