Enhancing engineering education through link prediction in social networks

Research output: Contribution to journalScientific review

4 Citations (Scopus)

Abstract

© 2016 TEMPUS Publications. In recent years the world has been a witness to a brutal onslaught of emergent technologies. As such it is not surprising that social networking has permeated through practically every human activity with amazing speed. Educational systems have not lagged behind; and not only is that true, but it is also evident that social networks have ostensibly penetrated in engineering education. This relevant and irrefutable fact has generated the necessity of posing systematic research activities on a worldwide scale that are related to this topic. In this context the topic covered in this paper is Social Network Research related to Engineering Education. Specifically, our research concerns the way or ways in which link prediction in social networks is able to improve teaching-learning processes in engineering education. A suitably natural environment for the application of such research is that of scholarly publications related to computer science, particularly networks. The results of our research are promising; they facilitate valuable information regarding the tendencies of the actors immersed in engineering education, be they students or faculty members.
Original languageAmerican English
Pages (from-to)1566-1578
Number of pages1408
JournalInternational Journal of Engineering Education
StatePublished - 1 Jan 2016

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Engineering education
social network
education
engineering
predictions
computer science
educational system
students
witness
Computer science
learning
networking
learning process
Teaching
tendencies
Students
student

Cite this

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title = "Enhancing engineering education through link prediction in social networks",
abstract = "{\circledC} 2016 TEMPUS Publications. In recent years the world has been a witness to a brutal onslaught of emergent technologies. As such it is not surprising that social networking has permeated through practically every human activity with amazing speed. Educational systems have not lagged behind; and not only is that true, but it is also evident that social networks have ostensibly penetrated in engineering education. This relevant and irrefutable fact has generated the necessity of posing systematic research activities on a worldwide scale that are related to this topic. In this context the topic covered in this paper is Social Network Research related to Engineering Education. Specifically, our research concerns the way or ways in which link prediction in social networks is able to improve teaching-learning processes in engineering education. A suitably natural environment for the application of such research is that of scholarly publications related to computer science, particularly networks. The results of our research are promising; they facilitate valuable information regarding the tendencies of the actors immersed in engineering education, be they students or faculty members.",
author = "Angel Ferreira-Santiago and Cornelio Yanez-Marquez and Itzama Lopez-Yanez and Oscar Camacho-Nieto and Mario Aldape-Perez and Arguelles-Cruz, {Amadeo Jose}",
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AU - Ferreira-Santiago, Angel

AU - Yanez-Marquez, Cornelio

AU - Lopez-Yanez, Itzama

AU - Camacho-Nieto, Oscar

AU - Aldape-Perez, Mario

AU - Arguelles-Cruz, Amadeo Jose

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Y1 - 2016/1/1

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AB - © 2016 TEMPUS Publications. In recent years the world has been a witness to a brutal onslaught of emergent technologies. As such it is not surprising that social networking has permeated through practically every human activity with amazing speed. Educational systems have not lagged behind; and not only is that true, but it is also evident that social networks have ostensibly penetrated in engineering education. This relevant and irrefutable fact has generated the necessity of posing systematic research activities on a worldwide scale that are related to this topic. In this context the topic covered in this paper is Social Network Research related to Engineering Education. Specifically, our research concerns the way or ways in which link prediction in social networks is able to improve teaching-learning processes in engineering education. A suitably natural environment for the application of such research is that of scholarly publications related to computer science, particularly networks. The results of our research are promising; they facilitate valuable information regarding the tendencies of the actors immersed in engineering education, be they students or faculty members.

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