TY - GEN
T1 - Finding the needle in a haystack
T2 - 11th European Conference on Technology Enhanced Learning, EC-TEL 2016
AU - Paraschiv, Ionut Cristian
AU - Dascalu, Mihai
AU - McNamara, Danielle
AU - Trausan-Matu, Stefan
N1 - Funding Information:
This work is partially funded by the 644187 H2020 RAGE (Realising an Applied Gaming Eco-System) http://www.rageproject.eu/project .
Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - The speed at which new scientific papers are published has increased dramatically, while the process of tracking the most recent publications having a high impact has become more and more cumbersome. In order to support learners and researchers in retrieving relevant articles and identifying the most central researchers within a domain, we propose a novel 2-mode multilayered graph derived from Cohesion Network Analysis (CNA). The resulting extended CNA graph integrates both authors and papers, as well as three principal link types: coauthorship, co-citation, and semantic similarity among the contents of the papers. Our rankings do not rely on the number of published documents, but on their global impact based on links between authors, citations, and semantic relatedness to similar articles. As a preliminary validation, we have built a network based on the 2013 LAK dataset in order to reveal the most central authors within the emerging Learning Analytics domain.
AB - The speed at which new scientific papers are published has increased dramatically, while the process of tracking the most recent publications having a high impact has become more and more cumbersome. In order to support learners and researchers in retrieving relevant articles and identifying the most central researchers within a domain, we propose a novel 2-mode multilayered graph derived from Cohesion Network Analysis (CNA). The resulting extended CNA graph integrates both authors and papers, as well as three principal link types: coauthorship, co-citation, and semantic similarity among the contents of the papers. Our rankings do not rely on the number of published documents, but on their global impact based on links between authors, citations, and semantic relatedness to similar articles. As a preliminary validation, we have built a network based on the 2013 LAK dataset in order to reveal the most central authors within the emerging Learning Analytics domain.
KW - 2-mode multilayered graph
KW - Co-authorship
KW - Co-citation
KW - Learning analytics
KW - Semantic similarity
UR - http://www.scopus.com/inward/record.url?scp=84988453120&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988453120&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-45153-4_79
DO - 10.1007/978-3-319-45153-4_79
M3 - Conference contribution
AN - SCOPUS:84988453120
SN - 9783319451527
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 632
EP - 635
BT - Adaptive and Adaptable Learning - 11th European Conference on Technology Enhanced Learning, EC-TEL 2016, Proceedings
A2 - Verbert, Katrien
A2 - Sharples, Mike
A2 - Klobučar, Tomaž
PB - Springer Verlag
Y2 - 13 September 2016 through 16 September 2016
ER -