TY - GEN
T1 - Predicting the Global Impact of Authors from the Learning Analytics Community - A Case Study grounded in CNA
AU - Ionita, Remus Florentin
AU - Corlatescu, Dragos Georgian
AU - Dascalu, Mihai
AU - McNamara, Danielle S.
N1 - Funding Information:
This research was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI, project number TE 70 PN-III-P1-1.1- TE-2019-2209, ATES - Automated Text Evaluation and Simplification, and by the Office of Naval Research (Grants: N00014-17-1-2300 and N00014-19-1-2424) and the Institute of Education Sciences (R305A180144)
Funding Information:
This research was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS – UEFISCDI, project number TE 70 PN-III-P1-1.1-TE-2019-2209, ATES – “Automated Text Evaluation and Simplification”, and by the Office of Naval Research (Grants: N00014-17-1-2300 and N00014-19-1-2424) and the Institute of Education Sciences (R305A180144). The opinions expressed are those of the authors and do not represent the views of these granting agencies.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/5
Y1 - 2021/5
N2 - Exploring new or emerging research domains or subdomains can become overwhelming due to the magnitude of available resources and the high speed at which articles are published. As such, a tool that curates the information and underlines central entities, both authors and articles from a given research context, is highly desirable. Starting from the articles of the International Conference of Learning Analytics Knowledge (LAK) in its first decade, this paper proposes a novel method grounded in Cohesion Network Analysis (CNA) to analyze subcommunities of authors based on the semantic similarities between authors and papers, and estimate their global impact. Paper abstracts are represented as embeddings using a fine-tuned SciBERT language model, alongside a custom trained LSA model. The extrapolation between the local LAK community to a worldwide importance was also underlined by the comparison between the rankings obtained from our method and statistics from ResearchGate. The accuracies for binary classifications in terms of high/low impact predictions were around 70% for authors, and around 80% for articles. Our method can guide researchers by providing valuable information on the interactions between the members of a knowledge community and by highlighting central local authors who may potentially have a high global impact.
AB - Exploring new or emerging research domains or subdomains can become overwhelming due to the magnitude of available resources and the high speed at which articles are published. As such, a tool that curates the information and underlines central entities, both authors and articles from a given research context, is highly desirable. Starting from the articles of the International Conference of Learning Analytics Knowledge (LAK) in its first decade, this paper proposes a novel method grounded in Cohesion Network Analysis (CNA) to analyze subcommunities of authors based on the semantic similarities between authors and papers, and estimate their global impact. Paper abstracts are represented as embeddings using a fine-tuned SciBERT language model, alongside a custom trained LSA model. The extrapolation between the local LAK community to a worldwide importance was also underlined by the comparison between the rankings obtained from our method and statistics from ResearchGate. The accuracies for binary classifications in terms of high/low impact predictions were around 70% for authors, and around 80% for articles. Our method can guide researchers by providing valuable information on the interactions between the members of a knowledge community and by highlighting central local authors who may potentially have a high global impact.
KW - Cohesion Network Analysis
KW - Global Author and Paper Impact
KW - Semantic and Co-authorship Links
UR - http://www.scopus.com/inward/record.url?scp=85112058922&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85112058922&partnerID=8YFLogxK
U2 - 10.1109/CSCS52396.2021.00078
DO - 10.1109/CSCS52396.2021.00078
M3 - Conference contribution
AN - SCOPUS:85112058922
T3 - Proceedings - 2021 23rd International Conference on Control Systems and Computer Science Technologies, CSCS 2021
SP - 439
EP - 446
BT - Proceedings - 2021 23rd International Conference on Control Systems and Computer Science Technologies, CSCS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd International Conference on Control Systems and Computer Science Technologies, CSCS 2021
Y2 - 26 May 2021 through 28 May 2021
ER -