Predicting the Global Impact of Authors from the Learning Analytics Community--A Case Study Grounded in CNA [Konferenzbericht] Paper presented at the International Conference on Control Systems and Computer Science (23rd, Bucharest, Romania, May 26-28, 2021).

Autor/inn/en	Ionita, Remus Florentin; Dascalu, Mihai; Corlatescu, Dragos-Georgian; McNamara, Danielle S
Titel	Predicting the Global Impact of Authors from the Learning Analytics Community--A Case Study Grounded in CNA [Konferenzbericht] Paper presented at the International Conference on Control Systems and Computer Science (23rd, Bucharest, Romania, May 26-28, 2021).
Quelle	(2021), (9 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Prediction; Learning Analytics; Authors; Network Analysis; Global Approach; Journal Articles; Semantics + Suchen Sie Ihr Suchwort? Vorhersage; Author; Autor; Autorin; Netzplantechnik; Globales Denken; Journal article; Zeitschriftenaufsatz; Semantik
Abstract	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. (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01