Predicting the Global Impact of Authors from the Learning Analytics Community--A Case Study Grounded in CNA
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| Title: | Predicting the Global Impact of Authors from the Learning Analytics Community--A Case Study Grounded in CNA |
|---|---|
| Language: | English |
| Authors: | Ionita, Remus Florentin, Dascalu, Mihai, Corlatescu, Dragos-Georgian, McNamara, Danielle S |
| Source: | Grantee Submission. 2021. |
| Peer Reviewed: | Y |
| Page Count: | 9 |
| Publication Date: | 2021 |
| Sponsoring Agency: | Office of Naval Research (ONR) (DOD) Institute of Education Sciences (ED) |
| Contract Number: | N000141712300 N000141912424 R305A180144 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Descriptors: | Prediction, Learning Analytics, Authors, Network Analysis, Global Approach, Journal Articles, Semantics |
| DOI: | 10.1109/CSCS52396.2021.00078 |
| 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. |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2023 |
| Accession Number: | ED628428 |
| Database: | ERIC |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED628428 Name: ERIC Full Text Category: fullText Text: Full Text from ERIC |
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| Items | – Name: Title Label: Title Group: Ti Data: Predicting the Global Impact of Authors from the Learning Analytics Community--A Case Study Grounded in CNA – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ionita%2C+Remus+Florentin%22">Ionita, Remus Florentin</searchLink><br /><searchLink fieldCode="AR" term="%22Dascalu%2C+Mihai%22">Dascalu, Mihai</searchLink><br /><searchLink fieldCode="AR" term="%22Corlatescu%2C+Dragos-Georgian%22">Corlatescu, Dragos-Georgian</searchLink><br /><searchLink fieldCode="AR" term="%22McNamara%2C+Danielle+S%22">McNamara, Danielle S</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Grantee+Submission%22"><i>Grantee Submission</i></searchLink>. 2021. – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 9 – Name: DatePubCY Label: Publication Date Group: Date Data: 2021 – Name: SourceSuprt Label: Sponsoring Agency Group: SrcSuprt Data: Office of Naval Research (ONR) (DOD)<br />Institute of Education Sciences (ED) – Name: NumberContract Label: Contract Number Group: NumCntrct Data: N000141712300<br />N000141912424<br />R305A180144 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Speeches/Meeting Papers<br />Reports - Research – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Authors%22">Authors</searchLink><br /><searchLink fieldCode="DE" term="%22Network+Analysis%22">Network Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Global+Approach%22">Global Approach</searchLink><br /><searchLink fieldCode="DE" term="%22Journal+Articles%22">Journal Articles</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink> – Name: DOI Label: DOI Group: ID Data: 10.1109/CSCS52396.2021.00078 – Name: Abstract Label: Abstract Group: Ab Data: 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. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: CodeSource Label: IES Funded Group: SrcInfo Data: Yes – Name: DateEntry Label: Entry Date Group: Date Data: 2023 – Name: AN Label: Accession Number Group: ID Data: ED628428 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED628428 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/CSCS52396.2021.00078 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 9 Subjects: – SubjectFull: Prediction Type: general – SubjectFull: Learning Analytics Type: general – SubjectFull: Authors Type: general – SubjectFull: Network Analysis Type: general – SubjectFull: Global Approach Type: general – SubjectFull: Journal Articles Type: general – SubjectFull: Semantics Type: general Titles: – TitleFull: Predicting the Global Impact of Authors from the Learning Analytics Community--A Case Study Grounded in CNA Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ionita, Remus Florentin – PersonEntity: Name: NameFull: Dascalu, Mihai – PersonEntity: Name: NameFull: Corlatescu, Dragos-Georgian – PersonEntity: Name: NameFull: McNamara, Danielle S IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Type: published Y: 2021 Titles: – TitleFull: Grantee Submission Type: main |
| ResultId | 1 |
