View in EDS PDF Full Text

A Systematic Review of Big Data Driven Education Evaluation

Saved in:
Bibliographic Details
Title: A Systematic Review of Big Data Driven Education Evaluation
Language: English
Authors: Lin Lin, Danhua Zhou, Jingying Wang (ORCID 0000-0002-6109-7542), Yu Wang
Source: SAGE Open. 2024 14(2).
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 18
Publication Date: 2024
Document Type: Journal Articles
Information Analyses
Descriptors: Data Analysis, Educational Research, Geographic Regions, Periodicals, Authors, Evaluation Methods, Algorithms, Intellectual Disciplines, Research Methodology, Objectives, Artificial Intelligence
DOI: 10.1177/21582440241242180
ISSN: 2158-2440
Abstract: The rapid development of artificial intelligence has driven the transformation of educational evaluation into big data-driven. This study used a systematic literature review method to analyzed 44 empirical research articles on the evaluation of big data education. Firstly, it has shown an increasing trend year by year, and is mainly published in thematic journals such as educational technology, science education, and language teaching. Chinese and American researchers have made the greatest contributions in this field. Secondly, the algorithmic models for big data education evaluation research are diverse, the text modality is the most popular, the evaluation subjects are mainly college students, with fewer primary and secondary school students, and science is the discipline that most commonly applies big data education evaluation. The evaluation objectives of big data education evaluation mainly focus on five aspects: high-order thinking analysis, learning performance prediction, learning emotion recognition, teaching management decision-making, and evaluation mode optimization, and the text modality is widely used for data collection in high-order thinking analysis; regardless of the evaluation objectives, higher education students are the most widely evaluated objects; the science discipline is the main field of using big data technology to empower teaching evaluation. Thirdly, the current research topics of big data education evaluation mainly focus on online learning behavior and environmental participation evaluation, process assessment of learning motivation and emotional analysis, development and optimization of subject domain big data models, cognitive diagnosis and high-order thinking skills evaluation, and design of learning analysis frameworks based on data mining.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1433293
Database: ERIC
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwE1R88dIcsvId770jRdLj5ZAAAA4zCB4AYJKoZIhvcNAQcGoIHSMIHPAgEAMIHJBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDOkNp_Q5r-XR84AJhwIBEICBm_EjucvI5XI-KHMNmapDrKJ2xnSTDrJkBspaM9Q1gLIlZjaqKNrC4BY2rxdiC4v-EOV9rLpo_uQG-o7tWDKGyIhWAZSeC5ZX1lo9KBBYBB0SVnmz-Es6SQvm_T4ttoIDptn_vy_UKCtcYz_KupuEZOIwK24gxfLT7mqpmLpZpFzkbGLOh94Eoon7tnMT2XdOlSKXTi3gJF4paRyh
Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: EJ1433293
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Systematic Review of Big Data Driven Education Evaluation
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Lin+Lin%22">Lin Lin</searchLink><br /><searchLink fieldCode="AR" term="%22Danhua+Zhou%22">Danhua Zhou</searchLink><br /><searchLink fieldCode="AR" term="%22Jingying+Wang%22">Jingying Wang</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6109-7542">0000-0002-6109-7542</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yu+Wang%22">Yu Wang</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22SAGE+Open%22"><i>SAGE Open</i></searchLink>. 2024 14(2).
– Name: Avail
  Label: Availability
  Group: Avail
  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 18
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2024
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Information Analyses
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Research%22">Educational Research</searchLink><br /><searchLink fieldCode="DE" term="%22Geographic+Regions%22">Geographic Regions</searchLink><br /><searchLink fieldCode="DE" term="%22Periodicals%22">Periodicals</searchLink><br /><searchLink fieldCode="DE" term="%22Authors%22">Authors</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Methods%22">Evaluation Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Intellectual+Disciplines%22">Intellectual Disciplines</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Methodology%22">Research Methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Objectives%22">Objectives</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/21582440241242180
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2158-2440
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The rapid development of artificial intelligence has driven the transformation of educational evaluation into big data-driven. This study used a systematic literature review method to analyzed 44 empirical research articles on the evaluation of big data education. Firstly, it has shown an increasing trend year by year, and is mainly published in thematic journals such as educational technology, science education, and language teaching. Chinese and American researchers have made the greatest contributions in this field. Secondly, the algorithmic models for big data education evaluation research are diverse, the text modality is the most popular, the evaluation subjects are mainly college students, with fewer primary and secondary school students, and science is the discipline that most commonly applies big data education evaluation. The evaluation objectives of big data education evaluation mainly focus on five aspects: high-order thinking analysis, learning performance prediction, learning emotion recognition, teaching management decision-making, and evaluation mode optimization, and the text modality is widely used for data collection in high-order thinking analysis; regardless of the evaluation objectives, higher education students are the most widely evaluated objects; the science discipline is the main field of using big data technology to empower teaching evaluation. Thirdly, the current research topics of big data education evaluation mainly focus on online learning behavior and environmental participation evaluation, process assessment of learning motivation and emotional analysis, development and optimization of subject domain big data models, cognitive diagnosis and high-order thinking skills evaluation, and design of learning analysis frameworks based on data mining.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1433293
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1433293
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/21582440241242180
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
    Subjects:
      – SubjectFull: Data Analysis
        Type: general
      – SubjectFull: Educational Research
        Type: general
      – SubjectFull: Geographic Regions
        Type: general
      – SubjectFull: Periodicals
        Type: general
      – SubjectFull: Authors
        Type: general
      – SubjectFull: Evaluation Methods
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Intellectual Disciplines
        Type: general
      – SubjectFull: Research Methodology
        Type: general
      – SubjectFull: Objectives
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
    Titles:
      – TitleFull: A Systematic Review of Big Data Driven Education Evaluation
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Lin Lin
      – PersonEntity:
          Name:
            NameFull: Danhua Zhou
      – PersonEntity:
          Name:
            NameFull: Jingying Wang
      – PersonEntity:
          Name:
            NameFull: Yu Wang
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-electronic
              Value: 2158-2440
          Numbering:
            – Type: volume
              Value: 14
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: SAGE Open
              Type: main
ResultId 1