View in EDS

Design and Validation of a Diagnostic MOOC Evaluation Method Combining AHP and Text Mining Algorithms

Saved in:
Bibliographic Details
Title: Design and Validation of a Diagnostic MOOC Evaluation Method Combining AHP and Text Mining Algorithms
Language: English
Authors: Nie, Yanjiao (ORCID 0000-0002-7483-9269), Luo, Heng (ORCID 0000-0002-6551-8885), Sun, Di (ORCID 0000-0003-3801-9340)
Source: Interactive Learning Environments. 2021 29(2):315-328.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 14
Publication Date: 2021
Document Type: Journal Articles
Reports - Research
Descriptors: Online Courses, Evaluation Methods, Course Evaluation, Mathematics, Data Analysis, Accuracy, Validity, Educational Quality, Standards
DOI: 10.1080/10494820.2020.1802298
ISSN: 1049-4820
Abstract: The proliferation of massive open online courses (MOOCs) highlights the necessity of developing accurate and diagnostic evaluation methods to assess the courses' quality and effectiveness. Hence, this study proposes a diagnostic MOOC evaluation (DME) method that combines the Analytic Hierarchy Process algorithm and learner review mining to integrate expert opinions, standardized rubrics, and learner feedback into the evaluation process. For this purpose, 30 MOOCs from the Coursera website were purposively selected and evaluated using the DME method and the results compared with expert evaluation and learner rating scores. The preliminary findings, in general, support the feasibility, accuracy, and diagnostic utility of the DME method and its suitability as a low-cost, sophisticated, and accurate method for MOOC evaluation. Finally, the study discusses several limitations and technical issues of the DME method that should be addressed in future research and practice.
Abstractor: As Provided
Entry Date: 2021
Accession Number: EJ1292442
Database: ERIC
FullText Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: EJ1292442
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Design and Validation of a Diagnostic MOOC Evaluation Method Combining AHP and Text Mining Algorithms
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Nie%2C+Yanjiao%22">Nie, Yanjiao</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-7483-9269">0000-0002-7483-9269</externalLink>)<br /><searchLink fieldCode="AR" term="%22Luo%2C+Heng%22">Luo, Heng</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-6551-8885">0000-0002-6551-8885</externalLink>)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Di%22">Sun, Di</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-3801-9340">0000-0003-3801-9340</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Interactive+Learning+Environments%22"><i>Interactive Learning Environments</i></searchLink>. 2021 29(2):315-328.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 14
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2021
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Online+Courses%22">Online Courses</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Methods%22">Evaluation Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Course+Evaluation%22">Course Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics%22">Mathematics</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Validity%22">Validity</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Quality%22">Educational Quality</searchLink><br /><searchLink fieldCode="DE" term="%22Standards%22">Standards</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/10494820.2020.1802298
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1049-4820
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The proliferation of massive open online courses (MOOCs) highlights the necessity of developing accurate and diagnostic evaluation methods to assess the courses' quality and effectiveness. Hence, this study proposes a diagnostic MOOC evaluation (DME) method that combines the Analytic Hierarchy Process algorithm and learner review mining to integrate expert opinions, standardized rubrics, and learner feedback into the evaluation process. For this purpose, 30 MOOCs from the Coursera website were purposively selected and evaluated using the DME method and the results compared with expert evaluation and learner rating scores. The preliminary findings, in general, support the feasibility, accuracy, and diagnostic utility of the DME method and its suitability as a low-cost, sophisticated, and accurate method for MOOC evaluation. Finally, the study discusses several limitations and technical issues of the DME method that should be addressed in future research and practice.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2021
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1292442
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1292442
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/10494820.2020.1802298
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 315
    Subjects:
      – SubjectFull: Online Courses
        Type: general
      – SubjectFull: Evaluation Methods
        Type: general
      – SubjectFull: Course Evaluation
        Type: general
      – SubjectFull: Mathematics
        Type: general
      – SubjectFull: Data Analysis
        Type: general
      – SubjectFull: Accuracy
        Type: general
      – SubjectFull: Validity
        Type: general
      – SubjectFull: Educational Quality
        Type: general
      – SubjectFull: Standards
        Type: general
    Titles:
      – TitleFull: Design and Validation of a Diagnostic MOOC Evaluation Method Combining AHP and Text Mining Algorithms
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Nie, Yanjiao
      – PersonEntity:
          Name:
            NameFull: Luo, Heng
      – PersonEntity:
          Name:
            NameFull: Sun, Di
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2021
          Identifiers:
            – Type: issn-print
              Value: 1049-4820
          Numbering:
            – Type: volume
              Value: 29
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Interactive Learning Environments
              Type: main
ResultId 1