View in EDS

Exploring Writing Analytics and Postsecondary Success Indicators

Saved in:
Bibliographic Details
Title: Exploring Writing Analytics and Postsecondary Success Indicators
Language: English
Authors: Burstein, Jill, McCaffrey, Daniel, Beigman Klebanov, Beata, Ling, Guangming, Holtzman, Steven
Source: Grantee Submission. 2019Paper presented at the International Conference on Learning Analytics & Knowledge (9th, Tempe, AZ, Mar 2019).
Peer Reviewed: Y
Page Count: 4
Publication Date: 2019
Sponsoring Agency: Institute of Education Sciences (ED)
Contract Number: R305A160115
Document Type: Reports - Research
Speeches/Meeting Papers
Education Level: Higher Education
Postsecondary Education
Descriptors: Undergraduate Students, Writing (Composition), Writing Evaluation, Learning Analytics, Writing Research, Grade Point Average, Writing Achievement, Natural Language Processing, Predictor Variables
Abstract: Writing is a challenge and a potential obstacle for students in U.S. 4-year postsecondary institutions lacking prerequisite writing skills. This study aims to address the research question: Is there a relationship between specific features (analytics) in coursework writing and broader success predictors? Knowledge about this relationship could contribute to more immediate personalized learning support for students. To investigate, we collected authentic coursework writing from students enrolled at one of six 4-year colleges. We then extracted natural language processing (NLP) writing features (analytics) from the writing samples and examined relationships between the analytics and college grade point average (GPA). Consistent with Burstein et al. (2017), findings suggest that NLP writing analytics may contribute to college GPA prediction. Our findings imply that real-time NLP writing analytics from authentic coursework writing could be used to efficiently track success and flag potential obstacles during students' college careers. [This paper was published in: "Companion Proceedings 9th International Conference on Learning Analytics & Knowledge" (LAK19), p.213-214, 2019.]
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2019
Accession Number: ED598690
Database: ERIC
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED598690
    Name: ERIC Full Text
    Category: fullText
    Text: Full Text from ERIC
Header DbId: eric
DbLabel: ERIC
An: ED598690
AccessLevel: 3
PubType: Report
PubTypeId: report
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Exploring Writing Analytics and Postsecondary Success Indicators
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Burstein%2C+Jill%22">Burstein, Jill</searchLink><br /><searchLink fieldCode="AR" term="%22McCaffrey%2C+Daniel%22">McCaffrey, Daniel</searchLink><br /><searchLink fieldCode="AR" term="%22Beigman+Klebanov%2C+Beata%22">Beigman Klebanov, Beata</searchLink><br /><searchLink fieldCode="AR" term="%22Ling%2C+Guangming%22">Ling, Guangming</searchLink><br /><searchLink fieldCode="AR" term="%22Holtzman%2C+Steven%22">Holtzman, Steven</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Grantee+Submission%22"><i>Grantee Submission</i></searchLink>. 2019Paper presented at the International Conference on Learning Analytics & Knowledge (9th, Tempe, AZ, Mar 2019).
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 4
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2019
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: Institute of Education Sciences (ED)
– Name: NumberContract
  Label: Contract Number
  Group: NumCntrct
  Data: R305A160115
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Reports - Research<br />Speeches/Meeting Papers
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Writing+%28Composition%29%22">Writing (Composition)</searchLink><br /><searchLink fieldCode="DE" term="%22Writing+Evaluation%22">Writing Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Writing+Research%22">Writing Research</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+Point+Average%22">Grade Point Average</searchLink><br /><searchLink fieldCode="DE" term="%22Writing+Achievement%22">Writing Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+Language+Processing%22">Natural Language Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Writing is a challenge and a potential obstacle for students in U.S. 4-year postsecondary institutions lacking prerequisite writing skills. This study aims to address the research question: Is there a relationship between specific features (analytics) in coursework writing and broader success predictors? Knowledge about this relationship could contribute to more immediate personalized learning support for students. To investigate, we collected authentic coursework writing from students enrolled at one of six 4-year colleges. We then extracted natural language processing (NLP) writing features (analytics) from the writing samples and examined relationships between the analytics and college grade point average (GPA). Consistent with Burstein et al. (2017), findings suggest that NLP writing analytics may contribute to college GPA prediction. Our findings imply that real-time NLP writing analytics from authentic coursework writing could be used to efficiently track success and flag potential obstacles during students' college careers. [This paper was published in: "Companion Proceedings 9th International Conference on Learning Analytics & Knowledge" (LAK19), p.213-214, 2019.]
– 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: 2019
– Name: AN
  Label: Accession Number
  Group: ID
  Data: ED598690
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED598690
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 4
    Subjects:
      – SubjectFull: Undergraduate Students
        Type: general
      – SubjectFull: Writing (Composition)
        Type: general
      – SubjectFull: Writing Evaluation
        Type: general
      – SubjectFull: Learning Analytics
        Type: general
      – SubjectFull: Writing Research
        Type: general
      – SubjectFull: Grade Point Average
        Type: general
      – SubjectFull: Writing Achievement
        Type: general
      – SubjectFull: Natural Language Processing
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
    Titles:
      – TitleFull: Exploring Writing Analytics and Postsecondary Success Indicators
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Burstein, Jill
      – PersonEntity:
          Name:
            NameFull: McCaffrey, Daniel
      – PersonEntity:
          Name:
            NameFull: Beigman Klebanov, Beata
      – PersonEntity:
          Name:
            NameFull: Ling, Guangming
      – PersonEntity:
          Name:
            NameFull: Holtzman, Steven
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 03
              Type: published
              Y: 2019
          Titles:
            – TitleFull: Grantee Submission
              Type: main
ResultId 1