Are High School Students Accurate in Predicting Their AP Exam Scores?: Examining Inaccuracy and Overconfidence of Students' Predictions

Saved in:
Bibliographic Details
Title: Are High School Students Accurate in Predicting Their AP Exam Scores?: Examining Inaccuracy and Overconfidence of Students' Predictions
Language: English
Authors: Teresa M. Ober (ORCID 0000-0001-9698-9543), Maxwell R. Hong, Matthew F. Carter, Alex S. Brodersen, Daniella Rebouças-Ju, Cheng Liu, Ying Cheng
Source: Grantee Submission. 2021.
Peer Reviewed: Y
Page Count: 26
Publication Date: 2021
Sponsoring Agency: National Science Foundation (NSF), Division of Research on Learning in Formal and Informal Settings (DRL)
Contract Number: 1350787
Document Type: Reports - Research
Education Level: High Schools
Secondary Education
Descriptors: High School Students, Self Evaluation (Individuals), Student Attitudes, High Stakes Tests, Advanced Placement Programs, Tests, Student Evaluation, Accuracy, Scores, Age Differences, Gender Differences, Parent Background, Educational Attainment, Mathematics Instruction, Correlation, Classroom Environment, Learner Engagement, Context Effect, Statistics, Institutional Characteristics, Prior Learning
DOI: 10.1080/0969594X.2022.2037508
Abstract: We examined whether students were accurate in predicting their test performance two testing contexts (low-stakes and high-stakes). The sample comprised U.S. high school students enrolled in an advanced placement (AP) statistics course during the 2017-2018 academic year (N=209; M[subscript age]=16.6 years). We found that even two months before taking the AP exam, a high stakes summative assessment, students were moderately accurate in predicting their actual scores ([kappa][subscript weighted]=0.62). When the same variables were entered into models predicting inaccuracy and overconfidence bias, results did not provide evidence that age, gender, parental education, number of math classes previously taken, or course engagement accounted for variation in accuracy. Overconfidence bias differed between students enrolled at different schools. Results indicated that students' predictions of performance were positively associated with performance in both low- and high-stakes testing contexts. The findings shed light on ways to leverage students' self-assessment for learning.
Abstractor: As Provided
Entry Date: 2024
Accession Number: ED642087
Database: ERIC
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED642087
    Name: ERIC Full Text
    Category: fullText
    Text: Full Text from ERIC
Header DbId: eric
DbLabel: ERIC
An: ED642087
AccessLevel: 3
PubType: Report
PubTypeId: report
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Are High School Students Accurate in Predicting Their AP Exam Scores?: Examining Inaccuracy and Overconfidence of Students' Predictions
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Teresa+M%2E+Ober%22">Teresa M. Ober</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9698-9543">0000-0001-9698-9543</externalLink>)<br /><searchLink fieldCode="AR" term="%22Maxwell+R%2E+Hong%22">Maxwell R. Hong</searchLink><br /><searchLink fieldCode="AR" term="%22Matthew+F%2E+Carter%22">Matthew F. Carter</searchLink><br /><searchLink fieldCode="AR" term="%22Alex+S%2E+Brodersen%22">Alex S. Brodersen</searchLink><br /><searchLink fieldCode="AR" term="%22Daniella+Rebouças-Ju%22">Daniella Rebouças-Ju</searchLink><br /><searchLink fieldCode="AR" term="%22Cheng+Liu%22">Cheng Liu</searchLink><br /><searchLink fieldCode="AR" term="%22Ying+Cheng%22">Ying Cheng</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: 26
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2021
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: National Science Foundation (NSF), Division of Research on Learning in Formal and Informal Settings (DRL)
– Name: NumberContract
  Label: Contract Number
  Group: NumCntrct
  Data: 1350787
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22High+Schools%22">High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22High+School+Students%22">High School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Evaluation+%28Individuals%29%22">Self Evaluation (Individuals)</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22High+Stakes+Tests%22">High Stakes Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Advanced+Placement+Programs%22">Advanced Placement Programs</searchLink><br /><searchLink fieldCode="DE" term="%22Tests%22">Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Evaluation%22">Student Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Scores%22">Scores</searchLink><br /><searchLink fieldCode="DE" term="%22Age+Differences%22">Age Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Parent+Background%22">Parent Background</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Attainment%22">Educational Attainment</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics+Instruction%22">Mathematics Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Correlation%22">Correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Classroom+Environment%22">Classroom Environment</searchLink><br /><searchLink fieldCode="DE" term="%22Learner+Engagement%22">Learner Engagement</searchLink><br /><searchLink fieldCode="DE" term="%22Context+Effect%22">Context Effect</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Institutional+Characteristics%22">Institutional Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Prior+Learning%22">Prior Learning</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/0969594X.2022.2037508
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: We examined whether students were accurate in predicting their test performance two testing contexts (low-stakes and high-stakes). The sample comprised U.S. high school students enrolled in an advanced placement (AP) statistics course during the 2017-2018 academic year (N=209; M[subscript age]=16.6 years). We found that even two months before taking the AP exam, a high stakes summative assessment, students were moderately accurate in predicting their actual scores ([kappa][subscript weighted]=0.62). When the same variables were entered into models predicting inaccuracy and overconfidence bias, results did not provide evidence that age, gender, parental education, number of math classes previously taken, or course engagement accounted for variation in accuracy. Overconfidence bias differed between students enrolled at different schools. Results indicated that students' predictions of performance were positively associated with performance in both low- and high-stakes testing contexts. The findings shed light on ways to leverage students' self-assessment for learning.
– 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: ED642087
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED642087
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/0969594X.2022.2037508
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 26
    Subjects:
      – SubjectFull: High School Students
        Type: general
      – SubjectFull: Self Evaluation (Individuals)
        Type: general
      – SubjectFull: Student Attitudes
        Type: general
      – SubjectFull: High Stakes Tests
        Type: general
      – SubjectFull: Advanced Placement Programs
        Type: general
      – SubjectFull: Tests
        Type: general
      – SubjectFull: Student Evaluation
        Type: general
      – SubjectFull: Accuracy
        Type: general
      – SubjectFull: Scores
        Type: general
      – SubjectFull: Age Differences
        Type: general
      – SubjectFull: Gender Differences
        Type: general
      – SubjectFull: Parent Background
        Type: general
      – SubjectFull: Educational Attainment
        Type: general
      – SubjectFull: Mathematics Instruction
        Type: general
      – SubjectFull: Correlation
        Type: general
      – SubjectFull: Classroom Environment
        Type: general
      – SubjectFull: Learner Engagement
        Type: general
      – SubjectFull: Context Effect
        Type: general
      – SubjectFull: Statistics
        Type: general
      – SubjectFull: Institutional Characteristics
        Type: general
      – SubjectFull: Prior Learning
        Type: general
    Titles:
      – TitleFull: Are High School Students Accurate in Predicting Their AP Exam Scores?: Examining Inaccuracy and Overconfidence of Students' Predictions
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Teresa M. Ober
      – PersonEntity:
          Name:
            NameFull: Maxwell R. Hong
      – PersonEntity:
          Name:
            NameFull: Matthew F. Carter
      – PersonEntity:
          Name:
            NameFull: Alex S. Brodersen
      – PersonEntity:
          Name:
            NameFull: Daniella Rebouças-Ju
      – PersonEntity:
          Name:
            NameFull: Cheng Liu
      – PersonEntity:
          Name:
            NameFull: Ying Cheng
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 14
              M: 11
              Type: published
              Y: 2021
          Titles:
            – TitleFull: Grantee Submission
              Type: main
ResultId 1