View in EDS

Prediction of Performance in Standardised Assessments from Computer-Based Formative Assessment Data.

Saved in:
Bibliographic Details
Title: Prediction of Performance in Standardised Assessments from Computer-Based Formative Assessment Data.
Authors: Garzón, Benjamín1 (AUTHOR) benjamin.garzon@econ.uzh.ch, Berger, Stéphanie2 (AUTHOR), Driver, Charles C.1 (AUTHOR), Tomasik, Martin J.1 (AUTHOR)
Source: Technology, Knowledge & Learning. Jun2026, Vol. 31 Issue 2, p967-995. 29p.
Subject Terms: *Formative evaluation, *Summative tests, *Academic achievement, *Educational tests & measurements, *Outcome-based education, Regression analysis
Abstract: Summative assessments (SAs) and formative assessments (FAs) fulfil complementary functions in the educational endeavour. SAs measure knowledge at the end of a unit in a standardised, high-stakes setting, while FAs evaluate student performance during daily classroom activities to tailor feedback and instruction. Computer-based FA (CBFA) systems enable collecting unprecedented amounts of data objectively and with minimal disruption for students, under conditions that more closely resemble real-life behaviour. Given concerns about student stress and ecological validity associated with SAs, potential biases in teacher judgements, and the high burden entailed by traditional classroom assessments, we investigated whether and how well FA outcomes can predict SA outcomes. Specifically, we estimated student abilities in a large sample of children evaluated at different time points during compulsory schooling and performed a systematic comparison of regression models trained to predict SA abilities on different subsets of features derived from FA abilities and auxiliary variables. A model that included mean abilities in different competence domains performed best, accounting for a considerable proportion of variance (30–48%), although this was still below that explained by past SA measures. Most predictive FA features generally corresponded to abilities from the same or a similar competence domain as the predicted SA ability. We report systematic model biases that would warrant consideration when using the models for decision-making. Our findings provide valuable insights into how learning progress connects to future achievement, which can help teachers adapt instruction earlier and inform policies to reduce reliance on high-stakes testing. [ABSTRACT FROM AUTHOR]
Copyright of Technology, Knowledge & Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
FullText Text:
  Availability: 0
Header DbId: ehh
DbLabel: Education Research Complete
An: 195071861
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Prediction of Performance in Standardised Assessments from Computer-Based Formative Assessment Data.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Garzón%2C+Benjamín%22">Garzón, Benjamín</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> benjamin.garzon@econ.uzh.ch</i><br /><searchLink fieldCode="AR" term="%22Berger%2C+Stéphanie%22">Berger, Stéphanie</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Driver%2C+Charles+C%2E%22">Driver, Charles C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tomasik%2C+Martin+J%2E%22">Tomasik, Martin J.</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Technology%2C+Knowledge+%26+Learning%22">Technology, Knowledge & Learning</searchLink>. Jun2026, Vol. 31 Issue 2, p967-995. 29p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Formative+evaluation%22">Formative evaluation</searchLink><br />*<searchLink fieldCode="DE" term="%22Summative+tests%22">Summative tests</searchLink><br />*<searchLink fieldCode="DE" term="%22Academic+achievement%22">Academic achievement</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+tests+%26+measurements%22">Educational tests & measurements</searchLink><br />*<searchLink fieldCode="DE" term="%22Outcome-based+education%22">Outcome-based education</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Summative assessments (SAs) and formative assessments (FAs) fulfil complementary functions in the educational endeavour. SAs measure knowledge at the end of a unit in a standardised, high-stakes setting, while FAs evaluate student performance during daily classroom activities to tailor feedback and instruction. Computer-based FA (CBFA) systems enable collecting unprecedented amounts of data objectively and with minimal disruption for students, under conditions that more closely resemble real-life behaviour. Given concerns about student stress and ecological validity associated with SAs, potential biases in teacher judgements, and the high burden entailed by traditional classroom assessments, we investigated whether and how well FA outcomes can predict SA outcomes. Specifically, we estimated student abilities in a large sample of children evaluated at different time points during compulsory schooling and performed a systematic comparison of regression models trained to predict SA abilities on different subsets of features derived from FA abilities and auxiliary variables. A model that included mean abilities in different competence domains performed best, accounting for a considerable proportion of variance (30–48%), although this was still below that explained by past SA measures. Most predictive FA features generally corresponded to abilities from the same or a similar competence domain as the predicted SA ability. We report systematic model biases that would warrant consideration when using the models for decision-making. Our findings provide valuable insights into how learning progress connects to future achievement, which can help teachers adapt instruction earlier and inform policies to reduce reliance on high-stakes testing. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Technology, Knowledge & Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=195071861
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10758-025-09947-2
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 29
        StartPage: 967
    Subjects:
      – SubjectFull: Formative evaluation
        Type: general
      – SubjectFull: Summative tests
        Type: general
      – SubjectFull: Academic achievement
        Type: general
      – SubjectFull: Educational tests & measurements
        Type: general
      – SubjectFull: Outcome-based education
        Type: general
      – SubjectFull: Regression analysis
        Type: general
    Titles:
      – TitleFull: Prediction of Performance in Standardised Assessments from Computer-Based Formative Assessment Data.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Garzón, Benjamín
      – PersonEntity:
          Name:
            NameFull: Berger, Stéphanie
      – PersonEntity:
          Name:
            NameFull: Driver, Charles C.
      – PersonEntity:
          Name:
            NameFull: Tomasik, Martin J.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 22111662
          Numbering:
            – Type: volume
              Value: 31
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Technology, Knowledge & Learning
              Type: main
ResultId 1