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Measuring Upper-Elementary Students' Understanding of AI Concepts -- A Rasch Model Analysis

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Title: Measuring Upper-Elementary Students' Understanding of AI Concepts -- A Rasch Model Analysis
Language: English
Authors: Srijita Chakraburty, Krista D. Glazewski, Cindy E. Hmelo-Silver, Dubravka Svetina Valdivia, Anne Ottenbreit-Leftwich, Bradford Mott, James Lester
Source: Information and Learning Sciences. 2025 126(7-8):445-471.
Availability: Emerald Publishing Limited. Howard House, Wagon Lane, Bingley, West Yorkshire, BD16 1WA, UK. Tel: +44-1274-777700; Fax: +44-1274-785201; e-mail: emerald@emeraldinsight.com; Web site: http://www.emerald.com/insight
Peer Reviewed: Y
Page Count: 27
Publication Date: 2025
Sponsoring Agency: National Science Foundation (NSF), Division of Research on Learning in Formal and Informal Settings (DRL)
Contract Number: 1934128
1934153
Document Type: Journal Articles
Reports - Research
Education Level: Elementary Education
Early Childhood Education
Grade 3
Primary Education
Grade 4
Intermediate Grades
Grade 5
Middle Schools
Descriptors: Elementary School Students, Knowledge Level, Artificial Intelligence, Student Evaluation, Test Reliability, Concept Formation, Developmentally Appropriate Practices, Grade 3, Grade 4, Grade 5
DOI: 10.1108/ILS-10-2023-0164
ISSN: 2398-5348
2398-5356
Abstract: Purpose: This paper aims to introduce a novel AI learning progression for upper-elementary students and aligns assessment items across levels of each construct to gather evidence of understanding. It also validates this quantitative measure by examining these items as two subscales for psychometric properties using the Rasch model. Design/methodology/approach: Conducting a cognitive analysis of diverse data sources, including the AI4K12 big ideas (Touretzky et al., 2019), student performance on assessment items, and classroom activities from prior implementations of an AI curriculum intervention (Glazewski et al., 2022), and drawing insights from subject matter experts, this paper outlines the design of the learning progression. The second section delves into the refinement and mapping of assessment items and an evaluation of their psychometric properties to ensure the reliable placement of students within the progression. Findings: This project identified key starting points for students and outlined how their understanding of core AI concepts should develop. The validation of the two subscales resulted in a reliable tool for accurately assessing students' AI abilities. This tool helps educators match assessment questions to students' current understanding and guide their progression through the learning journey. Originality/value: This learning progression offers a unique framework for teaching AI to younger students, addressing a gap in K-12 education. It provides a roadmap for progressively teaching AI concepts, allowing educators to design lessons and assessments that are appropriate for students' developmental stages.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1489034
Database: ERIC
FullText Text:
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  Data: Measuring Upper-Elementary Students' Understanding of AI Concepts -- A Rasch Model Analysis
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  Data: <searchLink fieldCode="AR" term="%22Srijita+Chakraburty%22">Srijita Chakraburty</searchLink><br /><searchLink fieldCode="AR" term="%22Krista+D%2E+Glazewski%22">Krista D. Glazewski</searchLink><br /><searchLink fieldCode="AR" term="%22Cindy+E%2E+Hmelo-Silver%22">Cindy E. Hmelo-Silver</searchLink><br /><searchLink fieldCode="AR" term="%22Dubravka+Svetina+Valdivia%22">Dubravka Svetina Valdivia</searchLink><br /><searchLink fieldCode="AR" term="%22Anne+Ottenbreit-Leftwich%22">Anne Ottenbreit-Leftwich</searchLink><br /><searchLink fieldCode="AR" term="%22Bradford+Mott%22">Bradford Mott</searchLink><br /><searchLink fieldCode="AR" term="%22James+Lester%22">James Lester</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Information+and+Learning+Sciences%22"><i>Information and Learning Sciences</i></searchLink>. 2025 126(7-8):445-471.
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  Data: Emerald Publishing Limited. Howard House, Wagon Lane, Bingley, West Yorkshire, BD16 1WA, UK. Tel: +44-1274-777700; Fax: +44-1274-785201; e-mail: emerald@emeraldinsight.com; Web site: http://www.emerald.com/insight
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  Data: 27
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  Data: National Science Foundation (NSF), Division of Research on Learning in Formal and Informal Settings (DRL)
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  Data: 1934128<br />1934153
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Elementary+Education%22">Elementary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Early+Childhood+Education%22">Early Childhood Education</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+3%22">Grade 3</searchLink><br /><searchLink fieldCode="EL" term="%22Primary+Education%22">Primary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+4%22">Grade 4</searchLink><br /><searchLink fieldCode="EL" term="%22Intermediate+Grades%22">Intermediate Grades</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+5%22">Grade 5</searchLink><br /><searchLink fieldCode="EL" term="%22Middle+Schools%22">Middle Schools</searchLink>
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  Data: 10.1108/ILS-10-2023-0164
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  Data: 2398-5348<br />2398-5356
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: This paper aims to introduce a novel AI learning progression for upper-elementary students and aligns assessment items across levels of each construct to gather evidence of understanding. It also validates this quantitative measure by examining these items as two subscales for psychometric properties using the Rasch model. Design/methodology/approach: Conducting a cognitive analysis of diverse data sources, including the AI4K12 big ideas (Touretzky et al., 2019), student performance on assessment items, and classroom activities from prior implementations of an AI curriculum intervention (Glazewski et al., 2022), and drawing insights from subject matter experts, this paper outlines the design of the learning progression. The second section delves into the refinement and mapping of assessment items and an evaluation of their psychometric properties to ensure the reliable placement of students within the progression. Findings: This project identified key starting points for students and outlined how their understanding of core AI concepts should develop. The validation of the two subscales resulted in a reliable tool for accurately assessing students' AI abilities. This tool helps educators match assessment questions to students' current understanding and guide their progression through the learning journey. Originality/value: This learning progression offers a unique framework for teaching AI to younger students, addressing a gap in K-12 education. It provides a roadmap for progressively teaching AI concepts, allowing educators to design lessons and assessments that are appropriate for students' developmental stages.
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  Data: EJ1489034
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        Value: 10.1108/ILS-10-2023-0164
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      – Text: English
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      Pagination:
        PageCount: 27
        StartPage: 445
    Subjects:
      – SubjectFull: Elementary School Students
        Type: general
      – SubjectFull: Knowledge Level
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Student Evaluation
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      – SubjectFull: Concept Formation
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      – SubjectFull: Grade 5
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      – TitleFull: Measuring Upper-Elementary Students' Understanding of AI Concepts -- A Rasch Model Analysis
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