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From replication to extension: Unveiling student profiles in fraction comparison tasks.

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Title: From replication to extension: Unveiling student profiles in fraction comparison tasks.
Authors: Maricau, Flore1,2 (AUTHOR) flore.maricau@kuleuven.be, Reinhold, Frank3 (AUTHOR) frank.reinhold@ph-freiburg.de, Leuders, Timo3 (AUTHOR) leuders@ph-freiburg.de, Loibl, Katharina4 (AUTHOR) katharina.loibl@ph-freiburg.de, Van Dooren, Wim2 (AUTHOR) wim.vandooren@kuleuven.be, Said-Metwaly, Sameh1,5 (AUTHOR) sameh.metwaly@kuleuven.be, Reynvoet, Bert6 (AUTHOR) bert.reynvoet@kuleuven.be, Depaepe, Fien1,2 (AUTHOR) fien.depaepe@kuleuven.be
Source: Contemporary Educational Psychology. Jun2026, Vol. 85, pN.PAG-N.PAG. 1p.
Subject Terms: *Bayesian analysis, *Mathematics education, *Ability grouping (Education), Cognitive bias, Magnitude (Mathematics)
Abstract: [Display omitted] • This study replicated and extended the findings of Reinhold et al. (2023). • A Bayesian theory-driven classification approach was used to cluster students. • We replicated these distinct profiles for both single- and multi-digit fractions. • The results highlight the need of extending the profiles by including gap thinking. Students often encounter difficulties when learning and processing fractions. In fraction comparison tasks, they tend to rely on a mixture of successful strategies, e.g., fraction magnitude processing or benchmarking, and erroneous strategies, e.g., natural number-based reasoning or gap thinking. Reinhold et al. (2023) used a theory-driven approach to classify students into distinct profiles depending on their strategy choice based on their performance on a comparison task with 24 single-digit fractions. The authors identified single strategy profiles (e.g., typical natural number bias) and composite profiles (e.g., benchmarking or typical bias). The current study aimed to replicate this study (RO1) and extend its design by incorporating multi-digit fractions (i.e., the denominator has at least two digits; RO2) and additional biased comparison strategies, specifically gap thinking (RO3). A set of 101 fraction comparison tasks was administered to 285 fifth- and sixth-grade students in Flanders, Belgium, controlling for benchmarking to 1/2, numerical distance, natural number-based reasoning and gap thinking. A Bayesian classification approach based on students' performance (accuracy, reaction time, and individual distance effect) replicated the distinct profiles identified by Reinhold et al. (2023) , not only for single- (RO1) but also for multi-digit fractions (RO2). Furthermore, we identified additional single strategy and composite profiles (e.g., applying benchmarking where possible, otherwise gap thinking), both based on accuracy and reaction time data (RO3). This study enhances our understanding of individual differences in fraction processing and emphasizes the importance of controlling for gap thinking. [ABSTRACT FROM AUTHOR]
Copyright of Contemporary Educational Psychology is the property of Academic Press Inc. 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
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  Data: From replication to extension: Unveiling student profiles in fraction comparison tasks.
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  Data: <searchLink fieldCode="AR" term="%22Maricau%2C+Flore%22">Maricau, Flore</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> flore.maricau@kuleuven.be</i><br /><searchLink fieldCode="AR" term="%22Reinhold%2C+Frank%22">Reinhold, Frank</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> frank.reinhold@ph-freiburg.de</i><br /><searchLink fieldCode="AR" term="%22Leuders%2C+Timo%22">Leuders, Timo</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> leuders@ph-freiburg.de</i><br /><searchLink fieldCode="AR" term="%22Loibl%2C+Katharina%22">Loibl, Katharina</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> katharina.loibl@ph-freiburg.de</i><br /><searchLink fieldCode="AR" term="%22Van+Dooren%2C+Wim%22">Van Dooren, Wim</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> wim.vandooren@kuleuven.be</i><br /><searchLink fieldCode="AR" term="%22Said-Metwaly%2C+Sameh%22">Said-Metwaly, Sameh</searchLink><relatesTo>1,5</relatesTo> (AUTHOR)<i> sameh.metwaly@kuleuven.be</i><br /><searchLink fieldCode="AR" term="%22Reynvoet%2C+Bert%22">Reynvoet, Bert</searchLink><relatesTo>6</relatesTo> (AUTHOR)<i> bert.reynvoet@kuleuven.be</i><br /><searchLink fieldCode="AR" term="%22Depaepe%2C+Fien%22">Depaepe, Fien</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> fien.depaepe@kuleuven.be</i>
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  Data: <searchLink fieldCode="JN" term="%22Contemporary+Educational+Psychology%22">Contemporary Educational Psychology</searchLink>. Jun2026, Vol. 85, pN.PAG-N.PAG. 1p.
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  Data: *<searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematics+education%22">Mathematics education</searchLink><br />*<searchLink fieldCode="DE" term="%22Ability+grouping+%28Education%29%22">Ability grouping (Education)</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+bias%22">Cognitive bias</searchLink><br /><searchLink fieldCode="DE" term="%22Magnitude+%28Mathematics%29%22">Magnitude (Mathematics)</searchLink>
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  Data: [Display omitted] • This study replicated and extended the findings of Reinhold et al. (2023). • A Bayesian theory-driven classification approach was used to cluster students. • We replicated these distinct profiles for both single- and multi-digit fractions. • The results highlight the need of extending the profiles by including gap thinking. Students often encounter difficulties when learning and processing fractions. In fraction comparison tasks, they tend to rely on a mixture of successful strategies, e.g., fraction magnitude processing or benchmarking, and erroneous strategies, e.g., natural number-based reasoning or gap thinking. Reinhold et al. (2023) used a theory-driven approach to classify students into distinct profiles depending on their strategy choice based on their performance on a comparison task with 24 single-digit fractions. The authors identified single strategy profiles (e.g., typical natural number bias) and composite profiles (e.g., benchmarking or typical bias). The current study aimed to replicate this study (RO1) and extend its design by incorporating multi-digit fractions (i.e., the denominator has at least two digits; RO2) and additional biased comparison strategies, specifically gap thinking (RO3). A set of 101 fraction comparison tasks was administered to 285 fifth- and sixth-grade students in Flanders, Belgium, controlling for benchmarking to 1/2, numerical distance, natural number-based reasoning and gap thinking. A Bayesian classification approach based on students' performance (accuracy, reaction time, and individual distance effect) replicated the distinct profiles identified by Reinhold et al. (2023) , not only for single- (RO1) but also for multi-digit fractions (RO2). Furthermore, we identified additional single strategy and composite profiles (e.g., applying benchmarking where possible, otherwise gap thinking), both based on accuracy and reaction time data (RO3). This study enhances our understanding of individual differences in fraction processing and emphasizes the importance of controlling for gap thinking. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Contemporary Educational Psychology is the property of Academic Press Inc. 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.)
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      – Type: doi
        Value: 10.1016/j.cedpsych.2026.102454
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      – Code: eng
        Text: English
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      – SubjectFull: Bayesian analysis
        Type: general
      – SubjectFull: Mathematics education
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      – SubjectFull: Ability grouping (Education)
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      – SubjectFull: Cognitive bias
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      – SubjectFull: Magnitude (Mathematics)
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      – TitleFull: From replication to extension: Unveiling student profiles in fraction comparison tasks.
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              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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