Modelling multiple problem‐solving strategies and strategy shift in cognitive diagnosis for growth.

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Title: Modelling multiple problem‐solving strategies and strategy shift in cognitive diagnosis for growth.
Authors: Liao, Manqian (AUTHOR), Jiao, Hong (AUTHOR)
Source: British Journal of Mathematical & Statistical Psychology. Feb2023, Vol. 76 Issue 1, p20-51. 32p.
Subjects: Problem solving in children, Problem solving, Data analysis, Diagnosis
Abstract: Problem‐solving strategies, defined as actions people select intentionally to achieve desired objectives, are distinguished from skills that are implemented unintentionally. In education, strategy‐oriented instructions that guide students to form problem‐solving strategies are found to be more effective for low‐achieving students than the skill‐oriented instructions designed for enhancing their skill implementation ability. Although the existing longitudinal cognitive diagnosis models (CDMs) can model the change in students' dynamic skill mastery status over time, they are not designed to model the shift in students' problem‐solving strategies. This study proposes a longitudinal CDM that considers both between‐person multiple strategies and within‐person strategy shift. The model, separating the strategy choice process from the skill implementation process, is intended to provide diagnostic information on strategy choice as well as skill mastery status. A simulation study is conducted to evaluate the parameter recovery of the proposed model and investigate the consequences of ignoring the presence of multiple strategies or strategy shift. Further, an empirical data analysis is conducted to illustrate the use of the proposed model to measure strategy shift, growth in skill implementation ability and skill mastery status. [ABSTRACT FROM AUTHOR]
Copyright of British Journal of Mathematical & Statistical Psychology is the property of Wiley-Blackwell 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.)
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  Data: Modelling multiple problem‐solving strategies and strategy shift in cognitive diagnosis for growth.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Liao%2C+Manqian%22">Liao, Manqian</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiao%2C+Hong%22">Jiao, Hong</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22British+Journal+of+Mathematical+%26+Statistical+Psychology%22">British Journal of Mathematical & Statistical Psychology</searchLink>. Feb2023, Vol. 76 Issue 1, p20-51. 32p.
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  Data: <searchLink fieldCode="DE" term="%22Problem+solving+in+children%22">Problem solving in children</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnosis%22">Diagnosis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Problem‐solving strategies, defined as actions people select intentionally to achieve desired objectives, are distinguished from skills that are implemented unintentionally. In education, strategy‐oriented instructions that guide students to form problem‐solving strategies are found to be more effective for low‐achieving students than the skill‐oriented instructions designed for enhancing their skill implementation ability. Although the existing longitudinal cognitive diagnosis models (CDMs) can model the change in students' dynamic skill mastery status over time, they are not designed to model the shift in students' problem‐solving strategies. This study proposes a longitudinal CDM that considers both between‐person multiple strategies and within‐person strategy shift. The model, separating the strategy choice process from the skill implementation process, is intended to provide diagnostic information on strategy choice as well as skill mastery status. A simulation study is conducted to evaluate the parameter recovery of the proposed model and investigate the consequences of ignoring the presence of multiple strategies or strategy shift. Further, an empirical data analysis is conducted to illustrate the use of the proposed model to measure strategy shift, growth in skill implementation ability and skill mastery status. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of British Journal of Mathematical & Statistical Psychology is the property of Wiley-Blackwell 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: 10.1111/bmsp.12280
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      – Code: eng
        Text: English
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        PageCount: 32
        StartPage: 20
    Subjects:
      – SubjectFull: Problem solving in children
        Type: general
      – SubjectFull: Problem solving
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Diagnosis
        Type: general
    Titles:
      – TitleFull: Modelling multiple problem‐solving strategies and strategy shift in cognitive diagnosis for growth.
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            NameFull: Liao, Manqian
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            NameFull: Jiao, Hong
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              M: 02
              Text: Feb2023
              Type: published
              Y: 2023
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              Value: 76
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