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Shaky Student Growth? A Comparison of Robust Bayesian Learning Progress Estimation Methods

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Title: Shaky Student Growth? A Comparison of Robust Bayesian Learning Progress Estimation Methods
Authors: Forthmann, Boris (ORCID 0000-0001-9755-7304), Förster, Natalie (ORCID 0000-0003-0634-5993), Souvignier, Elmar (ORCID 0000-0001-7962-7428)
Source: Journal of Intelligence. 2022 10.
Availability: MDPI AG. Klybeckstrasse 64, 4057 Basel, Switzerland. e-mail: indexing@mdpi.com; e-mail: jintelligence@mdpi.com; Web site: https://www.mdpi.com/journal/jintelligence
Peer Reviewed: Y
Page Count: 16
Publication Date: 2022
Document Type: Journal Articles
Reports - Research
Education Level: Elementary Education
Early Childhood Education
Grade 2
Primary Education
Descriptors: Learning Processes, Progress Monitoring, Robustness (Statistics), Bayesian Statistics, Learning Analytics, Academic Achievement, Growth Models, Elementary School Students, Grade 2, Reading Tests, Reading Comprehension, Prediction, Models, Accuracy, Student Evaluation
ISSN: 2079-3200
Abstract: Monitoring the progress of student learning is an important part of teachers' data-based decision making. One such tool that can equip teachers with information about students' learning progress throughout the school year and thus facilitate monitoring and instructional decision making is learning progress assessments. In practical contexts and research, estimating learning progress has relied on approaches that seek to estimate progress either for each student separately or within overarching model frameworks, such as latent growth modeling. Two recently emerging lines of research for separately estimating student growth have examined robust estimation (to account for outliers) and Bayesian approaches (as opposed to commonly used frequentist methods). The aim of this work was to combine these approaches (i.e., robust Bayesian estimation) and extend these lines of research to the framework of linear latent growth models. In a sample of N = 4970 second-grade students who worked on the quop-L2 test battery (to assess reading comprehension) at eight measurement points, we compared three Bayesian linear latent growth models: (a) a Gaussian model, (b) a model based on Student's t-distribution (i.e., a robust model), and (c) an asymmetric Laplace model (i.e., Bayesian quantile regression and an alternative robust model). Based on leave-one-out cross-validation and posterior predictive model checking, we found that both robust models outperformed the Gaussian model, and both robust models performed comparably well. While the Student's t model performed statistically slightly better (yet not substantially so), the asymmetric Laplace model yielded somewhat more realistic posterior predictive samples and a higher degree of measurement precision (i.e., for those estimates that were either associated with the lowest or highest degree of measurement precision). The findings are discussed for the context of learning progress assessment.
Abstractor: As Provided
Notes: https://osf.io/hjx43
Entry Date: 2022
Accession Number: EJ1354115
Database: ERIC
FullText Text:
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  Data: Shaky Student Growth? A Comparison of Robust Bayesian Learning Progress Estimation Methods
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  Data: <searchLink fieldCode="AR" term="%22Forthmann%2C+Boris%22">Forthmann, Boris</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9755-7304">0000-0001-9755-7304</externalLink>)<br /><searchLink fieldCode="AR" term="%22Förster%2C+Natalie%22">Förster, Natalie</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-0634-5993">0000-0003-0634-5993</externalLink>)<br /><searchLink fieldCode="AR" term="%22Souvignier%2C+Elmar%22">Souvignier, Elmar</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7962-7428">0000-0001-7962-7428</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Intelligence%22"><i>Journal of Intelligence</i></searchLink>. 2022 10.
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  Data: MDPI AG. Klybeckstrasse 64, 4057 Basel, Switzerland. e-mail: indexing@mdpi.com; e-mail: jintelligence@mdpi.com; Web site: https://www.mdpi.com/journal/jintelligence
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  Data: 16
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  Data: Monitoring the progress of student learning is an important part of teachers' data-based decision making. One such tool that can equip teachers with information about students' learning progress throughout the school year and thus facilitate monitoring and instructional decision making is learning progress assessments. In practical contexts and research, estimating learning progress has relied on approaches that seek to estimate progress either for each student separately or within overarching model frameworks, such as latent growth modeling. Two recently emerging lines of research for separately estimating student growth have examined robust estimation (to account for outliers) and Bayesian approaches (as opposed to commonly used frequentist methods). The aim of this work was to combine these approaches (i.e., robust Bayesian estimation) and extend these lines of research to the framework of linear latent growth models. In a sample of N = 4970 second-grade students who worked on the quop-L2 test battery (to assess reading comprehension) at eight measurement points, we compared three Bayesian linear latent growth models: (a) a Gaussian model, (b) a model based on Student's t-distribution (i.e., a robust model), and (c) an asymmetric Laplace model (i.e., Bayesian quantile regression and an alternative robust model). Based on leave-one-out cross-validation and posterior predictive model checking, we found that both robust models outperformed the Gaussian model, and both robust models performed comparably well. While the Student's t model performed statistically slightly better (yet not substantially so), the asymmetric Laplace model yielded somewhat more realistic posterior predictive samples and a higher degree of measurement precision (i.e., for those estimates that were either associated with the lowest or highest degree of measurement precision). The findings are discussed for the context of learning progress assessment.
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  Data: As Provided
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  Data: https://osf.io/hjx43
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  Data: 2022
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      Pagination:
        PageCount: 16
    Subjects:
      – SubjectFull: Learning Processes
        Type: general
      – SubjectFull: Progress Monitoring
        Type: general
      – SubjectFull: Robustness (Statistics)
        Type: general
      – SubjectFull: Bayesian Statistics
        Type: general
      – SubjectFull: Learning Analytics
        Type: general
      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: Growth Models
        Type: general
      – SubjectFull: Elementary School Students
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      – SubjectFull: Grade 2
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      – SubjectFull: Reading Tests
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      – SubjectFull: Reading Comprehension
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      – SubjectFull: Prediction
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      – SubjectFull: Accuracy
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      – SubjectFull: Student Evaluation
        Type: general
    Titles:
      – TitleFull: Shaky Student Growth? A Comparison of Robust Bayesian Learning Progress Estimation Methods
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