Modeling within‐year growth on the Mathematics Measure of Academic Progress

Boorse, Jaclin; Norman, Ethan R. Van

doi:10.1002/pits.22590

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…These findings may generalize to other core competencies (e.g., math skills; Salaschek and Souvignier 2014 ; Boorse and Van Norman 2021 ), grade levels (e.g., reading comprehension in fourth grade; Förster and Souvignier 2014 ), and contexts (e.g., special needs educational setting; Jenkins et al 2009 ) as long as the score distribution shows signs of being more peaked or having more extreme values at the tails of the distribution of progress monitoring data. However, we recommend that all three models be carefully examined in terms of model fit because the models have different distributional characteristics that imply, for example, differences in the measurement precision of learning progress monitoring estimates.…”

Section: Discussionmentioning

confidence: 82%

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

Forthmann¹,

Förster²,

Souvignier³

2022

J. Intell.

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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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Section: Discussionmentioning

confidence: 82%

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

Forthmann¹,

Förster²,

Souvignier³

2022

J. Intell.

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“…Sistem ini kemudian menggunakan data tersebut untuk menyesuaikan materi pembelajaran yang disajikan, memastikan bahwa setiap siswa mendapat dukungan sesuai kebutuhannya. Penelitian oleh organisasi seperti NWEA menunjukkan bahwa kurikulum adaptif dapat meningkatkan pemahaman matematika dan kemampuan membaca siswa, terutama bagi mereka yang berjuang dengan materi pelajaran standar (Boorse & Van Norman, 2021;NWEA, 2019) Teknologi pembelajaran mencakup penggunaan alat dan platform digital untuk mendukung proses pembelajaran. Ini termasuk penggunaan tablet di kelas, software pendidikan, dan sistem manajemen pembelajaran online yang menyediakan akses ke sumber daya pendidikan dan kegiatan belajar mandiri.…”

Section: Inovasi Dalam Sektor Pendidikan: Kurikulum Adaptif and Tekno...unclassified

Peran inovasi dalam pembangunan ekonomi dan pendidikan menuju visi Indonesia Maju 2045

Lase,

Waruwu,

Zebua

et al. 2024

Tuhenori J. Ilm. Multidisiplin

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Artikel ini mengeksplorasi peran kritis inovasi dalam pembangunan ekonomi dan pendidikan di Indonesia, dengan fokus pada pencapaian visi "Indonesia Maju 2045". Penelitian ini menggunakan pendekatan analisis komprehensif untuk mengkaji kebijakan saat ini dan menelusuri studi kasus pada beberapa inisiatif sukses yang melibatkan penerapan inovasi dalam sektor-sektor seperti fintech, edutech, dan ekonomi digital. Hasil dari penelitian ini menunjukkan bahwa inovasi teknologi, termasuk digitalisasi dan penggunaan AI, serta reformasi pendidikan yang adaptif, memegang peranan penting dalam mempercepat pertumbuhan ekonomi dan meningkatkan kualitas pendidikan. Artikel ini memberikan wawasan tentang bagaimana berbagai model inovasi yang telah diimplementasikan di negara berkembang dapat diadaptasi untuk mengatasi tantangan lokal dan memacu pertumbuhan sosial dan ekonomi di Indonesia. Selain itu, penelitian ini juga menekankan pentingnya kolaborasi antara pemerintah, industri, dan institusi pendidikan dalam menciptakan ekosistem inovasi yang berkelanjutan dan inklusif.

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Modeling Growth in Reading With Curriculum-Based Measurement Versus Computer-Adaptive Tests

Van Norman,

Forcht

2024

Assessment for Effective Intervention

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Curriculum-based measurement of reading (CBM-R) is a common assessment educators use to monitor student growth in broad reading skills and evaluate the effectiveness of instructional programs. Computer-adaptive tests (CATs), such as Star Reading, have been cited as a viable option to formatively assess reading growth. We used Bayesian multivariate-multilevel modeling to compare growth measured via concurrently collected CBM-R and CAT data for 3,192 students across Grades 1 ( n = 298), 2 ( n = 1,149), 3 ( n = 1,062), 4 ( n = 462), and 5 ( n = 221). After standardizing outcomes, the average rate of weekly improvement on each measure was highly similar across grade levels. Between-student variability for growth on each assessment was highly similar across grade levels. The magnitude of residual variance, or error, differed markedly between assessments. In Grades 1 to 3, Star Reading yielded less precise estimates of growth relative to CBM-R, and the opposite results were observed in Grade 5.

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Modeling within‐year growth on the Mathematics Measure of Academic Progress

Cited by 3 publications

References 24 publications

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

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

Peran inovasi dalam pembangunan ekonomi dan pendidikan menuju visi Indonesia Maju 2045

Modeling Growth in Reading With Curriculum-Based Measurement Versus Computer-Adaptive Tests

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