Wang2025

The data set contains Hedges' g effect sizes, standard errors,
sample sizes for experimental and control groups, and various study characteristics
including grade level, type of course, duration, learning model, role of ChatGPT,
and area of ChatGPT application. The meta-analysis examined the effect of ChatGPT
on students' learning performance, learning perception, and higher-order thinking
wang2025effectRoBMA.

datasets

A framework for Bayesian meta-analysis, including model estimation,
prior specification, model comparison, prediction, summaries, visualizations,
and diagnostics. The package fits single and model-averaged meta-analytic,
meta-regression, multilevel, publication bias adjusted, and generalized linear
mixed models The model-averaged meta-analytic models combine competing models
based on their predictive performance, weight inference by posterior model probabilities,
and test model components using Bayes factors (e.g., effect vs. no effect;
Bartoš et al., 2022, <doi:10.1002/jrsm.1594>; Maier, Bartoš & Wagenmakers, 2022,
<doi:10.1037/met0000405>; Bartoš et al., 2025, <doi:10.1037/met0000737>).
Users can specify flexible prior distributions for effect sizes, heterogeneity,
publication bias (including selection models and PET-PEESE), and moderators.

Franti<c5><a1>ek Barto<c5><a1>

RoBMA

Robust Bayesian Meta-Analyses

František Bartoš

Maximilian Maier

Eric-Jan Wagenmakers

Joris Goosen

Matthew Denwood

Martyn Plummer

Wang2025 function

A data.frame with 12 columns and 70 observations:<dl>
<dt><code>Learning_effect</code></dt>
<dd>Learning-effect outcome category.</dd><dt><code>Author_year</code></dt>
<dd>Author-year study label.</dd><dt><code>N_EG</code></dt>
<dd>Experimental-group sample size.</dd><dt><code>N_CG</code></dt>
<dd>Control-group sample size.</dd><dt><code>g</code></dt>
<dd>Hedges' g effect size.</dd><dt><code>Grade_level</code></dt>
<dd>Grade level.</dd><dt><code>Type_of_course</code></dt>
<dd>Course type.</dd><dt><code>Duration</code></dt>
<dd>Study duration.</dd><dt><code>Learning_model</code></dt>
<dd>Learning model.</dd><dt><code>Role_of_ChatGPT</code></dt>
<dd>Role of ChatGPT in the intervention.</dd><dt><code>Area_of_ChatGPT_application</code></dt>
<dd>Application area.</dd><dt><code>se</code></dt>
<dd>Standard error of g.</dd>
</dl>

Format

70 effect sizes from a meta-analysis of ChatGPT's impact on student learning
by wang2025effect;textualRoBMA — Wang2025

A data.frame with 12 columns and 70 observations:<dl>
<dt><code>Learning_effect</code></dt>
<dd>Learning-effect outcome category.</dd>

<dt><code>Author_year</code></dt>
<dd>Author-year study label.</dd>

<dt><code>N_EG</code></dt>
<dd>Experimental-group sample size.</dd>

<dt><code>N_CG</code></dt>
<dd>Control-group sample size.</dd>

<dt><code>g</code></dt>
<dd>Hedges' g effect size.</dd>

<dt><code>Grade_level</code></dt>
<dd>Grade level.</dd>

<dt><code>Type_of_course</code></dt>
<dd>Course type.</dd>

<dt><code>Duration</code></dt>
<dd>Study duration.</dd>

<dt><code>Learning_model</code></dt>
<dd>Learning model.</dd>

<dt><code>Role_of_ChatGPT</code></dt>
<dd>Role of ChatGPT in the intervention.</dd>

<dt><code>Area_of_ChatGPT_application</code></dt>
<dd>Application area.</dd>

<dt><code>se</code></dt>
<dd>Standard error of g.</dd>


</dl>

Wang2025: 70 effect sizes from a meta-analysis of ChatGPT's impact on student learning by wang2025effect;textualRoBMA

Description

Usage

Arguments

Format

References