The Cognitive Effects of Computational Thinking: A Systematic Review and Meta-Analytic Study

This repository contains all the scripts, functions and data to fully reproduce the statistical analysis of the The cognitive effects of computational thinking: A systematic review and meta-analytic study paper by Montuori C., Gambarota F., Altoè G. and Arfè B.

For a detailed description of the analysis method see the supplementary materials (HTML, PDF)

Setup

In order to reproduce the analysis is necessary to clone or download this repository and open the metaCoding.Rproj. Then the renv package will install all required packages with the correct version. After installing all packages, the main_script.R or individual scripts (scripts/*) can be used to run each analysis step.

Folders organization

• data/: contains raw and cleaned data in .rds and .xlsx/.csv format
• docs/: contains scripts to create the supplementary materials
• main_script.R: is a script for easily managing all analysis steps
• objects/: contains R objects in .rds format created from the analysis scripts
• R/: contains all custom functions for the project. Functions are automatically loaded when the project is loaded. For reloading use devtools::load_all()
• scripts/: main scripts for pre-processing, statistical analysis and creating tables/figures
  • 1_analysis.R: Scripts for calculating the effect size and computing the meta-analysis models
  • 2_tables_figures.R: Script to create tables and figures for the paper
  • 3_supplementary.R: Script to create supplementary materials objects
• tables/: contains tables created with the 2_tables_figures.R in .docx format
• tests/: is for testing the effect size computation functions

Coding style

The analysis project is organized as an R package where functions within the R/ folder are automatically available into the global environment when the project is activated (metaCoding.Rproj). Is possible to manually load the functions using devtools::load_all(). The coding style is based on the tidyverse using also metaprogramming. For managing multiple meta-analysis models we used nested tibbles as data structure (see here). In particular the objects/dat_meta.rds contains all processing steps and models as different columns.

Session Info

#>  setting  value
#>  version  R version 4.2.2 (2022-10-31 ucrt)
#>  os       Windows 10 x64 (build 19044)
#>  system   x86_64, mingw32
#>  ui       RTerm
#>  language (EN)
#>  collate  English_United States.utf8
#>  ctype    English_United States.utf8
#>  tz       Europe/Berlin
#>  date     2022-12-13
#>  pandoc   2.19.2 @ C:/Program Files/RStudio/bin/quarto/bin/tools/ (via rmarkdown)

R Packages

• devtools
• testthat
• rmarkdown
• bookdown
• knitr
• flextable
• here
• tidyverse
• broom.mixed
• metafor
• purrr
• rlang
• cowplot
• ggh4x
• grid
• latex2exp
• ftExtra
• cli
• renv
• sessioninfo
• officer
• readxl
• dplyr
• magrittr