About this table

Description	Math outcomes in an RCT
Reference	Bang, H. J., Li, L., & Flynn, K. (2023). Efficacy of an adaptive game-based math learning app to support personalized learning and improve early elementary school students’ learning. Early Childhood Education Journal, 51(4), 717-732.
DOI	10.1007/s10643-022-01332-3
Licence	CC BY 4.0
Source data	https://data.mendeley.com/datasets/bwkm69ycrc/1

Size and shape

Responses	63,640
Respondents	958
Items	38
Response categories	2
Responses per respondent	66.430
Responses per item	1674.737
Density	1.748
Longitudinal	TRUE

Classification

age range	Child (<18y)
child age (for child-focused studies)	Child (6-12y), Early (<6y)
sample	Educational, Program-based
construct type	Cognitive/educational
measurement tool	Test
item format	Likert Scale/selected response
primary language(s)	eng
construct name	Student Math Assessment

Item text

This table has item text in the IRW: the wording administered to respondents, not just the response codes.

Instrument	Early elementary mathematics assessment assembled from the Certica item bank (Bang, Li & Flynn 2023)
Mean words per item	13.239
Mean characters per item	63.265
Mean characters per response	2.256
Flesch-Kincaid grade level	1.943

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, il_hte_23.R. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.

Column	What it is	Source column
IRW standard columns
`block_id`	IRW standard The level above cluster_id — in a randomised design, the block or stratum within which assignment was made (treat varies within a block, and clusters nest in blocks).	`block`
`cluster_id`	IRW standard The group a focal unit is nested in — classroom, teacher, school.	`teacher_id`
`id`	IRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task).	not traced
`item`	IRW standard Item identifier.	made in the build script
`resp`	IRW standard Response value.	not traced
`treat`	IRW standard Treatment group assignment in experimental studies.	`condition`
`wave`	IRW standard Longitudinal wave indicator.	made in the build script

What item codes and covariate values stand for is in the codebook released with the source data.

Get the data

Download CSVno account needed Browse on Redivisexplore and query Croissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("gilbert_meta_23")

# Python
pip install irw

import irw
df = irw.fetch("gilbert_meta_23")

How to cite

@article{Bang_2022, title={Efficacy of an Adaptive Game-Based Math Learning App to Support Personalized Learning and Improve Early Elementary School Students’ Learning}, volume={51}, ISSN={1573-1707}, url={http://dx.doi.org/10.1007/s10643-022-01332-3}, DOI={10.1007/s10643-022-01332-3}, number={4}, journal={Early Childhood Education Journal}, publisher={Springer Science and Business Media LLC}, author={Bang, Hee Jin and Li, Linlin and Flynn, Kylie}, year={2022}, month=apr, pages={717–732} }

Version and provenance

IRW version	v549
Redivis dataset	item_response_warehouse v70.0
Redivis dataset DOI	10.57761/4qxs-pq87
Manifest pin for this IRW version	v70.0
Metadata source	irw_meta v39.0