63,640 responses from 958 respondents to 38 items.
| 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 |
| 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 |
| 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 |
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 |
block_idcluster_ididitemresptreatwave
Or load it directly in R or Python:
# 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")
| IRW version | v393 |
|---|---|
| Redivis dataset | item_response_warehouse v53.0 |
| Redivis dataset DOI | 10.57761/4g08-xt41 |
| Manifest pin for this IRW version | v53.0 |
| Metadata source | irw_meta v23.0 |