5,310 responses from 354 respondents to 15 items.
One of 2 tables from the same source; this one measures ChatGPT utilisation in learning. The others are listed under Related tables.
| Description | ChatGPT utilisation as part of a digital learning system, 15 items, 1-5; N = 354 grade-8 students, SMP Negeri 5 South Tangerang, Indonesia; cov_age, cov_gender, cov_class. |
|---|---|
| Reference | Naulia, M., & Tjhin, V. U. (2026). The Utilization of ChatGPT as a Component of a Digital Learning System and Its Impact on Students' Learning Motivation: A Case Study of SMP Negeri 5 South Tangerang. IEEE IAICT 2026. https://doi.org/10.1109/IAICT71158.2026.11620932 |
| DOI | 10.1109/IAICT71158.2026.11620932 |
| Licence | CC BY 4.0 |
| Source data | https://zenodo.org/records/21901190 |
| Responses | 5,310 |
|---|---|
| Respondents | 354 |
| Items | 15 |
| Response categories | 5 |
| Responses per respondent | 15 |
| Responses per item | 354 |
| Density | 1 |
| Longitudinal | FALSE |
| age range | Child (<18y) |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | ind |
| construct name | ChatGPT utilisation in learning |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, naulia_2026_chatgpt_motivation.py. 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 | ||
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). | made in the build script |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
| Respondent covariates | ||
cov_age | Covariate | made in the build script |
cov_class | Covariate | made in the build script |
cov_gender | Covariate | made in the build script |
What item codes and covariate values stand for is in the codebook released with the source data.
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("naulia_2026_chatgpt_use")
# Python
pip install irw
import irw
df = irw.fetch("naulia_2026_chatgpt_use")
@inproceedings{Naulia_2026, title={The Utilization of ChatGPT as a Component of a Digital Learning System and Its Impact on Students’ Learning Motivation}, url={http://dx.doi.org/10.1109/IAICT71158.2026.11620932}, DOI={10.1109/iaict71158.2026.11620932}, booktitle={2026 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)}, publisher={IEEE}, author={Naulia, Mega Pertiwi and Tjhin, Viani Utami}, year={2026}, month=July, pages={125–129} }
| IRW version | v584 |
|---|---|
| Redivis dataset | item_response_warehouse_5 v8.5 |
| Redivis dataset DOI | 10.57761/41xy-5k56 |
| Manifest pin for this IRW version | v8.5 |
| Metadata source | irw_meta v44.0 |