matosaslopez_2022_bars_teaching

23,239 responses from 2,324 respondents to 10 items.

About this table

DescriptionStudent evaluation of teaching on a 10-item behaviourally-anchored rating scale, 1-5; two deposits pooled with cov_teaching_mode (blended vs face-to-face). 23,239 responses from 2,324 respondents on 10 items.
ReferenceMatosas-Lopez, L. (2022). Datasets on student evaluations of a BARS questionnaire designed for blended-learning and face-to-face teaching [Data sets]. Zenodo. https://doi.org/10.5281/zenodo.15160903 and https://doi.org/10.5281/zenodo.15151307
LicenceCC BY 4.0
Source datahttps://zenodo.org/records/15160903

Size and shape

Responses23,239
Respondents2,324
Items10
Response categories5
Responses per respondent10.000
Responses per item2323.900
Density1.000
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleEducational, Program-based
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)spa

Columns

cov_agecov_degreecov_gendercov_teaching_modecov_universityiditemresp

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python:

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("matosaslopez_2022_bars_teaching")
# Python
pip install irw

import irw
df = irw.fetch("matosaslopez_2022_bars_teaching")

Version and provenance

IRW versionv393
Redivis datasetitem_response_warehouse_4 v7.0
Redivis dataset DOI10.57761/cpj5-vm97
Manifest pin for this IRW versionv7.0
Metadata sourceirw_meta v23.0