Dataset: Chilean Teachers' Knowledge and Experience with Artificial Intelligence as a Pedagogical Tool
Description
This dataset contains the statistical analysis results of the study "Chilean Teachers' Knowledge and Experience with Artificial Intelligence as a Pedagogical Tool". The analyses were performed using R and IBM SPSS Statistics 29, covering descriptive statistics, non-parametric tests (Mann–Whitney U, Kruskal–Wallis H, Friedman with post-hoc Wilcoxon), Spearman correlations, multiple linear regression models, and k-means cluster analysis. The dataset includes processed data tables, summary outputs, and figures corresponding to the study’s main findings, as well as supplementary appendices with detailed item statistics, reliability measures, and demographic breakdowns.
Files
Steps to reproduce
Survey responses were exported from Google Forms to Excel and then imported into IBM SPSS Statistics 29 and R for processing. In SPSS, data were cleaned and screened, followed by descriptive statistics and internal consistency checks (Cronbach’s alpha). Normality was assessed using Kolmogorov–Smirnov and Shapiro–Wilk tests. Non-parametric analyses included Mann–Whitney U and Kruskal–Wallis H tests for group comparisons, and the Friedman test with Wilcoxon post-hoc for within-subject comparisons. Spearman correlations examined associations between age, teaching experience, and model dimensions. Multiple linear regression models were developed to identify significant predictors, and k-means cluster analysis was performed to define teacher profiles. All outputs, tables, and figures were saved to ensure replicability.
Institutions
- Universidad de ChileSantiago de Chile