Dataset on Professional Teacher Competence in the Digital Era: Evidence from 6,266 Indonesian Teachers
Description
This dataset presents responses from 6,266 teachers across 35 provinces in Indonesia, representing six major island regions: Sumatera, Java, Kalimantan, Sulawesi, Bali–Nusa Tenggara, and Maluku–Papua. The data were collected through an online survey conducted between July and September 2025 to measure teachers’ professional competence in the digital era. The dataset includes demographic information (gender, age, certification status, teaching level) and responses to 38 Likert-scale items assessing professional teaching competence across pedagogical, personal, social, and technological domains. Descriptive statistics (mean, standard deviation, skewness, kurtosis) and reliability analysis (Cronbach’s Alpha = 0.9898) are provided for each item. The data can be used to analyze regional variations, identify competency trends, and support evidence-based policymaking in teacher professional development and digital transformation initiatives.
Files
Steps to reproduce
The dataset was generated through a quantitative online survey conducted between February and June 2025 among teachers across Indonesia. The questionnaire consisted of 38 Likert-scale items measuring professional teacher competence across pedagogical, personal, social, and technological domains. Each item was rated on a five-point scale ranging from 1 (strongly disagree) to 5 (strongly agree). Data were collected using Google Forms, exported to Microsoft Excel, and subsequently analyzed using R Studio (v4.3.2) with the psych and tidyverse packages. Descriptive statistics (mean, standard deviation, skewness, and kurtosis) and internal consistency reliability (Cronbach’s Alpha) were computed to evaluate the quality of the instrument and responses. To reproduce the dataset, researchers can use the provided questionnaire, distribute it through an online survey platform, export the responses in .csv format, and run the supplied R script (data_analysis.R) to replicate the data cleaning, descriptive analysis, and reliability tests. The entire process was performed using open-source tools, ensuring that the workflow is fully transparent and reproducible.
Institutions
- Universitas Negeri Yogyakarta