Dataset on Indonesian Teachers’ Readiness for Curriculum Transformation Toward Deep Learning

Description

This dataset was developed to examine teachers’ readiness for curriculum transformation toward deep learning-oriented practices in Indonesia. The study was guided by the Teacher Readiness Framework, which assumes that teachers’ Readiness for Change is shaped by individual, social, organizational, leadership, and resource-related factors, both directly and indirectly through Teacher Self-Efficacy. The main research hypothesis is that Beliefs and Attitudes, Organizational Characteristics, Peer Support, Principal Support, and Resources and Infrastructures influence teachers’ Readiness for Change, with Teacher Self-Efficacy acting as a central mediating mechanism. The dataset contains anonymized survey responses from 2,271 teachers across various educational levels in Indonesia. Data were collected using a structured questionnaire consisting of reflective indicators measuring seven latent constructs: Beliefs and Attitudes, Organizational Characteristics, Peer Support, Principal Support, Resources and Infrastructures, Teacher Self-Efficacy, and Readiness for Change. Respondents indicated their agreement with each statement using a Likert-type scale. The dataset also includes demographic information and coded item-level responses that can be used for further statistical analysis. The data show that Teacher Self-Efficacy plays a central role in explaining teachers’ Readiness for Change. In the related structural equation model, Teacher Self-Efficacy emerged as the strongest predictor of Readiness for Change and mediated several relationships between individual, social, organizational, and resource-related factors and readiness. Organizational Characteristics and Resources and Infrastructures showed important direct and indirect relationships with readiness, while Peer Support and Principal Support were more strongly connected to readiness through Teacher Self-Efficacy. A notable finding is that Principal Support showed a negative indirect pathway to Readiness for Change through Teacher Self-Efficacy, suggesting that leadership support may need to be interpreted carefully depending on how teachers perceive it. This dataset can be interpreted as a perception-based survey dataset on teacher readiness, self-efficacy, and school support in the context of curriculum change. It can be reused to test alternative measurement and structural models, conduct mediation analysis, compare teacher groups, assess measurement invariance, or examine relationships among teacher beliefs, school organization, leadership, resources, and readiness for educational change. Researchers may also use the data for cross-country comparison, secondary analysis, or methodological demonstrations using partial least squares structural equation modeling, confirmatory factor analysis, or other multivariate approaches.

Steps to reproduce

The dataset was generated through the following procedures: 1. The research framework was developed based on literature on teacher readiness for change, organizational readiness, teacher self-efficacy, peer and principal support, and resource readiness in educational change. 2. A structured questionnaire was designed to measure seven reflective constructs: Beliefs and Attitudes, Organizational Characteristics, Peer Support, Principal Support, Resources and Infrastructures, Teacher Self-Efficacy, and Readiness for Change. The questionnaire items were adapted from established scales and contextualized to curriculum transformation toward deep learning-oriented practices in Indonesia. 3. The questionnaire was distributed to teachers in Indonesia using an online survey format. Respondents were informed about the purpose of the study, voluntary participation, anonymity, and confidentiality before completing the questionnaire. 4. A total of 2,271 valid teacher responses were retained for the final dataset. Responses were screened for completeness and suitability for analysis. Personal identifiers were removed before the dataset was prepared for public sharing. 5. The data were coded at item level. Each row represents one teacher respondent, and each column represents either demographic information or questionnaire indicators. Likert-type responses were coded numerically according to the response scale used in the questionnaire. 6. The cleaned dataset was imported into SmartPLS 4 for Partial Least Squares Structural Equation Modeling. The analysis included assessment of the measurement model, structural model, mediation effects, explained variance, and predictive relevance where applicable. 7. The PLS Algorithm was used to estimate indicator loadings, construct reliability, convergent validity, discriminant validity, path coefficients, and explained variance. PLS Bootstrapping was then used to estimate the significance of direct, indirect, and total effects. 8. Consistent Partial Least Squares was also conducted as a robustness check. The PLSc Algorithm and PLSc Bootstrapping outputs were used to compare the stability of key structural relationships with the main PLS-SEM results. 9. The repository includes the anonymized dataset, cleaned dataset, questionnaire items, codebook, and SmartPLS output files so that other researchers can reproduce the measurement and structural model analyses or test alternative models using the same data.

Institutions

• Muhammadiyah University of Surakarta

  Central Java, Surakarta

Categories

Funders

• Muhammadiyah University of Surakarta

  Surakarta

Licence