Dataset of TPACK in teaching practice: Adversity quotient, attitude computer technology and self-efficacy among Indonesian teachers.

This dataset describes the measurement of adversity quotient (AQ), attitude computer technology (ACT), and self-efficacy with computer technology (SCT) of Indonesian teachers in implementing the technological pedagogical content knowledge (TPACK) concept in their teaching practice. The online survey was distributed to collect data on demographic information (4 items), AQ (11 items), ACT (19 items), SCT (22 items), and TPACK (5 items). It was carried out from August to September 2022. A total of 901 teachers from 28 provinces in Indonesia were recruited using probability sampling technique. Data from the survey were analyzed using the statistical analysis of One Way Anova and Partial Correlation. This dataset can help teacher institutions design effective programs to develop teacher digital competencies in integrating technology. Future researchers can compare this dataset with more rigorous data from developing countries.

Survey data of internet skills, internet attitudes, computer self-efficacy, and digital citizenship among students in Indonesia.

Dataset provides wide measurement data on internet skills, internet attitudes, computer self-efficacy of the students related to the digital citizenship in Indonesia. Due to the pandemic of Covid-19, the survey was conducted online by considering the informant consent on assessing demographic information (7 items), internet skills (9 items), internet attitude (5 items), expertise and skills in using a computer (5 items), respect (6 items), educate (5 items), and protect (4 items), which was carried out from March to April. There were a total of 581 respondents selected through probability sampling based on random convenient sample from 12 public and private senior high schools which spread throughout 5 cities in Central Java, Indonesia. The survey data were analyzed using multivariate analysis and partial least structure with the analysis technique of Structural Equation Modelling (SEM). In the future, this data can help educators, researchers, and educational policy makers to determine the level of readiness of students' digital citizenship attributes and efforts to conduct further research on efforts to strengthen digital citizenship in curricular programs.