Digital Competencies Mapping Dataset of Pre-service Teachers in Indonesia

Description

This dataset describes Indonesian pre-service teachers' perception of five Digital competency areas: 1) data and information literacy, 2) communication and collaboration, 3) digital content creation, 4) safety, and 5) troubleshooting. The dataset was generated from 23 education and teacher training faculties across departments at Muhammadiyah Universities in 14 provinces of Indonesia in June 2021. An online questionnaire using the Digital competency Framework-based Questionnaire (DFBQ) was distributed to collect data on demographic information (6 items), area 1 (5 items), area 2 (7 items), area 3 (6 items), area 4 (9 items) and area 5 (9 items). A total of 1400 students in their first to fifth years were recruited using a random sampling technique. The dataset from GoogleForms of a Likert scale questionnaire was then converted to Excel. Rasch model analysis was used to map the respondents’ digital competencies skills. The dataset was cleaned using Winsteps 5.2.3 software before analyzing it using the Rasch Model analysis. The uploaded datasets have the.xlsx extension. This dataset can help teacher-training institutions design effective programs to improve pre-service teachers' digital competencies. Future researchers can compare this dataset with more rigorous data from other countries.

Steps to reproduce

Data was gathered from the 1st to 5th year pre-service teachers across departments at Muhammadiyah's Universities in Indonesia. The survey was conducted on June 17th- 22nd, 2021, by broadcasting the digital Likert scale questionnaires (Google form) to the Deans/heads of Education faculties at Muhammadiyah Universities through the association of Muhammadiyah's higher-education network. After collection, the raw data is converted into a .xlsx extension format, then .prn format, before being analysed using Winsteps 5.23 software. The analysis used in this study using Winsteps 5.2.3 software includes statistical analysis, person-item measurement, unidimensionality, instrument's validity and reliability, and item and person's Wright mapping.

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