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Students' self-regulated learning (SRL) profile dataset measured during Covid-19 mitigation in Yogyakarta, Indonesia
The Covid-19 pandemic has made changes in various sectors of life in Indonesia, including education. The Indonesian Ministry of Education and Culture issued a policy for the implementation of online learning. One of the factors that determine the success of online learning is the level of student se...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7572366/ https://www.ncbi.nlm.nih.gov/pubmed/33102667 http://dx.doi.org/10.1016/j.dib.2020.106422 |
Sumario: | The Covid-19 pandemic has made changes in various sectors of life in Indonesia, including education. The Indonesian Ministry of Education and Culture issued a policy for the implementation of online learning. One of the factors that determine the success of online learning is the level of student self-regulated learning. Thus understanding the capabilities of SRL is essential for achieving successful education during this pandemic. This article presents data that explore the profiles of self-regulated learning in 1st-grade to 12th-grade students. Four aspects of self-regulated learning include planning, monitoring, controlling, and reflecting. Data retrieval is related to predictions of online learning success during Covid-19 mitigation. The sample consisted of 6571 students. The questionnaire was distributed to 61 schools (37 primary schools, 12 junior high schools, and 12 senior high schools) with an online survey in Yogyakarta, Indonesia. The questionnaire was prepared in an online format using Google Form. This link was presented with an introductory sentence from the researcher and distributed to students through the respective principal. Students may only fill in a questionnaire once but were allowed to make changes in response. The collected data were selected to be valid and reliable using the Rasch model. Some data released are items that are not filled in, extremely low or high data. These data can be further processed with various statistical techniques such as Two-way, ANOVA, MANOVA, or Cluster Analysis following the intended in-depth analysis needs. The data will be useful for researchers, educational decision-makers, and education managers to improve online learning services and implementation that enhance student learning achievement. |
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