Science Learning in Answering Digital Competency Needs of Preservice Math and Science Teachers in the STEM Context
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| Titel: | Science Learning in Answering Digital Competency Needs of Preservice Math and Science Teachers in the STEM Context |
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| Autoren: | Muhammad Luthfi Hidayat, Harun Joko Prayitno, Sofyan Anif, Maram Meccawy, Tariq Jamil Saifullah Khanzada |
| Quelle: | Jurnal Pendidikan IPA Indonesia. 12:410-422 |
| Verlagsinformationen: | Universitas Negeri Semarang, 2023. |
| Publikationsjahr: | 2023 |
| Schlagwörter: | FOS: Computer and information sciences, Economics, Generalization, Social Sciences, Digital Competence in Education and Workforce Development, Mathematical analysis, Education, Context (archaeology), Distance Learning, Service (business), Concept map, FOS: Mathematics, Psychology, 10. No inequality, Science education, Digital learning, Biology, Digital Skills, 9. Industry and infrastructure, Pedagogy, 4. Education, Statistics, Paleontology, Wright, Economy, Digital Transformation in Higher Education, Computer science, Mathematics education, Online Education, Programming language, FOS: Psychology, Online Learning, Computer Science, Physical Sciences, E-Learning in Higher Education, Rasch model, Mathematics, Information Systems |
| Beschreibung: | These study purposes are to map the digital competency of pre-service math and science teachers and investigate how science learning can answer the challenge of digital competencies (DigComp) needs. This research used Rasch Model analysis to make knowledgeability mapping of the subject and analyzed using Wright map output by Winsteps 5.3.4. Data was gathered from a survey of 328 pre-service teachers of science majors, e.g., Biology, Chemistry, Geography, or Math, using the DigComp Framework-Based Questionnaire (DFBQ). The responses were based on respondents’ diverse demographic profiles (gender, region, living area, and field of study). The findings identify several differences in teacher training students’ knowledgeability of digital competencies that the Wright map in the Rasch model can map. Knowledgeability mapping is essential to determine which part of DigComp still needs to be strengthened with science education in the context of Science, Technology, Engineering, and Mathematics (STEM) implementation. The study yields two main conclusions: 1) The mapping study of pre-service math and science teachers’ knowledgeability in the DigComp framework shows Logit Value Person (0.31LVP1.11) and Logit Value Item (LVI 0.66 logits); (0 ≥ LVI ≥ 0.66) that are reflecting a middle to lower level of competency, and 2) Science learning has high potential to address this challenge through its learning strategy implementations. The findings can be recommendations for future research of knowledgeability mapping and policy development and discuss implications for digital competency framework practices. |
| Publikationsart: | Article Other literature type |
| ISSN: | 2089-4392 2339-1286 |
| DOI: | 10.15294/jpii.v12i3.43960 |
| DOI: | 10.60692/ncsqq-7rb20 |
| DOI: | 10.60692/y1nab-8xa38 |
| Dokumentencode: | edsair.doi.dedup.....4a9b72f1bce7d8c8706e1615105a8f2b |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | These study purposes are to map the digital competency of pre-service math and science teachers and investigate how science learning can answer the challenge of digital competencies (DigComp) needs. This research used Rasch Model analysis to make knowledgeability mapping of the subject and analyzed using Wright map output by Winsteps 5.3.4. Data was gathered from a survey of 328 pre-service teachers of science majors, e.g., Biology, Chemistry, Geography, or Math, using the DigComp Framework-Based Questionnaire (DFBQ). The responses were based on respondents’ diverse demographic profiles (gender, region, living area, and field of study). The findings identify several differences in teacher training students’ knowledgeability of digital competencies that the Wright map in the Rasch model can map. Knowledgeability mapping is essential to determine which part of DigComp still needs to be strengthened with science education in the context of Science, Technology, Engineering, and Mathematics (STEM) implementation. The study yields two main conclusions: 1) The mapping study of pre-service math and science teachers’ knowledgeability in the DigComp framework shows Logit Value Person (0.31LVP1.11) and Logit Value Item (LVI 0.66 logits); (0 ≥ LVI ≥ 0.66) that are reflecting a middle to lower level of competency, and 2) Science learning has high potential to address this challenge through its learning strategy implementations. The findings can be recommendations for future research of knowledgeability mapping and policy development and discuss implications for digital competency framework practices. |
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| ISSN: | 20894392 23391286 |
| DOI: | 10.15294/jpii.v12i3.43960 |