SCIENCE LEARNING IN ANSWERING DIGITAL COMPETENCY NEEDS OF PRE-SERVICE MATH AND SCIENCE TEACHERS IN THE STEM CONTEXT

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, Engineer-ing, 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 (0.31 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.