A Digital Learning Environment (DLE) with STEM and Game-Based Learning (GBL) approaches for fostering scientific literacy among future biology teachers - Amining Rahmasiwi; Dita Purwinda Anggerlla; Triana Atika Zulfa; Muhammad Ali (Universitas Islam Negeri Sumatera Utara, 2025)
Scientific literacy is a crucial competency for biology graduates to address 21st-century scientific and educational challenges, as it enables the understanding, evaluation, and application of scientific information in academic and real-world contexts. However, prior studies indicate that prospective biology teachers often exhibit low scientific literacy, particularly in conceptual understanding, problem-solving, and interpretation of experimental data. To address this issue, this study aimed to develop and examine the effectiveness of a Digital Learning Environment (DLE) integrating STEM and Game-Based Learning (GBL) approaches to enhance scientific literacy among prospective biology teachers. The study was conducted at Raden Mas Said State Islamic University Surakarta during the even semester of the 2023–2024 academic year, employing the ADDIE instructional design model. A pre-experimental posttest-only control group design was used during the implementation phase, involving 19 students from the Tadris Biology Program. The developed web-based DLE consisted of mission-based learning modules designed to strengthen general biology concepts, incorporating sequential missions, guiding clues, datasets, conceptual explanations, and Virtual Laboratory tools for online experimentation. Expert validation results indicated that the DLE was feasible, user-friendly, and effective in improving students’ scientific literacy. Nevertheless, the study identified several limitations, including technical challenges, students’ adaptation to digital learning, a short implementation duration, and a limited sample size, which restricts generalizability. Future studies are recommended to involve larger and more diverse samples, extend the implementation period, apply qualitative methods, and integrate adaptive learning analytics and AI-based feedback to support personalized learning and higher order scientific thinking.