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All Journal Jurnal Biolokus: Jurnal Penelitian Pendidikan Biologi dan Biologi PEDAGOGIC: Indonesian Journal of Science Education and Technology
Triana Atika Zulfa
Universitas Islam Negeri Raden Mas Said Surakarta
Agriculture, Biological Sciences & Forestry Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Efektivitas Problem Based Learning Berbasis Deep Learning terhadap Kemampuan Berpikir Kritis pada Pembelajaran IPA SD Isnaini Yuliana Ekasari; Triana Atika Zulfa
PEDAGOGIC: Indonesian Journal of Science Education and Technology Vol. 6 No. 3 (2026): PEDAGOGIC: Indonesian Journal of Science Education and Technology
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/ijset.v6i3.5455

Abstract

Critical thinking ability is an essential skill for elementary school students in solving complex phenomena in science learning; however, in reality, students’ abilities in analysis and evaluation are still relatively low. This study aims to determine the effectiveness of the Problem Based Learning model based on a deep learning approach on students’ critical thinking ability in elementary school science learning. This study employed a quantitative approach with a quasi-experimental type and a posttest-only control group design. The population consisted of all fourth-grade students of SDIT Al-Anis Kartasura totaling 75 students, with class IV A as the experimental class and class IV B as the control class. Data were collected through essay tests and analyzed using descriptive and inferential statistics. The results showed that the average critical thinking ability of students in the experimental class was 81.00 (high category), while the control class was 74.21 (medium category). The hypothesis test showed a significance value of 0.038 < 0.05, indicating that the Problem Based Learning model based on a deep learning approach is effective in improving students’ critical thinking ability.
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
Jurnal Biolokus : Jurnal Penelitian Pendidikan Biologi dan Biologi Vol 8, No 2 (2025): December
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30821/biolokus.v8i2.4356

Abstract

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.
Co-Authors Amining Rahmasiwi Dita Purwinda Anggerlla Isnaini Yuliana Ekasari Muhammad Ali