Fostering 21st-Century Problem-Solving: The Efficacy of the Deep Learning-Integrated INSPIRE Model in Science Education
), Paidi Paidi(2), Helen Grace B. Gonzales(3), Mualimim Mualimim(4), Atik Kurniawati(5), Ahmad Kamal Sudrajad(6), Nurul Faizah(7), Kurnia Padmawati(8), Crescentia Mishi Juniorita Ngangi(9)
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Country:
(1) Master Program of Biology Education, Yogyakarta State University, Indonesia
(2) Department of Biology Education, Yogyakarta State University, Indonesia
(3) University of Science & Technology of Southern Philippines, Philippines
(4) Department of Biology Education, Yogyakarta State University, Indonesia
(5) Department of Biology Education, Yogyakarta State University, Indonesia
(6) Department of Biology Education, Yogyakarta State University, Indonesia
(7) Master Program of Biology Education, Yogyakarta State University, Indonesia
(8) Master Program of Biology Education, Yogyakarta State University, Indonesia
(9) Master Program of Biology Education, Yogyakarta State University, Indonesia
Corresponding Author
Twenty-first-century learning, particularly in biology education, emphasizes the development of critical thinking and problem-solving skills as essential 4C competencies. However, current evidence indicates that Indonesian students' problem-solving abilities remain low, as reflected in the 2022 PISA results, which show that only 31% of students achieved basic creative thinking skills. This study aims to address this gap by examining the effectiveness of the INSPIRE (Identifying, Nurturing, Setting up, Planning, Investigating, Reasoning, Ending) learning model integrated with a deep learning approach defined here as a pedagogical framework emphasizing meaningful, mindful, and joyful learning on students' problem-solving skills in biology. This quasi-experimental study was conducted at SMA Negeri 1 Piyungan, Yogyakarta, during the odd semester of the 2024/2025 academic year and involved 72 tenth-grade students studying the environmental change material. Participants were purposively selected from two intact classes (n=36 each) based on equivalent characteristics, balanced prior academic performance, and schedule availability. The study employed a pretest-posttest non-equivalent control group design, with the experimental group receiving instruction through the INSPIRE model enriched with e-modules and guided inquiry activities. In contrast, the control group experienced conventional lecture-based teaching. Problem-solving abilities were measured using a validated six-item essay test covering four Polya indicators: understanding the problem, devising a plan, carrying out the plan, and looking back (r=0.368-0.579; α=0.923). Data analysis using an independent-samples t-test revealed statistically significant differences between groups (t = -27.195, df = 70, p < 0.001), with the experimental class achieving a mean posttest score of 92.01 (SD = 3.50) compared to the control class mean of 62.15 (SD = 5.57). Cohen's d effect size calculation yielded 0.98, indicating a large practical significance. The experimental group demonstrated a gain score of 58.24 points, compared with 28.82 in the control group. These findings provide empirical evidence that the deep learning-based INSPIRE model effectively enhances students' problem-solving skills in biology, offering a viable pedagogical alternative to conventional instruction methods.
Keywords: biology learning, deep learning, environmental change, INSPIRE model, problem solving.
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