<![CDATA[This study aims to examine the effectiveness of the INSPIRE learning model in improving students’ mastery of environmental change material. The study is motivated by the persistent issue of low student learning outcomes and limited active engagement resulting from the dominance of teacher-centered instructional approaches. The INSPIRE model integrates inquiry-based learning and deep learning principles to promote conceptual understanding and meaningful learning processes. This research employed a quasi-experimental design with a pretest–posttest control group. The participants were two groups of 10th-grade students at SMAN 1 Piyungan, Yogyakarta, for a total of 36 students. The experimental group was taught using the INSPIRE model, while the control group received conventional instruction. Data were collected using a material mastery test and analyzed with an independent-samples t-test at a significance level of 0.05. The results showed a statistically significant difference between the two groups, with the experimental group achieving a higher mean posttest score (93.17) compared to the control group (62.85) (p < 0.001). These findings indicate that the INSPIRE learning model is associated with higher student mastery of environmental change material compared to conventional methods. Theoretically, the INSPIRE model is grounded in constructivist, social cognitive, and humanistic learning theories, which emphasize active learning, collaborative inquiry, and student engagement. The findings suggest that integrating inquiry-based and deep learning approaches can support improved conceptual understanding and overall learning outcomes. However, the results should be interpreted with caution, given the quasi-experimental design and the limited number of cognitive test items. Therefore, the INSPIRE learning model may serve as an alternative instructional strategy for enhancing biology learning and supporting the development of 21st-century competencies. Keywords: INSPIRE learning model, inquiry-based learning, deep learning, environmental change.]]>
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