Spatial thinking is one of the essential cognitive competencies in geography education because it enables students to understand spatial concepts, use representational tools, and reason about the spatial relationships among phenomena on the Earth’s surface. However, various studies have shown that the spatial thinking skills of Indonesian high school students remain suboptimal, particularly in the aspects of visualization, pattern analysis, and spatial interconnection. A similar condition was also found at SMA Negeri 1 Gaung Anak Serka, Indragiri Hilir Regency, Riau Province, where students still experienced difficulties in reading maps, relating the location of a region to its physical and social conditions, and explaining geospatial phenomena spatially. This study aims to describe the implementation of the Geo-STAR Model and analyze its effect on improving the spatial thinking skills of Grade X Phase E students on the Atmosphere topic. The research employed a quantitative approach with a quasi-experimental pretest-posttest control group design. The research sample consisted of class X.3 as the experimental group and class X.1 as the control group, selected through purposive sampling. The Geo-STAR Model was implemented through six learning syntaxes that integrated Google Earth, National Geographic MapMaker, Assemblr Edu (Augmented Reality), Mentimeter, and Seesaw. The results showed that the model was implemented according to the syntax and was able to create interactive, contextual, and spatial-exploration-based learning. There was a significant difference between the pretest and posttest scores in the experimental class with a significance value of 0.000. The average N-Gain of the experimental class reached 0.86 and was categorized as high, while the control class obtained an average N-Gain of 0.68, which fell into the medium category. Thus, the Geo-STAR Model has been proven to have a positive and significant effect on improving students’ spatial thinking skills in geography learning on the Atmosphere topic.
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