Spatial ability, which is included in one of the multiple intelligences, is an ability that is considered as important as linguistic intelligence in most literature. Spatial ability, which is closely related to geometry, is also connected to navigational ability. Map or plan reading lessons through Orienteering support experiential learning which is considered more effective because learning is centered on students. The quasi-experimental research method used in this study was carried out at Grade 3 SD Negeri 2 Tambak Baya on 55 students consisting of 27 students in the control group and the remaining 28 in the experimental group. The study took place in 3 meetings. The research data were taken based on the Spatial Thinking Skill Test (STST) aspect indicators whose instruments were modified and developed into action tests that were delivered verbally but contained definite answers like answer keys. The results of the study revealed that there was an increase in student learning outcomes through the application of orienteering as seen from the average comparison test of the pre-test of 35.48 with the post-test learning outcomes of 78.81. From the results of the N-gain statistical test, it was found that orienteering was quite effective in improving spatial thinking skills which were useful for students' navigation skills with an N-gain score of 70.03%. Different results were also found between male and female students in the application of orienteering in the experimental class based on a comparison of the average post-test learning outcomes. From the results of this study, it can be concluded that the application of orienteering is quite effective in developing students' thinking skills. Orienteering can be recommended as a teaching method in Social Natural Sciences lessons because it is a fun activity because learning is centered on students which is done through experience so that the application of spatial thinking skills can be applied in everyday life. This is also relevant to the current independent curriculum which uses a deep learningĀ approach.
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