The contemporary educational landscape is marked by increasingly complex geographic phenomena, including spatial processes, climate change, and human–environment interactions (UNESCO, 2021). To understand these issues in depth, students must develop geography-related scientific literacy, defined as the ability to grasp geographical processes, apply reasoning, and use analytical tools effectively (OECD, 2019).

Achieving this requires geography education to equip learners with strong competencies in analysing, interpreting, and reasoning with both quantitative and visual geographic data (NGSS, 2013). Visual data—such as maps, graphs, satellite imagery, and spatial models—plays a particularly important role in helping students decode patterns, observe relationships, and construct evidence-based explanations (Mayer, 2014; Kerski, 2015). Therefore, strengthening scientific literacy has become a central objective of modern geography education (OECD, 2019).

Reflecting this trend, fostering scientific literacy constitutes a central educational objective of the Grade 9 Geography curriculum at Nazarbayev Intellectual Schools.

In pursuit of this objective, NIS 9th-grade students are expected to interpret data, understand environmental processes, and make evidence-based decisions. To facilitate these skills, visual data such as maps, graphs, satellite images, infographics, and GIS outputs-play a central role in helping learners convert abstract geographical concepts into concrete understanding (Kerski, 2015; van der Schee & Kolvoord, 2010).

However, classroom observations and students’ assignments indicate that many students in 9^thgrade geography classes at Nazarbayev Intellectual School struggle to analyze these visuals critically. This research investigates how the use of visual data during geography lessons influences the development of students’ scientific literacy, including their ability to interpret information, reason scientifically, and solve real-world problems-following the action research framework outlined by Creswell and Plano Clark (2018).

This study employed a mixed-methods approach to collect both quantitative and qualitative data. Quantitative data were acquired from pre- and post-test assessments of scientific literacy, whereas qualitative data were derived from classroom observations, student work analysis, and teacher reflective practices. A total of 32 ninth-grade students participated in this study and were divided into two groups. Students in the experimental group (n = 16) were taught using visual-data-enriched instruction, whereas students in the control group (n = 16) received conventional text-based instruction. The intervention was implemented over 8 weeks and focused on three key topics, during which students in the experimental group interpreted climate change data using line graphs, anomaly maps, and satellite imagery. Using GIS maps and demographic infographics, they analyzed urbanization patterns and studied hazards through case studies that incorporated hazard-risk maps and time-series images. The instruction incorporated scaffolding strategies, including visual decoding protocols, guided questioning, and the Claim Evidence Reasoning (CER) framework, to assist students in developing scientific explanations and reasoning as recommended by McNeill and Krajcik (2012). The quantitative data were extracted using a Scientific Literacy Assessment Rubric adapted from the OECD PISA science framework (OECD, 2019). Students’ performance was evaluated using an assessment rubric across three tasks: map interpretation, graph analysis, and remote-sensing-based inference.Quantitative data were analysed using paired t-tests to assess whether statistically significant differences existed between pre- and post-test scores.Utilising Braun and Clarke’s (2006) thematic coding approach, the qualitative data were analyzed to identify key themes and gain insights into students’ learning processes and engagement within visual-data-enriched lessons.

Discussion This outcome aligns with prior research indicating that visual representations facilitate spatial reasoning (Bednarz, 2011) and support evidence-based analytical reasoning (Kastens, 2012). The integration of visual data scaffolds students’ cognitive shift from descriptive observations to more sophisticated analytical thinking, while GIS tools and remote-sensing imagery provide scientific contexts that reflect real-world geographical inquiry. Conclusion The findings provide compelling evidence that integrating visual data into geography education substantially improves key dimensions of students’ scientific literacy, especially in interpreting data, constructing evidence-based reasoning, and articulating scientific explanations. The study recommends the systematic incorporation of visual data analysis across geography curricula, providing focused teacher training in GIS and data visualization, and designing assessment tasks that integrate maps, satellite imagery, and graphical representations. Curriculum designers should be encouraged to embed visual-data skills within educational standards, and schools should provide access to GIS tools and digital imagery to foster inquiry-based learning. In parallel, teacher professional development should focus on enhancing spatial and graphical literacy for effective classroom implementation. This study’s results are limited since it was conducted in a single school, indicating the need for future research with larger and more diverse samples.

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