This article represents the findings from the qualitative portion of a mixed methods study that investigated the impact of middle school students' spatial skills on their plate tectonics learning while using a computer visualization. Higher spatial skills have been linked to higher STEM achievement, while use of computer visualizations has mixed results for helping various students with different spatial levels. This study endeavors to better understand the difference between what high and low spatial-skilled middle school students notice and interpret while using a plate tectonic computer visualization. Also, we examine the differences in the quantity and quality of students' spatial language. The collected data include student spatial scores, student interviews, screencasts, and online artifacts. The artifacts were students' answers to questions inserted in an online curriculum (GEODE) with the embedded computer visualization (Seismic Explorer). Students were asked what they "noticed" during interviews and in the curriculum. Typed student answers and interviews were analyzed for types and quantity of spatial words. Analysis of typed answers and interviews indicated that there are differences in the number and types of spatial words used by high or low spatial students. Additionally, high spatial learners talk about depth, notice patterns in data and are more likely to make a hypothesis to explain what they see on the screen. Findings suggest that students go through an iterative cycle of noticing and interpreting when using a scientific model. Overall, results show a significant positive relationship between spatial skills and what students notice while learning plate tectonics. An explanation for the increased gain in plate tectonics comprehension is that students with higher spatial skills notice more, so they are able to interpret more details of the model. This finding implies that students with low spatial skills do not benefit as much from use of a computer visualization and will need more scaffolding in order to interpret details in the computer visualization.