As part of the e4usa curriculum, a MATLAB model has been developed and implemented in order to cultivate engineering and computational thinking skills in high school students. The MATLAB model uses a live script that allows students to interact with sliders and dropdown menus to change parameters on a water filtration model. With computational skills increasingly in demand, the literature suggests that adding computational thinking and coding skills as a new form of literacy is crucial for preparing future engineering professionals. Additionally, to ensure that students are better prepared by the time they reach their post-secondary studies, early exposure to computational thinking skills has valuable implications. In this fundamental paper, we describe outcomes resulting from students' interactions with MATLAB in e4usa. The mathematical model allows the students to analyze the effects of different filtration materials, impurities to be removed, length of the water filter, and the space between particles in their filtration material. Using at first a mathematical model rather than testing physical materials will allow them to learn more about their potential filtration materials so that they may make more informed decisions about which filtration materials they want to select for their design and use in the prototype that they build and test. With that said, we focus on student outcomes in this design activity. We hypothesize that this modeling activity prior to design may reduce the time spent in physical testing as well as the volume of materials consumed. Additionally, we are inquisitive about the impact that it has on the subsequent design activities compared to previous semesters where this lesson was taught, where it was observed that students spend a considerable amount of time trying out different materials. As part of our data, we have collected teacher data from surveys, pre and post-responses about their expectations, attitudes, and perceived value of implementing the MATLAB model in their classrooms, class observation data from at least two schools where we noted the interactions between the teachers and students, and teacher and student focus groups at the end of the semester where we expect to collect richer data from these two groups that will allow us to triangulate data collected from surveys and classroom observations.