By investigating post-ECS CS computational thinking ability we can better understand how to support students in CPS by identifying students who got the most out of ECS and those who got the least. We implemented an exploratory Linear Mixed Models (LMM) accounting for student demographics, academics, perception of CS, and different measurements of teacher instructional practice. Our model found that pre-ECS CS computational thinking ability, high performance in ECS, as well as high student expectancy and value in CS, lower costs in CS (EVC; see Barron & Hulleman, 2015), and high teacher expectations can all lead to higher post-ECS CS computational thinking ability. Some groups of students were also found to not get as much out of ECS, including Black students and students who receive services (free/reduced lunch, special ed, ESL), with statistically significantly lower CS computational thinking ability scores than their peers. We discuss the potential impacts on ECS teaching in CPS.