Background: The overarching goal of this study was to explore the underlying mechanism of solving statics or structural analysis problems by utilizing cognitive load theory to examine task characteristics (i.e., the characteristics of instructional materials) with a focus on working memory load (WML) and automaticity. Purpose/Hypothesis: According to cognitive load theory, humans have limited working memory but relatively unlimited long-term memory. Thus, we hypothesized that testing conditions with a high level of automaticity would yield better execution outcomes than those with a low level of automaticity (Hypothesis 1). We hypothesized that testing conditions with low demand on WML would be superior to those with a high demand on WML on the measures of execution outcomes (i.e., accuracy and response time; Hypothesis 2). We expected to observe the interaction between automaticity and WML by alternating the difficulty levels of the task characteristics and involving working memory through the primary task (Hypothesis 3). Design/Method: We utilized a 2 (two levels of WML: four steps versus five steps) × 2 (two levels of automaticity: automatized schemas enforced or not enforced) design. The participants were 31 college engineering students enrolled in a fundamental structural analysis course at a university in the northeastern United States. Results: Findings indicate that automaticity can explain a large percentage of the variance in participants' scores and response times; that is, despite the levels of WML, participants scored higher and performed faster under high automaticity conditions than under low automaticity conditions. WML explained a large between-subject variance in response time, but it accounted for a nonsignificant percentage of the score variance. The interaction effect between automaticity and WML on response time was statistically significant, but the interaction effect on score was not significant. Conclusion: Our findings are aligned with cognitive load theory and the idea that humans have limited WML. In addition, our results underscore the importance of automaticity in problem solving, especially when the task demands high WML. It appears that automaticity helps engineering learners bypass the limits of human working memory. The implications of our findings for engineering educators are also discussed.