My dissertation explores how elicited gestures can support 6th grade students' understanding of nonlinear dynamics of complex systems. My hypothesis is that elicited gestures make these patterns salient to the student, who learns about these quantitative dynamics via embodied mechanisms. To elicit these gestures, I designed a computer simulation called the Embodied Simulation of Population Dynamics (ESPD). The ESPD elicits bimanual gestures to represent nonlinear changes between two quantities. I explore how the learning from elicited gestures can take place at three different granularity levels. First, I compare the ESPD versus a non-embodied instructional intervention. This comparison tests whether physical movement has an effect on learning. Second, having compared individual learning, I examine the effect elicited gestures have in the way a group of students build situated meaning of graphical representations. Third, I explore a measurement model of students' enacted movements using the ESPD log data. With the log data, I measure the student ability to enact the elicited gestures. Results show (1) statistically significant higher learning gains for students in the ESPD condition; (2) students in the ESPD condition spontaneously used more complex, action-laden gestures to convey their understanding of the quantitative dynamics; and (3) the ability to enact the elicited movement predicts learning gains. Findings from this dissertation will be useful to researchers, teachers, and designers who want to include elicited gestures as part of their instructional approach with early middle school students. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]