The aims of university System Dynamics (SD) programs include the mastery of systems thinking skills, modeling skills, and/or application to solving real-world, dynamic, complex problems. Existing research has articulated learning sequences of what learners should know and be able to do, and when. It is called the competence development framework for learning SD (Schaffernicht & Groesser, 2016). A learning sequence is a hierarchy because it presumes that students must learn one learning goal in the sequence before they are able to learn the next. The hierarchical nature of this competence framework for SD has been validated via expert consensus of its conceptualization and ordering but has not been empirically tested and validated. This research sought to validate the beginner level sequence of the competence framework for SD by establishing relationships between the learning goals in terms of their hierarchical structure, necessity, and sufficiency. The research was grounded in Knowledge Space Theory (Albert & Lukas, 1999; Doignon & Falmagne, 1985, 2012, 2016; Unlu & Sargin, 2010). It used a quantitative design and a unit of information called practical learning outcomes (LOs), which are the assessment outcomes associated with each learning goal (LG). The assessment information was collected from college classroom assessments administered at three points in time. Learning outcomes were organized and displayed in an assessment information system to show the response patterns of students over time, and inductive item tree analysis gave further information on the hierarchical relationships and linear versus non-linear nature of the LGs. Finally, Bayes' formula for the posterior probability of the working hypothesis was used to evaluate causal relationships in terms of how necessary and sufficient attainment of one learning goal is on another learning goal. Results showed in general that aspects of this small section of the competence framework for SD are valid in terms of some of the sequence, hierarchical nature, and necessary causal relationships of some of the learning goals; but other aspects do not match. More specifically, the results can help to focus SD educators' attention on critical learning goals in the sequence on which to spend valuable and limited instructional time. Some of these points include key learning goals for which their attainment implies attainment of several other learning goals, such as Explains "polarity"; Identifies linear behavior (when shown a behavior over time (BOT) graph); explain S shaped growth; and Infers a stock's accumulation behavior given the flows. Other key points include learning goals for which there is a causal relationship, such as Describes linear behavior (in words or as a graph, indicated or drawn) a Describes exponential behavior (in words or as a graph, indicated or drawn); and Describes the difference and the relationship between stock and flow a Describes a stock's accumulation behavior given the in- and outflows. Unique contributions of this research included the demonstration of a type of educational information and assessment method for practical use in the classroom; application of a method of empirical validation for a segment of the competence framework for SD; and creation of additional assessment questions and associated rubrics for use by SD professors, all of which could help support improved effectiveness and efficiency in SD teaching. [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.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]