Balancing temporal shortages of renewable energy with natural gas for the generation of electricity is a challenge for dispatchers. This is compounded by the recent proposal of blending cleanly-produced hydrogen into natural gas networks. To introduce the concepts of gas flow, this thesis begins by linearizing the partial differential equations (PDEs) that govern the flow of natural gas in a single pipe. The solution of the linearized PDEs is used to investigate wave attenuation and characterize critical operating regions where linearization is applicable. The nonlinear PDEs for a single gas are extended to mixtures of gases with the addition of a PDE that governs the conservation of composition. The gas mixture formulation is developed for general gas networks that can inject or withdraw arbitrary time-varying mixtures of gases into or from the network at arbitrarily specified nodes, while being influenced by time-varying control actions of compressor units. The PDE formulation is discretized in space to form a nonlinear control system of ordinary differential equations (ODEs), which is used to prove that homogeneous mixtures are well-behaved and heterogeneous mixtures may be ill-behaved in the sense of monotone-ordering of solutions. Numerical simulations are performed to compute interfaces that delimit monotone and periodic system responses. The ODE system is used as the constraints of an optimal control problem (OCP) to minimize the expended energy of compressors. Moreover, the ODE system for the natural gas network is linearized and used as the constraints of a linear OCP. The OCPs are digitally implemented as optimization problems following the discretization of the time domain. The optimization problems are applied to pipelines and small test networks. Some qualitative and computational applications, including linearization error analysis and transient responses, are also investigated.

This three-article dissertation considers the pedagogical practices for developing statistically literate students and teaching data-driven decision-making with the goal of preparing students for civic engagement and improving student achievement. The first article discusses a critical review of the literature on data-driven decision-making project conditions in K-12 educational settings. Upon reviewing the literature, I synthesized and summarized the current practices into three distinct models. The models serve to clarify the pedagogical choices of the teacher and the degree at which students' views are involved and incorporated into the projects. I propose an alternative model/framework and discuss possible implications in the classroom. In the second article, I use the framework developed in the first article as the basis for an educational research intervention. I describe a study where I developed a handbook based on the framework and implemented a sample of professional development sessions from the handbook. Advisors and teachers provided feedback on the handbook and professional development. This feedback served as the subject of analysis while I continued to refine the handbook and the professional learning sessions. I describe the refinement process and the implications in terms of design decisions of educational interventions and statistical knowledge for teaching. The final article performs a secondary data analysis of school, teacher, and student level data using the Trends in International Mathematics and Science Study (TIMSS) database. The paper seeks to answer the research question: “Which aspects of teacher professional knowledge measures predict student achievement in the mathematical domain of data and statistical topics?” The results indicate that when controlling for school level wealth index, teacher characteristics are not as influential as the school level wealth index. I discuss future research as well as school policy and curriculum implications of these results.

There is a need in the ecology literature to have a discussion about the fundamental theories from which population dynamics arises. Ad hoc model development is not uncommon in the field often as a result of a need to publish rapidly and frequently. Ecologists and statisticians like Robert J. Steidl and Kenneth P Burnham have called for a more deliberative approach they call "hard thinking". For example, the phenomena of population growth can be captured by almost any sigmoid function. The question of which sigmoid function best explains a data set cannot be answered meaningfully by statistical regression since that can only speak to the validity of the shape. There is a need to revisit enzyme kinetics and ecological stoichiometry to properly justify basal model selection in ecology. This dissertation derives several common population growth models from a generalized equation. The mechanistic validity of these models in different contexts is explored through a kinetic lens. The behavioral kinetic framework is then put to the test by examining a set of biologically plausible growth models against the 1968-1995 elk population count data for northern Yellowstone. Using only this count data, the novel Monod-Holling growth model was able to accurately predict minimum viable population and life expectancy despite both being exogenous to the model and data set. Lastly, the elk/wolf data from Yellowstone was used to compare the validity of the Rosenzweig-MacArthur and Arditi-Ginzburg models. They both were derived from a more general model which included both predator and prey mediated steps. The Arditi-Ginzburg model was able to fit the training data better, but only the Rosenzweig-MacArthur model matched the validation data. Accounting for animal sexual behavior allowed for the creation of the Monod-Holling model which is just as simple as the logistic differential equation but provides greater insights for conservation purposes. Explicitly acknowledging the ethology of wolf predation helps explain the differences in predictive performances by the best fit Rosenzweig-MacArthur and Arditi-Ginzburg models. The behavioral kinetic framework has proven to be a useful tool, and it has the ability to provide even further insights going forward.

This dissertation is on the topic of sameness of representation of mathematical entities from a mathematics education perspective. In mathematics, people frequently work with different representations of the same thing. This is especially evident when considering the prevalence of the equals sign (=). I am adopting the three-paper dissertation model. Each paper reports on a study that investigates understandings of the identity relation. The first study directly addresses function identity: how students conceptualize, work with, and assess sameness of representation of function. It uses both qualitative and quantitative methods to examine how students understand function sameness in calculus contexts. The second study is on the topic of implicit differentiation and student understanding of the legitimacy of it as a procedure. This relates to sameness insofar as differentiating an equation is a valid inference when the equation expresses function identity. The third study directly addresses usage of the equals sign (“=”). In particular, I focus on the notion of symmetry; equality is a symmetric relation (truth-functionally), and mathematicians understand it as such. However, results of my study show that usage is not symmetric. This is small qualitative study and incorporates ideas from the field of linguistics.

Learning loss occurs during academic breaks, and this can be detrimental to student success especially in sequential classes like Arizona State University’s Engineering Calculus sequence in which retention of the topics taught in a prior class is expected. The Keeping in School Shape Program (KiSS) is designed as a cost effective, efficient, and accessible way of addressing this problem. The KiSS program uses push technology to give students a way to regularly review material over academic breaks while also fostering a growth mindset.Every day, during an academic break, students are sent a link via text message or email to access a multiple-choice daily review problem which represents material from a previous course that is requisite for success in an upcoming course. Before solving the daily problem, students use a 5-point scale to indicate how confident they are that they can solve the problem. Students then complete the daily review problem and have a variety of resources to support them as they do so, as well as options after they complete it. Students are able to view a hint and try a problem again, view a solution, and attempt a challenge problem. On Tuesdays (aka 2’s-Days) students are given the opportunity to complete either an additional daily review problem or an additional challenge problem, and on Sundays (aka Trivia Days) students can decide between completing only a mathematics trivia question or trivia along with the daily review problem. There is much to be learned from each individual student who participates in the KiSS program. Three surveys were conducted during the Winter Break 2020 KiSS program that gave insight into students’ experience in the KiSS program along with their personal background and mindset regarding mathematics. Ten students responded to all three of these surveys. This thesis will present a case study for each of these ten students based on their data from program participation and survey responses. Conclusions will be drawn regarding ways in which the KiSS program is helping students and ways in which it can be improved to help students be better prepared for their upcoming studies.

This dissertation report follows a three-paper format, with each paper having a different but related focus. In Paper 1 I discuss conceptual analysis of mathematical ideas relative to its place within cognitive learning theories and research studies. In particular, I highlight specific ways mathematics education research uses conceptual analysis and discuss the implications of these uses for interpreting and leveraging results to produce empirically tested learning trajectories. From my summary and analysis I develop two recommendations for the cognitive researchers developing empirically supported learning trajectories. (1) A researcher should frame his/her work, and analyze others’ work, within the researcher’s image of a broadly coherent trajectory for student learning and (2) that the field should work towards a common understanding for the meaning of a hypothetical learning trajectory.

In Paper 2 I argue that prior research in online learning has tested the impact of online courses on measures such as student retention rates, satisfaction scores, and GPA but that research is needed to describe the meanings students construct for mathematical ideas researchers have identified as critical to their success in future math courses and other STEM fields. This paper discusses the need for a new focus in studying online mathematics learning and calls for cognitive researchers to begin developing a productive methodology for examining the meanings students construct while engaged in online lessons.

Paper 3 describes the online Precalculus course intervention we designed around measurement imagery and quantitative reasoning as themes that unite topics across units. I report results relative to the meanings students developed for exponential functions and related ideas (such as percent change and growth factors) while working through lessons in the intervention. I provide a conceptual analysis guiding its design and discuss pre-test and pre-interview results, post-test and post-interview results, and observations from student behaviors while interacting with lessons. I demonstrate that the targeted meanings can be productive for students, show common unproductive meanings students possess as they enter Precalculus, highlight challenges and opportunities in teaching and learning in the online environment, and discuss needed adaptations to the intervention and future research opportunities informed by my results.

There have been a number of studies that have examined students’ difficulties in understanding the idea of logarithm and the effectiveness of non-traditional interventions. However, there have been few studies that have examined the understandings students develop and need to develop when completing conceptually oriented logarithmic lessons. In this document, I present the three papers of my dissertation study. The first paper examines two students’ development of concepts foundational to the idea of logarithm. This paper discusses two essential understandings that were revealed to be problematic and essential for students’ development of productive meanings for exponents, logarithms and logarithmic properties. The findings of this study informed my later work to support students in understanding logarithms, their properties and logarithmic functions. The second paper examines two students’ development of the idea of logarithm. This paper describes the reasoning abilities two students exhibited as they engaged with tasks designed to foster their construction of more productive meanings for the idea of logarithm. The findings of this study provide novel insights for supporting students in understanding the idea of logarithm meaningfully. Finally, the third paper begins with an examination of the historical development of the idea of logarithm. I then leveraged the insights of this literature review and the first two papers to perform a conceptual analysis of what is involved in learning and understanding the idea of logarithm. The literature review and conceptual analysis contributes novel and useful information for curriculum developers, instructors, and other researchers studying student learning of this idea.

The advancement of technology has substantively changed the practices of numerous professions, including teaching. When an instructor first adopts a new technology, established classroom practices are perturbed. These perturbations can have positive and negative, large or small, and long- or short-term effects on instructors’ abilities to teach mathematical concepts with the new technology. Therefore, in order to better understand teaching with technology, we need to take a closer look at the adoption of new technology in a mathematics classroom. Using interviews and classroom observations, I explored perturbations in mathematical classroom practices as an instructor implemented virtual manipulatives as novel didactic objects in rational function instruction. In particular, the instructor used didactic objects that were designed to lay the foundation for developing a conceptual understanding of rational functions through the coordination of relative size of the value of the numerator in terms of the value of the denominator. The results are organized according to a taxonomy that captures leader actions, communication, expectations of technology, roles, timing, student engagement, and mathematical conceptions.

Researchers have documented the importance of seeing a graph as an emergent trace of how two quantities’ values vary simultaneously in order to reason about the graph in terms of quantitative relationships. If a student does not see a graph as a representation of how quantities change together then the student is limited to reasoning about perceptual features of the shape of the graph.

This dissertation reports results of an investigation into the ways of thinking that support and inhibit students from constructing and reasoning about graphs in terms of covarying quantities. I collected data by engaging three university precalculus students in asynchronous teaching experiments. I designed the instructional sequence to support students in making three constructions: first imagine representing quantities’ magnitudes along the axes, then simultaneously represent these magnitudes with a correspondence point in the plane, and finally anticipate tracking the correspondence point to track how the two quantities’ attributes change simultaneously.

Findings from this investigation provide insights into how students come to engage in covariational reasoning and re-present their imagery in their graphing actions. The data presented here suggests that it is nontrivial for students to coordinate their images of two varying quantities. This is significant because without a way to coordinate two quantities’ variation the student is limited to engaging in static shape thinking.

I describe three types of imagery: a correspondence point, Tinker Bell and her pixie dust, and an actor taking baby steps, that supported students in developing ways to coordinate quantities’ variation. I discuss the figurative aspects of the students’ coordination in order to account for the difficulties students had (1) constructing a multiplicative object that persisted under variation, (2) reconstructing their acts of covariation in other graphing tasks, and (3) generalizing these acts of covariation to reason about formulas in terms of covarying quantities.