The study of mathematical anxiety has seen an increased importance in the past few decades in the field of mathematical education. As this topic is of great interest in education research, this thesis investigates the previous contributions made by other researchers via a literature review of mathematical education papers. Furthermore, a literature review of mathematical models of learning is presented. In the hopes of closing the gap between these two streams of research, this thesis conducts a study of mathematical anxiety at the first year university level through a survey and data analysis, and proposes a theoretical model of learning. Throughout the data analysis, the prevalence, effects, and correlates of mathematical anxiety are examined. Using a version of the Mathematical Anxiety Rating Scale refined by Plake \& Parker in 1982, factors such as gender, high school performance, and program choices are shown to be correlated to mathematical anxiety, as is consistent with previous literature. On the other hand, the model of learning offers a theoretical perspective in understanding the relationship between knowledge, effort, and anxiety, and how these variables interact during a learning experience. This model suggests that given an individual's aptitude, drive, and susceptibility for anxiety, that they may reach various levels of knowledge, effort, and anxiety throughout an academic term. / Thesis / Master of Science (MSc)

published_or_final_version / Statistics / Doctoral / Doctor of Philosophy

No description available.

Digitized by Kansas Correctional Industries

by Chan Chiu Fat. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 99-100). / Acknowledgment --- p.i / Abstract --- p.ii / Introduction --- p.1 / Chapter 1 --- Differentiability On Banach Spaces --- p.4 / Chapter 1.1 --- Introduction --- p.5 / Chapter 1.2 --- Subdifferentiability of Convex Functions --- p.12 / Chapter 1.3 --- Generalized Gradients --- p.16 / Chapter 1.4 --- The Rockafellar Subderivatives --- p.21 / Chapter 2 --- On Minimizing and Stationary Sequences --- p.25 / Chapter 2.1 --- On Equivalence of Minimizing and Stationary Sequences --- p.26 / Chapter 2.2 --- Constrained Optimization Problem --- p.35 / Chapter 2.3 --- Convergence of an Iterative Algorithm --- p.41 / Chapter 3 --- Asymptotically Weil-Behaved Functions --- p.47 / Chapter 3.1 --- Asymptotically Well-Behaved Convex Functions --- p.48 / Chapter 3.2 --- The Subclass R --- p.54 / Chapter 3.3 --- Application on Approximate Problems and Duality Theory --- p.61 / Chapter 4 --- The Minimum and Maximum Principle --- p.71 / Chapter 4.1 --- The Radon-Nikodym Property --- p.71 / Chapter 4.2 --- The Minimum and Maximum Principle for Nonconvex Functions --- p.83 / Bibliography --- p.99

"Exhibits" ([19] leaves) in pocket. Bibliography: leaves 430-440.

Consists mainly of 33 articles and papers reprinted from journals / 1 v. (various paging) ; / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (D.Sc.)--University of Adelaide, Dept. of Statistics, 1980

Spine title. / Lacks table of contents and abstract / Includes bibliographical references. / 11 items ; / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / A collection of 11 of the author's papers reprinted from various journals; submitted as the author's Doctor of Science thesis / Thesis (D.Sc.)--University of Adelaide, 1951

Thesis (M.A.) in Mathematics--University of Maine, 2004. / Includes vita. Includes bibliographical references (leaves 74-75).

The typical subroutines that compute $\\sin(x)$ and $\\exp(x)$ bear little resemblance to our mathematical knowledge of these functions: they are composed of concrete arithmetic expressions that include many mysterious numerical constants. Instead of programming these subroutines conventionally, we can express their construction using symbolic ideas such as periodicity and Taylor series. Such an approach has many advantages: the code is closer to the mathematical basis of the function, less vulnerable to errors, and is trivially adaptable to various precisions.