Graduate Learning Outcome Statement
Master of Science in Mathematics
Mission Statement
The mission of the graduate program of the Department of Mathematics and Statistics is to provide an excellent education for students in mathematics and statistics, to focus scholarly efforts on expanding our knowledge of those two disciplines, and to participate in activities that promote mathematics and statistics in relevant ways. Our programs prepare students for high-level quantitative careers and for discipline-related Ph.D. programs. Our courses are designed to immerse students in graduate-level mathematics and statistics and to promote independent thinking in students. We strive to instill in students who interact with us an appreciation for the power of mathematics and statistics and a desire to be lifelong learners and practitioners in mathematics or statistics. Graduate Faculty are to engage in research projects that yield new results in their areas of expertise or that apply their knowledge to solve problems of interest to scholars in other disciplines. Graduate Faculty are also to be involved in meaningful professional service to the university and the disciplines regionally, nationally, and internationally. All of our endeavors will be subject to self-reflection designed to maximize their effectiveness.
Learning Outcome Assessment Plan
Learning Outcome:
Recognize and apply principles of abstract mathematics to solve complex problems and to compose coherent and correct proofs in a variety of areas such as linear algebra and probability.
Assessment Criteria:
Assignments and exam questions that directly link to program-level expected learning outcomes and are scored using established criteria in the courses such as linear algebra and probability.
Learning Outcome:
Recognize and apply principles of applied mathematics to solve complex problems in a variety of areas such as numerical analysis and scientific computing.
Assessment Criteria:
Assignments, exams, and projects in Scientific computing, Numerical Analysis.
Learning Outcome:
Recognize connections within mathematics in order to create and use mathematical models and to solve complex problems in one area of mathematics using techniques from other areas of mathematics.
Assessment Criteria:
Assignments and projects in Scientific computing, Numerical Analysis.
Learning Outcome:
Recognize and apply basic statistics principles to theoretical and applied problems.
Assessment Criteria:
Assignments and exam questions that directly link to program-level expected learning outcomes and are scored using established criteria in the course of Statistics Method I.
Learning Outcome:
Present mathematics arising from coursework and independent investigations that extend course content or illuminate areas that are new to the student.
Assessment Criteria:
Oral defense of a written Masters Thesis, Oral Presentation non-thesis option. Presentations in the Graduate Seminar, Student presentations required and conducted by individual instructor.