Mathematical Sciences

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Mathematical research at MSU is focused primarily on related topics in pure and applied mathematics. Research programs complement each other and are often applied to problems in science and engineering. Research in statistics encompasses a broad range of theoretical and applied topics. Because the statisticians are actively engaged in interdisciplinary work, much of the statistical research is directed toward practical problems. Mathematics education faculty are active in both qualitative and quantitative experimental research areas. These include teacher preparation, coaching and mentoring for in-service teachers, online learning and curriculum development.

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    College Algebra Structured Notes Workbook
    (Montana State University, 2024) Staebler, Heidi
    This structured note packet / workbook is designed to be used for in-class instruction by instructors with a wide variety of experience levels in a College Algebra course that prepares students for 4 credit hour Precalculus and Survey of Calculus courses. It includes topics that are found in OER Intermediate Algebra and College Algebra texts and is designed to promote instruction that strikes a balance between promoting foundational skills, conceptual understanding, connections between ideas / representations, applications and modeling. Each section / lesson contains the following components: • Link(s) to online OER reference text section(s) / resource(s) • Sectional objectives and vocabulary words / phrases • Break-out boxes for key definitions / ideas / strategies • Instructional examples interspersed with You Try examples • Associated MyOpenMath homework problem set (pilot during fall 2024) There is not a one-to-one match between each section and a 50-minute class session.
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    The Learning Difficulties Faced by Community College Algebra II Students in Understanding Algebraic and Symbolic Notation
    (Montana State University, 2017-04) Kong, Amy
    This research project is aimed to find out students’ common errors and misconceptions regarding to the understanding of the algebraic and symbolic notation and what factors affecting them in their understanding. 18 students from a semester-long algebra II class from a community college were invited to participate in this study by taking a pre- and a post- diagnostics tests. Students’ answers on the tests were analyzed. 15 students were interviewed afterwards to explain their errors. Students’ answers on the tests and responses from the interviews have shown that students did have some misconceptions concerning learning of algebra. They generally had difficulty in recognizing the roles of the variables that were used in algebraic expressions or equations. They also ignored the order of operations. Most of them had difficulty in knowing the difference between algebraic expressions and equations. The other purpose of this research was to study the effectiveness of adopting collaboration as an instructional strategy on the students’ conceptual understanding of the algebraic and symbolic notation. A paired-samples t-test was conducted to compare the results between the pre- and the post-diagnostic tests in adopting collaboration as an instructional strategy. The results suggested that the collaboration did have an impact on the students’ learning in algebraic and symbolic notation. Specifically, the results suggested that adopting collaboration could help students’ conceptual understanding on the algebraic and symbolic notation. The teaching strategies that might help students combat misconceptions and overcome learning difficulties when learning basic algebra are also discussed in this research report.
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    Stat 216 Course Pack Fall 2015: Activities and Notes
    (2015-08) Robison-Cox, James
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    Stat 216 Course Pack Spring 2016: Activities and Notes
    (2016-01) Robison-Cox, James
    Course notes developed for introductory statistics course at Montana State University using active learning approach. Each activity is preceded by a reading.
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    Intermediate Statistics with R
    (2014-01) Greenwood, Mark C.
    Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. This text covers more advanced graphical summaries, One-Way ANOVA with pair-wise comparisons, Two-Way ANOVA, Chi-square testing, and simple and multiple linear regression models. Models with interactions are discussed in the Two-Way ANOVA and multiple linear regression setting with categorical explanatory variables. Randomization-based inferences are used to introduce new parametric distributions and to enhance understanding of what evidence against the null hypothesis “looks like”. Throughout, the use of the statistical software R via Rstudio is emphasized with all useful code and data sets provided within the text. This is Version 3.1 of the book.
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