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Physical Sciences and Mathematics Commons™

Open Access. Powered by Scholars. Published by Universities.®

Education

Central Washington University

2021

Articles 1 - 5 of 5

Full-Text Articles in Physical Sciences and Mathematics

Integrating Common Data Analytics Tools Into Non-Technical Undergraduate Curricula, Kurt Kirstein Apr 2021

Integrating Common Data Analytics Tools Into Non-Technical Undergraduate Curricula, Kurt Kirstein

All Faculty Scholarship for the College of Education and Professional Studies

Aside from statistics courses, accessible data analytics skills are often excluded from traditional non-technical university programs. These are topics that are typically the domain of programs that focus on math, statistics and computer science. Yet the need for these skills in non-technical disciplines is changing. A rapid expansion of data-related processes in organizations of many types requires individuals who have at least a working knowledge of common analytic tools. This article briefly describes three categories of data analytics tools that can be useful for graduates in any discipline. The first category covers descriptive tools that allow students to learn what …

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Can CP Be Less Than CV?, Yingbin Ge, Samuel L. Montgomery, Gabriel L. Borrello Apr 2021

Can CP Be Less Than CV?, Yingbin Ge, Samuel L. Montgomery, Gabriel L. Borrello

All Faculty Scholarship for the College of the Sciences

Can CP be less than CV? This is a fundamental question in physics, chemistry, chemical engineering, and mechanical engineering. This question hangs in the minds of many students, instructors, and researchers. The first instinct is to answer “Yes, for water between 0 and 4 °C” if one knows that water expands as temperature decreases in this temperature range. The same question is asked in several Physical Chemistry and Physics textbooks. Students are supposed to answer that water contracts when heated at below 4 °C in an isobaric process. Because work is done to the contracting water, less …

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Math Escape Rooms: A Novel Approach For Engaging Learners In Math Circles, Janice F. Rech, Paula Jakopovic, Hannah Seidl, Greg Lawson, Rachel Pugh Mar 2021

Math Escape Rooms: A Novel Approach For Engaging Learners In Math Circles, Janice F. Rech, Paula Jakopovic, Hannah Seidl, Greg Lawson, Rachel Pugh

Journal of Math Circles

Engaging middle and high school students in Math Circles requires time, planning and creativity. Finding novel approaches to maintain the interest of a variety of learners can be challenging. This paper outlines a model for developing and implementing math escape rooms as a unique structure for facilitating collaborative problem solving in a Math Circle. These escape rooms were designed and hosted by undergraduate secondary mathematics education majors. We provide possible structures for hosting escape rooms that could translate to a range of settings, as well as reflections and lessons learned through our experiences that could inform practitioners in other settings.

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Mathematical Zendo: A Game Of Patterns And Logic, Philip Deorsey, Corey Pooler, Michael Ferrara Jan 2021

Mathematical Zendo: A Game Of Patterns And Logic, Philip Deorsey, Corey Pooler, Michael Ferrara

Journal of Math Circles

Mathematical Zendo is a logic game that actively engages participants in pattern recognition, problem solving, and critical thinking while providing a fun opportunity to explore all manner of mathematical objects. Based upon the popular game of Zendo, created by Looney Labs, Mathematical Zendo centers on a secret rule, chosen by the leader, that must be guessed by teams of players. In each round of the game, teams provide examples of the mathematical object of interest (e.g. functions, numbers, sets) and receive information about whether their guesses do or do not satisfy the secret rule. In this paper, we introduce Mathematical …

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Full Interpretable Machine Learning Method With In-Line Coordinates, Hoang Phan Jan 2021

Full Interpretable Machine Learning Method With In-Line Coordinates, Hoang Phan

All Master's Theses

This thesis explores a new approach for machine learning classification task in 2-dimensional space (2-D ML) with In-line Coordinates. This is a full machine learning approach that does not require to deal with n-dimensional data in n-dimensional space. In-line coordinates method allows discovering n-D patterns in 2-D space without loss of n-D information using graph representation of n-D data in 2-D. Specifically, this thesis shows that it can be done with In-line Based Coordinates in different modifications, which are defined, including static and dynamic ones. Some classification and regression algorithms based on these In-line Coordinates were explored. Two successful cases …

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