9. Using other fields

2020 ◽  
pp. 100-113
Author(s):  
Timothy Williamson
Keyword(s):  
Political Philosophy ◽  
Computer Science ◽  
Social Anthropology ◽  
Science Reading ◽  
And Mathematics ◽  
Reading History ◽  
Different Cultures

‘Using other fields’ looks at the connections between philosophy and other fields of systemic enquiry, from psychology and history to physics, economics, and computer science. Reading history is the closest we get to conducting experiments in politics, creating theories of political philosophy. Social anthropology helps us understand different cultures, beliefs, and arguments. Linguistics matters, as language is the essential medium of philosophy and looking for valid patterns across words helps us to assess philosophical experiments. Connections and messages in computer science, hypotheses in economics and mathematics, and perceptions in the sciences, all combine to inform our understanding of philosophy.

scholarly journals Polynomials and General Degree-Based Topological Indices of Generalized Sierpinski Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chengmei Fan ◽  
M. Mobeen Munir ◽  
Zafar Hussain ◽  
Muhammad Athar ◽  
Jia-Bao Liu
Keyword(s):  
Complex Systems ◽  
Computer Science ◽  
Topological Indices ◽  
Fractal Nature ◽  
Similar Nature ◽  
Zagreb Index ◽  
Recursive Sequences ◽  
And Mathematics

Sierpinski networks are networks of fractal nature having several applications in computer science, music, chemistry, and mathematics. These networks are commonly used in chaos, fractals, recursive sequences, and complex systems. In this article, we compute various connectivity polynomials such as M -polynomial, Zagreb polynomials, and forgotten polynomial of generalized Sierpinski networks S k n and recover some well-known degree-based topological indices from these. We also compute the most general Zagreb index known as α , β -Zagreb index and several other general indices of similar nature for this network. Our results are the natural generalizations of already available results for particular classes of such type of networks.

scholarly journals Adaptation of the Core CDIO Standards 3.0 to STEM Higher Education

2021 ◽  
Vol 30 (2) ◽  
pp. 9-21
Author(s):  
A. I. Chuchalin
Keyword(s):  
Applied Sciences ◽  
Higher Education ◽  
Engineering Education ◽  
Computer Science ◽  
High Tech ◽  
The Core ◽  
Science Technology ◽  
Postgraduate Studies ◽  
Systematic Training ◽  
And Mathematics

It is proposed to adapt the new version of the internationally recognized standards for engineering education the Core CDIO Standards 3.0 to the programs of basic higher education in the field of technology, natural and applied sciences, as well as mathematics and computer science in the context of the evolution of STEM. The adaptation of the CDIO standards to STEM higher education creates incentives and contributes to the systematic training of specialists of different professions for coordinated teamwork in the development of high-tech products, as well as in the provision of comprehensive STEM services. Optional CDIO Standards are analyzed, which can be used selectively in STEM higher education. Adaptation of the CDIO-FCDI-FFCD triad to undergraduate, graduate and postgraduate studies in the field of science, technology, engineering and mathematics is considered as a mean for improving the system of three-cycle STEM higher education.

Formative External Evaluation and Data Analysis Report Year Three: Building Opportunities for STEM Success

2020 ◽  
Author(s):  
Angelicque Tucker Blackmon ◽  
Keyword(s):  
Data Analysis ◽  
Computer Science ◽  
Self Efficacy ◽  
Study Skills ◽  
Mathematics Students ◽  
External Evaluation ◽  
College Chemistry ◽  
Before And After ◽  
And Mathematics

This report is an analysis of college chemistry, biology, computer science, and mathematics students' perceptions of STEM self-efficacy and study skills before and after an intervention.

Beyond Practicalism

Utopophobia ◽  
2019 ◽  
pp. 304-315
Author(s):  
David Estlund
Keyword(s):  
Political Philosophy ◽  
Special Interest ◽  
Intrinsic Value ◽  
Intellectual Work ◽  
Math And Science ◽  
The Face ◽  
And Mathematics

This chapter argues against “practicalism.” It shows that it is very plausible that some things must be of intrinsic value, that is, apart from what they can be used to produce. A narrower practicalism might hold that intellectual work in particular is never of intrinsic value, and so is worthless unless it is of practical value. The chapter contends that this flies in the face of some robust views about the value of some intellectual work in science and mathematics. This leaves two problems of special interest here: first, so far, even if that point makes general intellectual practicalism appear implausible, it has no tendency to show that nonpractical philosophy, or in particular political philosophy, might be of intrinsic value. They might lack whatever it is about nonpractical yet important math and science that makes them important. This leads to the second problem, which is that even if those examples tend to refute practicalism, they do not yet provide any account of what is valuable about them.

Computational Thinking and Mathematics

2016 ◽  
pp. 853-865
Author(s):  
Thiago Schumacher Barcelos ◽  
Ismar Frango Silveira
Keyword(s):  
Problem Solving ◽  
Computer Science ◽  
Basic Education ◽  
National Curriculum ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Educational Systems ◽  
Curriculum Guidelines ◽  
And Mathematics ◽  
The One

On the one hand, ensuring that students archive adequate levels of Mathematical knowledge by the time they finish basic education is a challenge for the educational systems in several countries. On the other hand, the pervasiveness of computer-based devices in everyday situations poses a fundamental question about Computer Science being part of those known as basic sciences. The development of Computer Science (CS) is historically related to Mathematics; however, CS is said to have singular reasoning mechanics for problem solving, whose applications go beyond the frontiers of Computing itself. These problem-solving skills have been defined as Computational Thinking skills. In this chapter, the possible relationships between Math and Computational Thinking skills are discussed in the perspective of national curriculum guidelines for Mathematics of Brazil, Chile, and United States. Three skills that can be jointly developed by both areas are identified in a literature review. Some challenges and implications for educational research and practice are also discussed.

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