Students are more likely to succeed in courses they find valuable (Eccles et al., 1983; Wigfield, Rosenzweig, & Eccles, 2017). However, many students, especially those from backgrounds typically underrepresented in education, often struggle to find value in STEM courses (Lewis & Connell (2005). One possible explanation for this is that many students do not have the chance to explore how course material may be personally relevant to them. Utility-value interventions help address this major issue by scaffolding students to identify their own personally meaningful connections between their classes and their real lives (Hulleman & Harackiewicz, 2009).
Although numerous studies have shown the potential for utility-value interventions to increase student performance and motivation (Hulleman et al., 2009; Harackiewicz, Canning, Tibbetts, Prinski, & Hyde, 2016; Hulleman, Godes, Hendricks, & Harackiewicz, 2010; Gaspard et al., 2015), little is known about the active ingredients that make these interventions most effective. This problem is a particularly important one to solve given that recent work has pointed to the fact that the effectiveness of these interventions depends on strong student engagement with the intervention materials (Hulleman et al., in prep).
Recent work conducted with Character Lab has sought to explore this issue by developing and testing new intervention designs that support student engagement in different ways. Building on previous work by Hanna Gaspard and colleagues (2015), researchers developed an intervention in which high school participants read quotes from previous students about how they have used math in their real lives, and then were asked to rate how relevant they found the quotes and discuss how the quotes could be improved for future students. Findings suggest that this intervention not only decreased math anxiety (b*=-0.384, p=.025) and increased student perceptions of math utility value (b*=.522, p=.002)--controlling for student demographics, prior achievement, and pre-intervention measures of these variables--but also significantly increased the math GPA of students who received free/reduced price lunch (b*=6.714, p=.031).
The current study seeks to replicate these findings with a new population, as well as further investigate the psychological and intervention mechanisms essential to this study's success. It capitalizes and extends on prior work by exploring effective ways to prepare high school students to think about how math might help them in the current or future lives. Through identifying methods that support student engagement in the intervention, researchers can develop utility-value interventions that better support student motivation and achievement.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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Data from a comparative judgement survey consisting of 62 working mathematics educators (ME) at Norwegian universities or city colleges, and 57 working mathematicians at Norwegian universities. A total of 3607 comparisons of which 1780 comparisons by the ME and 1827 ME. The comparative judgement survey consisted of respondents comparing pairs of statements on mathematical definitions compiled from a literature review on mathematical definitions in the mathematics education literature. Each WM was asked to judge 40 pairs of statements with the following question: “As a researcher in mathematics, where your target group is other mathematicians, what is more important about mathematical definitions?” Each ME was asked to judge 41 pairs of statements with the following question: “For a mathematical definition in the context of teaching and learning, what is more important?” The comparative judgement was done with No More Marking software (nomoremarking.com) The data set consists of the following data: comparisons made by ME (ME.csv) comparisons made by WM (WM.csv) Look up table of codes of statements and statement formulations (key.csv) Each line in the comparison represents a comparison, where the "winner" column represents the winner and the "loser" column the loser of the comparison.
MIT Licensehttps://opensource.org/licenses/MIT
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Protein-Protein, Genetic, and Chemical Interactions for MATH-4 (Caenorhabditis elegans) curated by BioGRID (https://thebiogrid.org); DEFINITION: Protein MATH-4
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Abstract In this paper we turn our attention to the different language games associated to the development of Mathematical Modelling activities and to the meanings constituted by students within these language games in relation to the first order ordinary differential equations. The research is based on Mathematical Modelling in Mathematics Education and has as its philosophical basis the studies of Ludwig Wittgenstein and some of his interpreters. Considering these theoretical-philosophical elements, mathematical modelling activities were developed in a Mathematics Degree in a course of Ordinary Differential Equations. Data were collected through written records, audio and video recordings, questionnaires, and interviews. The data analysis methodology considers the students' discursive practices and allowed us to construct trees of idea association. The results indicate that the constitution of meaning within modelling activities is associated to the students' linguistic appropriation of the rules and techniques that are configured in specific language games identified in the Mathematical Modelling activities.
MIT Licensehttps://opensource.org/licenses/MIT
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Protein-Protein, Genetic, and Chemical Interactions for MATH-34 (Caenorhabditis elegans) curated by BioGRID (https://thebiogrid.org); DEFINITION: Protein MATH-34
MIT Licensehttps://opensource.org/licenses/MIT
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Protein-Protein, Genetic, and Chemical Interactions for MATH-42 (Caenorhabditis elegans) curated by BioGRID (https://thebiogrid.org); DEFINITION: Protein MATH-42
CONTEXT
Practice Scenario: The UIW School of Engineering wants to recruit more students into their program. They will recruit students with great math scores. Also, to increase the chances of recruitment, the department will look for students who qualify for financial aid. Students who qualify for financial aid more than likely come from low socio-economic backgrounds. One way to indicate this is to view how much federal revenue a school district receives through its state. High federal revenue for a school indicates that a large portion of the student base comes from low incomes families.
The question we wish to ask is as follows: Name the school districts across the nation where their Child Nutrition Programs(c25) are federally funded between the amounts $30,000 and $50,000. And where the average math score for the school districts corresponding state is greater than or equal to the nations average score of 282.
The SQL query below in 'Top5MathTarget.sql' can be used to answer this question in MySQL. To execute this process, one would need to install MySQL to their local system and load the attached datasets below from Kaggle into their MySQL schema. The SQL query below will then join the separate tables on various key identifiers.
DATA SOURCE Data is sourced from The U.S Census Bureau and The Nations Report Card (using the NAEP Data Explorer).
Finance: https://www.census.gov/programs-surveys/school-finances/data/tables.html
Math Scores: https://www.nationsreportcard.gov/ndecore/xplore/NDE
COLUMN NOTES
All data comes from the school year 2017. Individual schools are not represented, only school districts within each state.
FEDERAL FINANCE DATA DEFINITIONS
t_fed_rev: Total federal revenue through the state to each school district.
C14- Federal revenue through the state- Title 1 (no child left behind act).
C25- Federal revenue through the state- Child Nutrition Act.
Title 1 is a program implemented in schools to help raise academic achievement for all students. The program is available to schools where at least 40% of the students come from low inccome families.
Child Nutrition Programs ensure the children are getting the food they need to grow and learn. Schools with high federal revenue to these programs indicate students that also come from low income families.
MATH SCORES DATA DEFINITIONS
Note: Mathematics, Grade 8, 2017, All Students (Total)
average_scale_score - The state's average score for eighth graders taking the NAEP math exam.
A new approach to the validation of surface texture form removal methods is introduced. A linear algebra technique is presented that obtains total least squares (TLS) model fits for a continuous mathematical surface definition. This model is applicable to both profile and areal form removal, and can be used for a range of form removal models including polynomial and spherical fits. The continuous TLS method enables the creation of mathematically traceable reference pairs suitable for the assessment of form removal algorithms in surface texture analysis software. Multiple example reference pairs are presented and used to assess the performance of four tested surface texture analysis software packages. The results of each software are compared against the mathematical reference, highlighting their strengths and weaknesses.
MIT Licensehttps://opensource.org/licenses/MIT
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Protein-Protein, Genetic, and Chemical Interactions for MATH-39 (Caenorhabditis elegans) curated by BioGRID (https://thebiogrid.org); DEFINITION: MATH (meprin-associated Traf homology) domain containing
MIT Licensehttps://opensource.org/licenses/MIT
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Protein-Protein, Genetic, and Chemical Interactions for MATH-41 (Caenorhabditis elegans) curated by BioGRID (https://thebiogrid.org); DEFINITION: MATH (meprin-associated Traf homology) domain containing
MIT Licensehttps://opensource.org/licenses/MIT
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Protein-Protein, Genetic, and Chemical Interactions for MATH-33 (Caenorhabditis elegans) curated by BioGRID (https://thebiogrid.org); DEFINITION: Protein MATH-33
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Thematic meanings of numerical definitions of subject data in various fields of science lead to manipulation of digital codes of known physical, chemical, biological, genetic and other quantities. In principle, each scientific justification contains, to one degree or another, a quantitative, qualitative characteristic of comparison or content. Thus, the language of natural numbers, like mathematical operations, can be accompanied by any definition in any terminology. In this text, the author does not use well-known terms related to the main scientific areas. In this text, the numbers speak for themselves. Any combination of orders or compositions of complex numerical structures presented in this text has its own logical meaning. Any paradox of numerical combinations is an algorithm of real values of numbers.
MIT Licensehttps://opensource.org/licenses/MIT
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Protein-Protein, Genetic, and Chemical Interactions for MATH-48 (Caenorhabditis elegans) curated by BioGRID (https://thebiogrid.org); DEFINITION: MATH (meprin-associated Traf homology) domain containing
U.S. Government Workshttps://www.usa.gov/government-works
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The release of the LCA Commons Unit Process Data: field crop production Version 1.1 includes the following updates:Added meta data to reflect USDA LCA Digital Commons data submission guidance including descriptions of the process (reference to which the size of the inputs and outputs in the process relate, description of the process and technical scope and any aggregation; definition of the technology being used, its operating conditions); temporal representatives; geographic representativeness; allocation methods; process type (U: unit process, S: system process); treatment of missing intermediate flow data; treatment of missing flow data to or from the environment; intermediate flow data sources; mass balance; data treatment (description of the methods and assumptions used to transform primary and secondary data into flow quantities through recalculating, reformatting, aggregation, or proxy data and a description of data quality according to LCADC convention); sampling procedures; and review details. Also, dataset documentation and related archival publications are cited in the APA format.Changed intermediate flow categories and subcategories to reflect the ISIC International Standard Industrial Classification (ISIC).Added “US-” to the US state abbreviations for intermediate flow locations.Corrected the ISIC code for “CUTOFF domestic barge transport; average fuel” (changed to ISIC 5022: Inland freight water transport).Corrected flow names as follows: "Propachlor" renamed "Atrazine". “Bromoxynil octanoate” renamed “Bromoxynil heptanoate”. “water; plant uptake; biogenic” renamed “water; from plant uptake; biogenic” half the instances of “Benzene, pentachloronitro-“ replaced with Etridiazole and half with “Quintozene”. “CUTOFF phosphatic fertilizer, superphos. grades 22% & under; at point-of-sale” replaced with “CUTOFF phosphatic fertilizer, superphos. grades 22% and under; at point-of-sale”.Corrected flow values for “water; from plant uptake; biogenic” and “dry matter except CNPK; from plant uptake; biogenic” in some datasets.Presented data in the International Reference Life Cycle Data System (ILCD)1 format, allowing the parameterization of raw data and mathematical relations to be presented within the datasets and the inclusion of parameter uncertainty data. Note that ILCD formatted data can be converted to the ecospold v1 format using the OpenLCA software.Data quality rankings have been updated to reflect the inclusion of uncertainty data in the ILCD formatted data.Changed all parameter names to “pxxxx” to accommodate mathematical relation character limitations in OpenLCA. Also adjusted select mathematical relations to recognize zero entries. The revised list of parameter names is provided in the documentation attached.Resources in this dataset:Resource Title: Cooper-crop-production-data-parameterization-version-1.1 .File Name: Cooper-crop-production-data-parameterization-version-1.1.xlsxResource Description: Description of parameters that define the Cooper Unit process data for field crop production version 1.1Resource Title: Cooper_Crop_Data_v1.1_ILCD.File Name: Cooper_Crop_Data_v1.1_ILCD.zipResource Description: .zip archive of ILCD xml files that comprise crop production unit process modelsResource Software Recommended: openLCA,url: http://www.openlca.org/Resource Title: Summary of Revisions of the LCA Digital Commons Unit Process Data: field crop production for version 1.1 (August 2013).File Name: Summary of Revisions of the LCA Digital Commons Unit Process Data- field crop production, Version 1.1 (August 2013).pdfResource Description: Documentation of revisions to version 1 data that constitute version 1.1
Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
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Supplementary materials for chapter 5 of the doctoral dissertation "How we think about numbers - Early counting and mathematical abstraction". Contains preregistration, open data and open materialsAs children learn to count, they make one of their first mathematical abstractions. They initially learn how numbers in the count sequence correspond to quantities of physical things if the rules of counting are followed (i.e., if you say the numbers in order “one two three four …” as you tag each thing with a number). Around the age of four-years-old, children discover that these rules also define numbers in relation to each other, such that numbers contain meaning in themselves and without reference to the physical world (e.g., “five” is “one” more than “four”). It is through learning to count, that children discover the natural numbers as mathematical symbols defined by abstract rules.In this dissertation, I explored the developmental trajectory and the cognitive mechanisms of how we gain an understanding of the natural numbers as children. I present new methodological, empirical, and theoretical insights on how and when in the process of learning to count, children discover that numbers represent cardinalities, that numbers can be defined in relation to each other by the successor function and that numbers refer to units. Lastly, I explore this mathematical abstraction as the foundation of how we think about numbers as adults.My work critically tested prominent theories on how learning to count gives meaning to numbers through analogical mapping and conceptual bootstrapping. Findings across five empirical studies suggest that the process is more gradual and continuous than previous theories have proposed. Children begin to understand numbers as cardinalities defined in relation to other numbers by the successor function before they fully grasp the rules of counting. With learning the rules of counting this understanding continuously expands and matures. I further suggest that children may only fully understand numbers as abstract mathematical symbols once they understand how counting and numbers refer to the abstract notion of units rather than to physical things.The central finding of this dissertation is that learning to count does not change children’s understanding of numbers altogether and all at once. Nonetheless, when learning to count, children accomplish a fascinating mathematical abstraction, which builds the foundation for lifelong mathematical learning.© Theresa Elise Wege, CC BY-NC 4.0
Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
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The HWRT database of handwritten symbols contains on-line data of handwritten symbols such as all alphanumeric characters, arrows, greek characters and mathematical symbols like the integral symbol.
The database can be downloaded in form of bzip2-compressed tar files. Each tar file contains:
All CSV files use ";" as delimiter and "'" as quotechar. The data is given in YAML format as a list of lists of dictinaries. Each dictionary has the keys "x", "y" and "time". (x,y) are coordinates and time is the UNIX time.
About 90% of the data was made available by Daniel Kirsch via github.com/kirel/detexify-data. Thank you very much, Daniel!
This tutorial presents an introduction to Electrochemical Impedance Spectroscopy (EIS) theory and has been kept as free from mathematics and electrical theory as possible. If you still find the material presented here difficult to understand, don't stop reading. You will get useful information from this application note, even if you don't follow all of the discussions.
Four major topics are covered in this Application Note.
AC Circuit Theory and Representation of Complex Impedance Values
Physical Electrochemistry and Circuit Elements
Common Equivalent Circuit Models
Extracting Model Parameters from Impedance Data
No prior knowledge of electrical circuit theory or electrochemistry is assumed. Each topic starts out at a quite elementary level, then proceeds to cover more advanced material.
The number of postsecondary graduates, by International Standard Classification of Education (ISCED), institution type, Classification of Instructional Programs (CIP) 2021, STEM (science, technology, engineering and mathematics) and BHASE (business, humanities, health, arts, social science and education) groupings, status of student in Canada, age group and gender.
MIT Licensehttps://opensource.org/licenses/MIT
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Protein-Protein, Genetic, and Chemical Interactions for BATH-30 (Caenorhabditis elegans) curated by BioGRID (https://thebiogrid.org); DEFINITION: BTB and MATH domain containing
MIT Licensehttps://opensource.org/licenses/MIT
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Protein-Protein, Genetic, and Chemical Interactions for BPM5 (Arabidopsis thaliana (Columbia)) curated by BioGRID (https://thebiogrid.org); DEFINITION: BTB-POZ and math domain 5
Students are more likely to succeed in courses they find valuable (Eccles et al., 1983; Wigfield, Rosenzweig, & Eccles, 2017). However, many students, especially those from backgrounds typically underrepresented in education, often struggle to find value in STEM courses (Lewis & Connell (2005). One possible explanation for this is that many students do not have the chance to explore how course material may be personally relevant to them. Utility-value interventions help address this major issue by scaffolding students to identify their own personally meaningful connections between their classes and their real lives (Hulleman & Harackiewicz, 2009).
Although numerous studies have shown the potential for utility-value interventions to increase student performance and motivation (Hulleman et al., 2009; Harackiewicz, Canning, Tibbetts, Prinski, & Hyde, 2016; Hulleman, Godes, Hendricks, & Harackiewicz, 2010; Gaspard et al., 2015), little is known about the active ingredients that make these interventions most effective. This problem is a particularly important one to solve given that recent work has pointed to the fact that the effectiveness of these interventions depends on strong student engagement with the intervention materials (Hulleman et al., in prep).
Recent work conducted with Character Lab has sought to explore this issue by developing and testing new intervention designs that support student engagement in different ways. Building on previous work by Hanna Gaspard and colleagues (2015), researchers developed an intervention in which high school participants read quotes from previous students about how they have used math in their real lives, and then were asked to rate how relevant they found the quotes and discuss how the quotes could be improved for future students. Findings suggest that this intervention not only decreased math anxiety (b*=-0.384, p=.025) and increased student perceptions of math utility value (b*=.522, p=.002)--controlling for student demographics, prior achievement, and pre-intervention measures of these variables--but also significantly increased the math GPA of students who received free/reduced price lunch (b*=6.714, p=.031).
The current study seeks to replicate these findings with a new population, as well as further investigate the psychological and intervention mechanisms essential to this study's success. It capitalizes and extends on prior work by exploring effective ways to prepare high school students to think about how math might help them in the current or future lives. Through identifying methods that support student engagement in the intervention, researchers can develop utility-value interventions that better support student motivation and achievement.