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.

The Trends in International Mathematics and Science Study, 2015 (TIMSS 2015) is a data collection that is part of the Trends in International Mathematics and Science Study (TIMSS) program; program data are available since 1999 at . TIMSS 2015 (https://nces.ed.gov/timss/) is a cross-sectional study that provides international comparative information of the mathematics and science literacy of fourth-, eighth-, and twelfth-grade students and examines factors that may be associated with the acquisition of math and science literacy in students. The study was conducted using direct assessments of students and questionnaires for students, teachers, and school administrators. Fourth-, eighth-, and twelfth-graders in the 2014-15 school year were sampled. Key statistics produced from TIMSS 2015 provide reliable and timely data on the mathematics and science achievement of U.S. students compared to that of students in other countries. Data are expected to be released in 2018.

This dataset contains derived statistical summaries comparing Grade 5 mathematics performance in South Africa and Grade 4 performance in Singapore using TIMSS 2023 data. It includes mean scale scores, cross-domain differences, and estimated effect sizes across content (numbers, measurement, geometry, data) and cognitive (knowing, applying, and reasoning) domains. Supplementary materials include variable definitions and extended methodological notes.

Historical Dataset of Milford School District is provided by PublicSchoolReview and contain statistics on metrics:Comparison of Diversity Score Trends,Total Revenues Trends,Total Expenditure Trends,Average Revenue Per Student Trends,Average Expenditure Per Student Trends,Reading and Language Arts Proficiency Trends,Math Proficiency Trends,Science Proficiency Trends,Graduation Rate Trends,Overall School District Rank Trends,Asian Student Percentage Comparison Over Years (1991-2023),Hispanic Student Percentage Comparison Over Years (1991-2023),Black Student Percentage Comparison Over Years (1991-2023),White Student Percentage Comparison Over Years (1991-2023),Two or More Races Student Percentage Comparison Over Years (2017-2023),Comparison of Students By Grade Trends

The Trends in International Mathematics and Science Study, 2011 (TIMSS 2011), is a study that is part of the Trends in International Mathematics and Science Study (TIMSS) program. TIMSS 2011 (https://nces.ed.gov/timss/) is a cross-sectional study that provides international comparative information of the mathematics and science literacy of fourth- and eighth-grade students and examines factors that may be associated with the acquisition of math and science literacy in students. The study was conducted using direct assessments of students and questionnaires for students, teachers, and school administrators. Fourth- and eighth-graders in the 2010-11 school year were sampled. The study's response rate was 94 percent. Key statistics produced from TIMSS 2003 are mathematics and science achievement scores of U.S. fourth- and eighth- grade students compared to that of students in other countries.

The purpose of this study was to compare the prevalence of math anxiety in Russian and Chinese schoolchildren across genders and ages. The Abbreviated Math Anxiety Scale (AMAS) was used as a measurement tool for assessing math anxiety. The factor structure of the AMAS and item invariance between Russian and Chinese schoolchildren were also examined. A total of 4,292 Russian (54% girls, M = 13.7, SD = 1.21) and 3,410 Chinese (48% girls, M = 12.7, SD = 1.21, Me = 13.0) schoolchildren participated in the study. The bi-factor model of the AMAS fits provided the best fit for the data in both countries. AMAS items demonstrated invariance between the two groups. Overall, Russian schoolchildren demonstrated higher math anxiety across all ages and math anxiety subscales, except at ages 14–15, where Chinese schoolchildren reported higher learning-related math anxiety. Among Chinese schoolchildren, both learning and evaluation math anxiety increased with age. Conversely, for Russian schoolchildren, math evaluation anxiety increased, while learning math anxiety decreased with age. Gender differences were observed in both countries, with the onset of gender-related differences appearing earlier in Chinese schoolchildren.

About the Math-Students Dataset

This dataset, originally sourced from the UCI Machine Learning Repository, offers a rich collection of data on student performance in a math program. It provides detailed insights into both the academic achievements and the socio-demographic backgrounds of the students, making it an excellent resource for educational data mining and predictive analytics.

Key Features & Attributes

• Demographics & Background:

  • School: Identifies the student's school (e.g., Gabriel Pereira or Mousinho da Silveira).
  • Sex & Age: Basic demographic information to help explore performance trends among different groups.
  • Address & Family Size: Details about the student’s home environment, including whether they live in an urban or rural area and their family size.

• Parental & Household Information:

  • Parental Cohabitation & Education: Data on whether parents live together and their education levels, which can correlate with student support and academic outcomes.
  • Parental Occupation: Information on the mother’s and father’s jobs, providing further context on socioeconomic factors.

• Educational & Behavioral Variables:

  • Study Time & Failures: Weekly study time and history of past class failures help gauge academic dedication and potential challenges.
  • Support & Extracurricular Activities: Records on whether the student has received extra educational support or participates in extracurricular activities, which can influence overall performance.
  • School-Related Factors: Travel time to school, attendance (absences), and participation in additional paid classes contribute to a holistic view of the educational environment.

• Lifestyle & Social Factors:

  • Internet Access, Free Time & Socializing: Variables like internet availability, free time, and how often students go out with friends help capture lifestyle and behavioral patterns.
  • Health & Well-being: Self-reported health status and alcohol consumption patterns during weekdays and weekends offer insights into personal well-being, which may impact academic performance.

• Academic Performance:

  • Grades: The dataset includes three key assessments—G1 (first period grade), G2 (second period grade), and G3 (final grade). G3, the final grade, serves as the primary target variable for predictive models.

Potential Applications

• Predictive Modeling:
  Researchers and data scientists can build regression models to predict final grades (G3) based on the numerous socio-demographic and educational features.
• Exploratory Data Analysis:
  The dataset is ideal for exploring relationships between family background, lifestyle choices, and academic success. For example, one could analyze how study time or parental education levels correlate with performance.
• Educational Interventions:
  By identifying key factors that contribute to academic outcomes, educators and policymakers can develop targeted interventions to support at-risk students.
• Comparative Studies:
  While this dataset focuses on math scores, its structure is similar to the Portuguese language course dataset. This similarity provides opportunities for cross-domain comparisons in educational research.

Additional Insights

• Data Complexity & Quality:
  Despite its moderate size, the dataset is rich in both categorical and numerical variables. This diversity requires careful preprocessing and feature engineering but also offers the chance to uncover complex interactions between various factors.
• Research Impact:
  The dataset has been widely used in the field of educational data mining. Its comprehensive nature has provided a basis for numerous studies examining the interplay between academic performance and a range of external factors.
• Historical Context:
  Originating from a study presented at the 5th FUBUTEC 2008 conference, the dataset has contributed valuable insights into secondary school performance and continues to serve as a benchmark for educational analytics research.

After the random assignment of eight intact classes to the treatment and comparison groups, a pretest was administered to both groups to examine students’ basic understanding of linear programming word problems. This was followed by an intervention involving a series of lessons on the application of active learning heuristic problem-solving strategies on students’ understanding of solving linear programming problems by graphical method. The entire intervention lasted for approximately three months from mid October 2020 to March 2021. At the end of the intervention, both groups of students (treatment and comparison) sat for a post-test to assess the effect of an intervention (ALHPS) for the treatment group and to compare and contrast students’ performance and attitude before and after, based on their demographic characteristics. All the test items (pretest and posttest) were scored by experts, converted into percentages, recorded in SPSS, and data were analyzed using the Statistical Package for Social Sciences (SPSS) version 26, with Hayes (2022) PROCESS macro. This provided the analysis for exploring the direct and indirect relationship between ALHPS and, students’ performance and attitude towards learning linear programming.

Airoboros LLMs Math Dataset

Mastering Complex Mathematical Operations in Machine Learning

By Huggingface Hub [source]

About this dataset

The Airoboros-3.1 dataset is the perfect tool to help machine learning models excel in the difficult realm of complicated mathematical operations. This data collection features thousands of conversations between machines and humans, formatted in ShareGPT to maximize optimization in an OS ecosystem. The dataset’s focus on advanced subjects like factorials, trigonometry, and larger numerical values will help drive machine learning models to the next level - facilitating critical acquisition of sophisticated mathematical skills that are essential for ML success. As AI technology advances at such a rapid pace, training neural networks to correspondingly move forward can be a daunting and complicated challenge - but with Airoboros-3.1’s powerful datasets designed around difficult mathematical operations it just became one step closer to achievable!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

To get started, download the dataset from Kaggle and use the train.csv file. This file contains over two thousand examples of conversations between ML models and humans which have been formatted using ShareGPT - fast and efficient OS ecosystem fine-tuning tools designed to help with understanding mathematical operations more easily. The file includes two columns: category and conversations, both of which are marked as strings in the data itself.

Once you have downloaded the train file you can begin setting up your own ML training environment by using any of your preferred frameworks or methods. Your model should focus on predicting what kind of mathematical operations will likely be involved in future conversations by referring back to previous dialogues within this dataset for reference (category column). You can also create your own test sets from this data, adding new conversation topics either by modifying existing rows or creating new ones entirely with conversation topics related to mathematics. Finally, compare your model’s results against other established models or algorithms that are already published online!

Happy training!

Research Ideas

• It can be used to build custom neural networks or machine learning algorithms that are specifically designed for complex mathematical operations.
• This data set can be used to teach and debug more general-purpose machine learning models to recognize large numbers, and intricate calculations within natural language processing (NLP).
• The Airoboros-3.1 dataset can also be utilized as a supervised learning task: models could learn from the conversations provided in the dataset how to respond correctly when presented with complex mathematical operations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv | Column name | Description | |:------------------|:-----------------------------------------------------------------------------| | category | The type of mathematical operation being discussed. (String) | | conversations | The conversations between the machine learning model and the human. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Huggingface Hub.

*Comparing those included (observed) to those excluded.

Dataset Description

This comprehensive dataset provides insights into women's participation in Science, Technology, Engineering, and Mathematics (STEM) education across major countries from 2000 to 2023. The data captures both enrollment and graduation patterns, offering a nuanced view of gender dynamics in STEM fields globally. This dataset explores the representation of women in STEM education globally over two decades. It includes data on female enrollment, graduation rates, and fields of study within STEM. Columns: Country, Year, Female Enrollment (%), Female Graduation Rate (%), STEM Fields (e.g., Engineering, Computer Science), Gender Gap Index.

Key Features:

• Temporal Coverage: 24-year span (2000-2023) showing educational trends over time
• Geographic Scope: Data from 6 major countries (USA, China, India, Germany, Canada, Australia)
• Field Diversity: Four core STEM disciplines (Engineering, Computer Science, Mathematics, Biology)
• Dual Metrics: Both enrollment percentages and graduation rates for comprehensive analysis
• Gender Gap Index: Quantitative measure of gender parity in each field

Research Applications:

• Gender equity analysis in STEM education
• Cross-country comparative studies
• Temporal trend analysis of women's STEM participation
• Policy impact assessment on gender inclusion initiatives
• Educational pipeline analysis from enrollment to graduation

File Information

women_in_stem.csv

Size: ~50KB | Records: 500+ entries Format: UTF-8 encoded CSV with headers

This is the main dataset file containing all research data with clean, analysis-ready format.

Column Descriptors

Data Quality Notes:

• All percentages are recorded as decimal values (e.g., 20.4 = 20.4%)
• Gender Gap Index calculated using standard methodology
• Missing values handled through verified data sources
• Temporal consistency maintained across all records

Provenance & Methodology

Data Collection:

• Primary Sources: National education ministries, UNESCO statistics, OECD education databases
• Collection Period: 2023-2024 compilation of historical records
• Verification: Cross-referenced with multiple governmental and international sources
• Quality Assurance: Data validated against published academic research

Note: This dataset represents a completed historical analysis. While no regular updates are scheduled, significant data corrections or methodology improvements may prompt occasional revisions.

Usage Examples & Starter Code

Quick Data Overview

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load the dataset
df = pd.read_csv('women_in_stem.csv')

# Basic information
print(f"Dataset shape: {df.shape}")
print(f"Countries included: {df['Country'].unique()}")
print(f"STEM fields covered: {df['STEM Fields'].unique()}")
print(f"Year range: {df['Year'].min()} - {df['Year'].max()}")

Trend Analysis by Country

# Analyze enrollment trends over time
plt.figure(figsize=(12, 8))
for country in df['Country'].unique():
  country_data = df[df['Country'] == country]
  yearly_avg = country_data.groupby('Year')['Female Enrollment (%)'].mean()
  plt.plot(yearly_avg.index, yearly_avg.values, marker='o', label=country)

plt.title('Women\'s STEM Enrollment Trends by Country')
plt.xlabel('Year')
plt.ylabel('Average Female Enrollment (%)')
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()

Field Comparison Analysis

# Compare gender gaps across STEM fields
field_analysis = df.groupby('STEM Fields').agg({
  'Female Enrollment (%)': 'mean',
  'Female Graduation Rate (%)': 'mean',
  'Gender Gap Index': 'mean'
}).round(2)

print("Average Metrics by STEM Field:")
print(field_analysis)

Research Questions This Dataset Can Answer

1. How has women's participation in STEM evolved globally over the past two decades?
2. Which countries show the most progress in gender equity in STEM education?...

Abstract Comparative studies are an interesting way to find similarities or differences between different educational contexts. In Latin America, there are some studies about the attitudes of math teachers, but they are not very abundant. This work is intended to contribute to this body of studies by comparing two contexts inserted in the educational systems of Spain and Colombia. The objective is to analyze the set of beliefs and attitudes towards Mathematics, that teachers of basic education report, with special interest on anxiety. It is based on the validation and contextualization of the questionnaire on beliefs and attitudes towards mathematics. The methodology of the Classical Test Theory was followed. A pilot test was applied to calibrate the instrument; It was then administered to a sample of 232 teachers in both countries. With the data obtained, a comparative analysis was carried out to identify similarities and differences between the groups. The results show epistemological similarities regarding the items related to Euclideanism. However, in terms of quasi-empiricism and constructivism, there are marked differences by country. In relation to anxiety, teachers’ responses differ depending on the geographical context. It highlights the higher level of anxiety of Spanish teachers compared to Colombians. It was proved that factors such as the educational level and recruitment type determine your anxiety level and your belief system. In the future, it would be useful to verify whether anxiety levels also affect teaching practice during the teaching of mathematics.

ObjectiveTo provide a practical guidance for the analysis of N-of-1 trials by comparing four commonly used models.MethodsThe four models, paired t-test, mixed effects model of difference, mixed effects model and meta-analysis of summary data were compared using a simulation study. The assumed 3-cycles and 4-cycles N-of-1 trials were set with sample sizes of 1, 3, 5, 10, 20 and 30 respectively under normally distributed assumption. The data were generated based on variance-covariance matrix under the assumption of (i) compound symmetry structure or first-order autoregressive structure, and (ii) no carryover effect or 20% carryover effect. Type I error, power, bias (mean error), and mean square error (MSE) of effect differences between two groups were used to evaluate the performance of the four models.ResultsThe results from the 3-cycles and 4-cycles N-of-1 trials were comparable with respect to type I error, power, bias and MSE. Paired t-test yielded type I error near to the nominal level, higher power, comparable bias and small MSE, whether there was carryover effect or not. Compared with paired t-test, mixed effects model produced similar size of type I error, smaller bias, but lower power and bigger MSE. Mixed effects model of difference and meta-analysis of summary data yielded type I error far from the nominal level, low power, and large bias and MSE irrespective of the presence or absence of carryover effect.ConclusionWe recommended paired t-test to be used for normally distributed data of N-of-1 trials because of its optimal statistical performance. In the presence of carryover effects, mixed effects model could be used as an alternative.

Optinalysis is a function that autoreflectively or isoreflectively compares the symmetry/asymmetry, similarity/dissimilarity, and identity/unidentity within one or between two mathematical structures as a mirror-like (optic-like) reflection of each other about a central point [1]. Automorphic or shape optinalysis analyzes autoreflective pairs of a mathematical structure by optinalysis. It is a method of symmetry/asymmetry and identity estimation [1]. Isomorphic or comparative optinalysis refers to the analysis of isoreflective pairs of mathematical structures by optinalysis. It is a method of similarity/dissimilarity and identity estimation [1].

Reference: [1] K.B. Abdullahi, Kabirian-based optinalysis: A conceptually grounded framework for symmetry/asymmetry, similarity/dissimilarity, and identity/unidentity estimations in mathematical structures and biological sequences, MethodsX 11 (2023) 102400. https://doi.org/10.1016/j.mex.2023.102400

The attached data include written codes for calculation of structure parameters for direct contact condensers and wet bulb temperature for air-fuel and oxy-fuel combustion.

Also included is cost analysis spread sheet.

Analysis of covariance (ANCOVA) is commonly used in the analysis of randomized clinical trials to adjust for baseline covariates and improve the precision of the treatment effect estimate. We derive the exact power formulas for testing a homogeneous treatment effect in superiority, noninferiority, and equivalence trials under both unstratified and stratified randomizations, and for testing the overall treatment effect and treatment × stratum interaction in the presence of heterogeneous treatment effects when the covariates excluding the intercept, treatment, and prestratification factors are normally distributed. These formulas also work very well for nonnormal covariates. The sample size methods based on the normal approximation or the asymptotic variance generally underestimate the required size. We adapt the recently developed noniterative and two-step sample size procedures to the above tests. Both methods take into account the nonnormality of the t statistic, and the lower order variance term commonly ignored in the sample size estimation. Numerical examples demonstrate the excellent performance of the proposed methods particularly in small samples. We revisit the topic on the prestratification versus post-stratification by comparing their relative efficiency and power. Supplementary materials for this article are available online.

Abstract

The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The purpose of the project is to improve understanding of the causes and consequences of childhood poverty and examine how policies affect children's well-being, in order to inform the development of future policy and to target child welfare interventions more effectively.

The objectives of the study are to provide good quality long-term data about the lives of children living in poverty, trace linkages between key policy changes and child welfare, and inform and respond to the needs of policymakers, planners and other stakeholders. Research activities of the project include the collection of data on a set of child welfare outcomes and their determinants and the monitoring of changes in policy, in order to explore the links between the policy environment and outcomes for children.

The study is being conducted in Ethiopia, India (in Andhra Pradesh), Peru and Vietnam. These countries were selected because they reflect a range of cultural, geographical and social contexts and experience differing issues facing the developing world; high debt burden, emergence from conflict, and vulnerability to environmental conditions such as drought and flood.

The Young Lives study aims to track the lives of 12,000 children over a 15-year period. This is the time-frame set by the UN to assess progress towards the Millennium Development Goals. Round 1 of the study followed 2,000 children (aged between 6 and 18 months in 2002) and their households, from both urban and rural communities, in each of the four countries (8,000 children in total). Data were also collected on an older cohort of 1,000 children aged 7 to 8 years in each country, in order to provide a basis for comparison with the younger children when they reach that age. Round 2 of the study returned to the same children who were aged 1-year-old in Round 1 when they were aged approximately 5-years-old, and to the children aged 8-years-old in Round 1 when they were approximately 12-years-old. Round 3 of the study returned to the same children again when they were aged 7 to 8 years (the same as the older cohort in Round 1) and 14 to 15 years. It is envisaged that subsequent survey waves will take place in 2013 and 2016. Thus the younger children are being tracked from infancy to their mid-teens and the older children through into adulthood, when some will become parents themselves.

Further information about the survey, including publications, can be downloaded from the Young Lives website. School Survey: A school survey was introduced into Young Lives in 2010, following the third round of the household survey, in order to capture detailed information about children’s experiences of schooling. It addressed two main research questions: • how do the relationships between poverty and child development manifest themselves and impact upon children's educational experiences and outcomes? • to what extent does children’s experience of school reinforce or compensate for disadvantage in terms of child development and poverty?

The survey allows researchers to link longitudinal information on household and child characteristics from the household survey with data on the schools attended by the Young Lives children and children's achievements inside and outside the school. A wide range of stakeholders, including government representatives at national and sub-national levels, NGOs and donor organisations were involved in the design of the school survey, so the researchers could be sure that the ‘right questions’ were being asked to address major policy concerns. This consultation process means that policymakers already understand the context and potential of the Young Lives research and are interested to utilise the data and analysis to inform their policy decisions. The survey provides policy-relevant information on the relationship between child development (and its determinants) and children’s experience of school, including access, quality and progression. This combination of household, child and school-level data over time constitutes the comparative advantage of the Young Lives study.

School Survey data are currently only available for India and Peru. The Peru data are available from the UK Data Archive under SN 7479.

Further information is available from the Young Lives School Survey webpages.

Geographic coverage

Andhra Pradesh

Analysis unit

Individuals; Institutions/organisations

Universe

Location of Units of Observation: Subnational

Population: Young Lives children, the school they attend, their head teachers and class teachers, in Andhra Pradesh, India, 2010-2011.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Procedures: Multi-stage stratified random sample Number of Units: 953 Young Lives children across 249 schools

Mode of data collection

Face-to-face interview; Self-completion; Educational measurements; Observation

Research instrument

The survey instruments included data collection at the school, class and pupil level, and involved the head teacher, class teacher, and pupil. The instruments comprises of the following components: • School roster • Child questionnaire answer sheet 1 • Child questionnaire answer sheet 2 • Child Maths test • Child Telugu test • Child English test • Child language learning experience • Child observation 1 (Maths) • Child observation 2 (Maths) • Child observation 3 (Maths) • Teacher questionnaire • Teacher content knowledge test (Maths) • Maths teacher observation 1 • Maths teacher observation 2 • Head teacher questionnaire • School observation • School observation - homework

This indicator evaluates whether a national or cross-national assessment of learning outcomes was conducted in the last 5 years in (a) reading, writing or language and (b) mathematics at the relevant stages of education. The indicator is expressed as a simple ‘yes’ or ‘no’ for each subject area and each stage of education. The implementation of a learning assessment in the reference period serves as an educational vital tool to assess progress. 'Yes’ values indicate that the country is monitoring learning outcomes regularly at the given stage of education and in the given subject areas. This will enable the country to review and adapt as necessary its national policies on education and learning to ensure that all children and young people have the opportunity to acquire basic skills at each education level and in each subject area. Data on the administration of a large-scale assessment from a national representative sample from national learning assessment offices, ministries of education or other bodies responsible for learning assessments, including regional or international organizations running learning assessments (e.g. Conférence des ministres de l'Éducation des États et gouvernements de la Francophonie (CONFEMEN), Educational Quality and Assessment Programme (EQAP), International Association for the Evaluation of Educational Achievement (IEA), Organisation for Economic Co-operation and Development (OECD), Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ) and Third Regional Comparative and Explanatory Study (TERCE). For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/