This statistic shows the number of university and college students majoring in mathematics and statistics in Taiwan in school year 2024, by degree program. That school year, around *** students in Taiwan were enrolled in Ph.D. programs in the field of mathematics and statistics.

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.

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.

This dataset includes information related to the grades in two sections (the control section, CS; and the treatment section, TS) of the course Mathematics 2 from the second quadrimester (Q2) of 2024 of the first year of the degree in Economics at the University of A Coruña (UDC). Students in CS chose their seats freely, while in the TS, students were subsequently assigned to those seats closest to the instructor. The distances from each student to the professor are also included. Besides, this dataset contains the grades of both the CS and TS in the courses: Mathematics 1 (Q1), Economic History (Q1), Statistics 1 (Q2), Principles of Macroeconomics (Q2), Business Economics (Q2), all of them from the first year of the degree in Economics at UDC.

Question Paper Solutions of chapter Applied Statistics of Mathematics - II A, 2nd Semester , Bachelor of Technology

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for computer science and applied mathematics and statistics in the U.S.

This paper presents a study of the history of the Mathematics major of the Federal University of Mato Grosso do Sul (UFMS). First, we discuss the creation and further federalization of the University and the training course of Mathematics teachers in its various campuses. Secondly, we discuss specifically the Mathematics major of the Institute of Mathematics, located in Campo Grande, having as the main source curricular changes during 30 years time and a survey of the amount of its students in this period, as well as their professional performance. For this, we considered various documentary sources and responses to questionnaires completed by former students. The results show that it is necessary to rethink the initial training in view of the need for teachers to work in basic education.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for applied mathematics and statistics, economics in the U.S.

Question Paper Solutions of chapter Basic Statistics of Mathematics - II A, 2nd Semester , Bachelor of Technology

This view filters on US Census variables for field of bachelor degree reported for Mesa and other local comparison city's and Maricopa County. The American Community Survey (ACS) 1-year Estimates are source of information. For grouping of Majors Into Broad and Detailed Fields, see Appendix A at https://www2.census.gov/library/publications/2012/acs/acs-18.pdf

Science and Engineering include: Computers, mathematics, and statistics Biological, agricultural, and environmental sciences Physical and related science Psychology Social sciences Engineering Multidisciplinary studies

Science and engineering related includes nursing, architecture, math teacher

Arts, Humanities and other include: Literature and languages Liberal arts and history Visual and performing arts Communications

In 2021, * percent of graduates from tertiary institutions in Morocco had pursued subjects in natural sciences, mathematics, and statistics. This share was a decrease from the previous year, when ***** percent was registered. The highest percentage of students who graduated from this program in the given period was achieved in 2015.

Singapore Graduates from Higher Degree Courses: Natural, Physic & Math Science data was reported at 641.000 Person in 2017. This records an increase from the previous number of 618.000 Person for 2016. Singapore Graduates from Higher Degree Courses: Natural, Physic & Math Science data is updated yearly, averaging 269.000 Person from Dec 1993 (Median) to 2017, with 25 observations. The data reached an all-time high of 641.000 Person in 2017 and a record low of 47.000 Person in 1994. Singapore Graduates from Higher Degree Courses: Natural, Physic & Math Science data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.G073: Education Statistics: Graduates from Educational Institutions.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for economins and applied mathematics and statistics in the U.S.

This page provides information about award recipients, project abstracts, and summaries of competitions under the Upward Bound Math-Science Program.

Singapore Enrolment In Higher Degree Courses: Natural, Physic & Math Sciences data was reported at 2,205.000 Number in 2017. This records an increase from the previous number of 2,182.000 Number for 2016. Singapore Enrolment In Higher Degree Courses: Natural, Physic & Math Sciences data is updated yearly, averaging 1,467.000 Number from Dec 1993 (Median) to 2017, with 25 observations. The data reached an all-time high of 2,793.000 Number in 2011 and a record low of 298.000 Number in 1993. Singapore Enrolment In Higher Degree Courses: Natural, Physic & Math Sciences data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.G070: Education Statistics: Enrolment.

This statistic shows the activity of first-degree, full-time graduates of mathematical sciences who graduated at the end of academic year 2017/18 in the United Kingdom (UK). Of those graduates, ** percent were in full time employment in the UK, compared with * percent who were unemployed.

Singapore Graduates from University 1st Degree: Males: Natural,Physic, Math Sc data was reported at 755.000 Person in 2016. This records a decrease from the previous number of 790.000 Person for 2015. Singapore Graduates from University 1st Degree: Males: Natural,Physic, Math Sc data is updated yearly, averaging 432.000 Person from Dec 1993 (Median) to 2016, with 24 observations. The data reached an all-time high of 870.000 Person in 2013 and a record low of 276.000 Person in 2002. Singapore Graduates from University 1st Degree: Males: Natural,Physic, Math Sc data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.G064: Education Statistics: Graduates from Educational Institutions.

Question Paper Solutions of chapter Data of Basic Mathematics & Statistics, 2nd Semester , Bachelor in Business Administration (Hons.) 2023-2024

This statistic shows the percentage of students in the United States enrolled in life science, physical science, computing and mathematics and statistics subjects in 2014. Over * percent of students were enrolled in computing degree programs. In 2014, there were a total of ********** students in the United States, of whom ********* were science, mathematics and computing students.

Building strong quantitative skills prepares undergraduate biology students for successful careers in science and medicine. While math and statistics anxiety can negatively impact student learning within biology classrooms, instructors may reduce this anxiety by steadily building student competency in quantitative reasoning through instructional scaffolding, application-based approaches, and simple computer program interfaces. However, few statistical programs exist that meet all needs of an inclusive, inquiry-based laboratory course. These needs include an open-source program, a simple interface, little required background knowledge in statistics for student users, and customizability to minimize cognitive load, align with course learning outcomes, and create desirable difficulty. To address these needs, we used the Shiny package in R to develop a custom statistical analysis application. Our “BioStats” app provides students with scaffolded learning experiences in applied statistics that promotes student agency and is customizable by the instructor. It introduces students to the strengths of the R interface, while eliminating the need for complex coding in the R programming language. It also prioritizes practical implementation of statistical analyses over learning statistical theory. To our knowledge, this is the first statistics teaching tool where students are presented basic statistics initially, more complex analyses as they advance, and includes an option to learn R statistical coding. The BioStats app interface yields a simplified introduction to applied statistics that is adaptable to many biology laboratory courses.

Primary Image: Singing Junco. A sketch of a junco singing on a pine tree branch, created by the lead author of this paper.