This synthetic Student Performance Dataset is designed as an educational resource for data science, machine learning, and education analytics applications. The dataset provides detailed information on various factors influencing students’ academic performance, including demographics, family background, extracurricular activities, and study habits. It aims to help users analyze relationships between these factors and students’ grades, providing insights into student success and well-being.

Dataset Features:

• Gender: Gender of the student (e.g., "Male," "Female").
• Age: Age of the student (in years).
• Family Size: Size of the student’s family.
• Parental Status (Together/Apart): Whether the parents are living together or apart.
• Mother's Education Level: Education level of the student’s mother.
• Father's Education Level: Education level of the student’s father.
• Mother's Job: Occupation of the student’s mother.
• Father's Job: Occupation of the student’s father.
• Reason for Choosing School: Primary reason for selecting the school (e.g., proximity, reputation).
• Legal Guardian: Legal guardian of the student (e.g., "Mother," "Father," "Other").
• Travel Time to School (in hours): Daily travel time between home and school.
• Weekly Study Time (in hours): Hours spent studying outside school per week.
• Number of Past Failures: Number of previously failed subjects.
• Extra Educational Support: Whether the student receives additional educational support (e.g., "Yes," "No").
• Family Educational Support: Whether the family provides educational support (e.g., "Yes," "No").
• Paid Extra Classes: Whether the student takes extra paid classes (e.g., "Yes," "No").
• Extracurricular Activities: Participation in extracurricular activities (e.g., "Yes," "No").
• Attended Nursery School: Whether the student attended nursery school (e.g., "Yes," "No").
• Aspiration for Higher Education: Whether the student aspires to pursue higher education (e.g., "Yes," "No").
• Internet Access at Home: Availability of internet access at home (e.g., "Yes," "No").
• In a Romantic Relationship: Whether the student is in a romantic relationship (e.g., "Yes," "No").
• Quality of Family Relationships: Rated quality of relationships within the family.
• Free Time After School: Amount of free time available after school hours.
• Going Out with Friends: Frequency of going out with friends.
• Workday Alcohol Consumption: Level of alcohol consumption during workdays.
• Weekend Alcohol Consumption: Level of alcohol consumption during weekends.
• Current Health Status: Self-reported health status of the student.
• Number of School Absences: Total number of school days missed.
• First Period Grade: Grade received during the first grading period.
• Second Period Grade: Grade received during the second grading period.
• Final Grade: Final grade achieved by the student.

Distribution:

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Usage:

This dataset is useful for a variety of applications, including:

• Student Performance Analysis: To explore relationships between family background, study habits, and academic outcomes.
• Educational Research: To identify key factors influencing student success and well-being.
• Predictive Modeling: To build models that predict student grades or identify students at risk of underperforming.
• Policy Making: To analyze how socioeconomic factors and family structure impact education outcomes.

Coverage:

This dataset is synthetic and anonymized, ensuring that it is safe for experimentation and learning without compromising any real student data.

License:

CCO (Public Domain)

Who can use it:

Data science learners: For practising data manipulation, visualization, and predictive modelling. Educators and researchers: For academic studies or teaching purposes in student analytics and education research. Education professionals: For analyzing factors that influence student success and tailoring interventions to improve outcomes.

In the fall of 2022, about 81,360 students who were enrolled exclusively in distance education courses in postsecondary institutions were located outside of the United States. This is compared to around 3.13 million students who were located in the same state as the institution, but enrolled in exclusively distance education courses. This high level of enrollment in distance learning courses is due to the impact of the COVID-19 pandemic.

At a time when OECD and partner countries are trying to figure out how to reduce burgeoning debt and make the most of shrinking public budgets, spending on education is an obvious target for scrutiny. Education officials, teachers, policy makers, parents and students struggle to determine the merits of shorter or longer school days or school years, how much time should be allotted to various subjects, and the usefulness of after-school lessons and independent study. This report focuses on how students use learning time, both in and out of school. What are the ideal conditions to ensure that students use their learning time efficiently? What can schools do to maximise the learning that occurs during the limited amount of time students spend in class? In what kinds of lessons does learning time reap the most benefits? And how can this be determined? The report draws on data from the 2006 cycle of the Programme of International Student Assessment (PISA) to describe differences across and within countries in how much time students spend studying different subjects, how much time they spend in different types of learning activities, how they allocate their learning time and how they perform academically.

This statistic shows the percentage of students identifying as first-generation in the United States in 2016, by gender and ethnicity. As of 2016, about ** percent of the first-generation American students, broken down by gender, were female. Almost ** percent of the first-generation students identified themselves as Native Americans in the United States in 2016.

The dataset contains academic year wise compiled data on the complete profile of the United States of America (USA) Students who have enrolled abroad for pursuing different studies. The specifics of data contained include number of students by gender, race, programme and fields of study

The number of international bachelor's and master's students in the Netherlands increased significantly between 2006 and 2022. In 2006, there were ****** international students enrolled in the Netherlands, whereas by 2022, this figure had more than tripled to ******* international students.

Students

## Overview

Students is a dataset for object detection tasks - it contains Behavior annotations for 205 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset tracks annual total students amount from 2005 to 2023 for Student Leadership Academy

The purpose of the study: to provide impartial information for the school, its students, and their parents (caregivers, foster parents) about the achievements to make decisions on the further improvements of teaching and studying on student, teacher, class, school, municipality, and national level. The objectives of National Survey of Student Achievement (NASA): to collect the information for monitoring the national students’ achievements, planning the novelties, and implementing the novelties for monitoring the success; to evaluate the educational content, and substantiating students’ achievement criteria based on collected data; to prepare the necessary tools (i.e., standardized tests, etc.) for students and teachers for the impartial evaluation of their work results; to prepare the necessary tools (i.e., standardized tests, etc.) for the municipality’s education subdivisions and school principals for collecting the required data of work result assessments and planning of activities. National Survey of Student Achievement, first implemented in 2002, became the responsibility of the Education Supply Centre. Due to economic reasons, the assessments were not provided from 2009 to 2011. In 2012, the renewed assessment implementation was consigned to the National Examination Centre. Since the 2nd of September, 2019, the National Agency of Education took over the activities of the National Examination Centre and continues to carry them on to this day. During the 2012 National Assessments of Student Achievements, grade 8 students received notebooks with 9 types of tests. To pinpoint the personal peculiarities as well as home, class, and school context, etc., the student questionnaires were used for the research of educational context. One student got to fill out only one notebook which consisted of tests from two different subjects and a student questionnaire. The questionnaires provided in different types of notebooks consisted of general questions and a questionnaire from one or two objective fields. One line in SPSS Statistics from the 2012 National Survey of Student Achievement coincides with the achievements or questionnaire answers of one particular student or a teacher. The information provided in databases is impersonal - a student or a teacher is identified based on code, without providing the class or school’s name. Each school that has participated in the 2012 National Survey of Student Achievement received a unique five-number school code. The code used for identifying the schools of both grade 4 and grade 8 students and teachers consists of a school code and the numbers identifying a class and a student. The class code in the student’s database coincides with the code in the teacher’s database. To connect these databases, the variable named “ID_klase” would have to be used as an identifier. Dataset "NSSA 2012: 8th Grade Students Study, 2012" metadata and data were prepared implementing project "Disparities in School Achievement from a Person and Variable-Oriented Perspective: A Prototype of a Learning Analytics Tool NO-GAP" from 2020 to 2023. Project leader is chief research fellow Rasa Erentaitė. Project is funded by the European Regional Development Fund according to the 2014–2020 Operational Programme for the European Union Funds’ Investments, under measure’s No. 01.2.2-LMT-K-718 activity “Research Projects Implemented by World-class Researcher Groups to develop R&D activities relevant to economic sectors, which could later be commercialized” under a grant agreement with the Lithuanian Research Council (LMTLT).

There were approximately 18.58 million college students in the U.S. in 2022, with around 13.49 million enrolled in public colleges and a further 5.09 million students enrolled in private colleges. The figures are projected to remain relatively constant over the next few years.

What is the most expensive college in the U.S.? The overall number of higher education institutions in the U.S. totals around 4,000, and California is the state with the most. One important factor that students – and their parents – must consider before choosing a college is cost. With annual expenses totaling almost 78,000 U.S. dollars, Harvey Mudd College in California was the most expensive college for the 2021-2022 academic year. There are three major costs of college: tuition, room, and board. The difference in on-campus and off-campus accommodation costs is often negligible, but they can change greatly depending on the college town.

The differences between public and private colleges Public colleges, also called state colleges, are mostly funded by state governments. Private colleges, on the other hand, are not funded by the government but by private donors and endowments. Typically, private institutions are  much more expensive. Public colleges tend to offer different tuition fees for students based on whether they live in-state or out-of-state, while private colleges have the same tuition cost for every student.

ABSTRACT Introduction The isolation policy caused by COVID-19 is plaguing physical exercise behavior, which seems to affect college students’ physical and mental health. Objective Understand the current situation of college students’ exercise behavior during COVID-19, analyzing the physical and mental health status to provide policy guidance on formulating appropriate exercise behavior for college students in the context of the epidemic. Methods 250 students from 20 colleges and universities in China were randomly selected as observation volunteers. The adherents’ exercise-related behavior and physical and mental health were observed and analyzed by questionnaire, and subsequently evaluated according to statistical methods. Results The results showed that exercise motivation, exercise frequency, exercise duration, and exercise items of the surveyed individuals affected the physical and mental health of college students; these effects were statistically significant (p

Colleges and universities in the United States are still a popular study destination for Chinese students, with around 277 thousand choosing to take courses there in the 2023/24 academic year. Although numbers were heavily affected by the coronavirus pandemic, China is still the leading source of international students in the U.S. education market, accounting for 24.6 percent of all incoming students. The education exodus Mathematics and computer science courses led the field in terms of what Chinese students were studying in the United States, followed by engineering and business & management programs. The vast majority of Chinese students were self-funded, wth the remainder receiving state-funding to complete their overseas studies. Tuition fees can run into the tens of thousands of U.S. dollars, as foreign students usually pay out-of-state tuition fees. What about the local situation? Although studying abroad attracts many Chinese students, the country itself boasts the largest state-run education system in the world. With modernization of the national tertiary education system being a top priority for the Chinese government, the country has seen a significant increase in the number of local universities over the last decade. Enrolments in these universities exceeded 37 million in 2023, and a record of more than ten million students graduated in the same year, indicating that China's education market is still expanding.

This dataset tracks annual distribution of students across grade levels in Gateway Student Support Center

Historical Dataset of Student Empowerment Academy is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2007-2023),Total Classroom Teachers Trends Over Years (2008-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2008-2023),Hispanic Student Percentage Comparison Over Years (2007-2016),Black Student Percentage Comparison Over Years (2007-2016),Diversity Score Comparison Over Years (2007-2016),Free Lunch Eligibility Comparison Over Years (2008-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2008-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2016),Math Proficiency Comparison Over Years (2010-2016),Overall School Rank Trends Over Years (2010-2016),Graduation Rate Comparison Over Years (2011-2016)

While enrolment in tertiary education has increased dramatically over the past decades, many university-aged students do not enrol, nor do they expect to earn a university degree. While it is important to promote high expectations for further education, it is equally important to ensure that students’ expectations are well-aligned with their actual abilities. Grade Expectations: How Marks and Education Policies Shape Students' Ambitions reveals some of the factors that influence students’ thinking about further education. The report also suggests what teachers and education policy makers can do to ensure that more students have the skills, as well as the motivation, to succeed in higher education. In 2009, students in 21 PISA-participating countries and economies were asked about their expected educational attainment. An analysis of PISA data finds that students who expect to earn a university degree show significantly better performance in math and reading when compared to students who do not expect to earn such a university degree. However, performance is only one of the factors that determine expectations. On average across most countries and economies, girls and socio-economically advantaged students tend to hold more ambitious expectations than boys and disadvantaged students who perform just as well; and students with higher school marks are more likely to expect to earn a university degree – regardless of what those marks really measure.

This dataset tracks annual completion rates for first-time of full-time students from 2008 to 2024 for College of DuPage vs. Illinois

Proportion of students, aged 15 to 29, who were also working, by age group and type of institution attended, Canada and provinces. This table is included in Section E: Transitions and outcomes: Transitions to the labour market of the Pan Canadian Education Indicators Program (PCEIP). PCEIP draws from a wide variety of data sources to provide information on the school-age population, elementary, secondary and postsecondary education, transitions, and labour market outcomes. The program presents indicators for all of Canada, the provinces, the territories, as well as selected international comparisons and comparisons over time. PCEIP is an ongoing initiative of the Canadian Education Statistics Council, a partnership between Statistics Canada and the Council of Ministers of Education, Canada that provides a set of statistical measures on education systems in Canada.

High School Heights Dataset

You will find three datasets containing heights of the high school students.

All heights are in inches.

The data is simulated. The heights are generated from a normal distribution with different sets of mean and standard deviation for boys and girls.

There are 500 measurements for each gender.

Here are the datasets:

• hs_heights.csv: contains a single column with heights for all boys and girls. There's no way to tell which of the values are for boys and which ones are for girls.

• hs_heights_pair.csv: has two columns. The first column has boy's heights. The second column contains girl's heights.

• hs_heights_flag.csv: has two columns. The first column has the flag is_girl. The second column contains a girl's height if the flag is 1. Otherwise, it contains a boy's height.

To see how I generated this dataset, check this out: https://github.com/ysk125103/datascience101/tree/main/datasets/high_school_heights

Image by Gillian Callison from Pixabay

This scatter chart displays total students (people) against international students (people) in New York. The data is about universities.

This dataset presents data derived from the Public School Information System (PSIS) regarding the number of children enrolled in Pre-Kindergarten programs that are funded by their respective School district. The Connecticut State Department of Education collects information for this system on a school year basis.