Student Performance Data

This dataset provides insights into various factors influencing the academic performance of students. It is curated for use in educational research, data analytics projects, and predictive modeling. The data reflects a combination of personal, familial, and academic-related variables gathered through observation or survey.

The dataset includes a diverse range of students and captures key characteristics such as study habits, family background, school attendance, and overall performance. It is well-suited for exploring correlations, visualizing trends, and training machine learning models related to academic outcomes.

Highlights:

Clean, structured format suitable for immediate use Designed for beginner to intermediate-level data analysis Valuable for classification, regression, and data storytelling projects

File Format:

Type: CSV (Comma-Separated Values) Encoding: UTF-8 Structure: Each row represents a student record

Applications

Student performance prediction Educational policy planning Identification of performance gaps and influencing factors Exploratory data analysis and visualization

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.

Student Performance Dataset 2024 Overview This dataset comprises detailed information about high school students in China, collected from various universities and schools. It is designed to analyze the factors influencing student performance, well-being, and engagement. The data includes a wide range of features such as demographic details, academic performance, health status, parental support, and more. The participating institutions include prominent universities such as Tsinghua University, Peking University, Fudan University, Shanghai Jiao Tong University, and Zhejiang University.

Dataset Description Features Student ID: Unique identifier for each student. Gender: Gender of the student (Male/Female). Age: Age of the student. Grade Level: The grade level of the student (e.g., 9, 10, 11, 12). Attendance Rate: The percentage of days the student attended school. Study Hours: Average number of hours the student spends studying daily. Parental Education Level: The highest level of education attained by the student's parents. Parental Involvement: The level of parental involvement in the student's education (High, Medium, Low). Extracurricular Activities: Whether the student participates in extracurricular activities (Yes/No). Socioeconomic Status: Socioeconomic status of the student's family (High, Medium, Low). Previous Academic Performance: Previous academic performance level (High, Medium, Low). Class Participation: The level of participation in class (High, Medium, Low). Health Status: General health status of the student (Good, Average, Poor). Access to Learning Resources: Whether the student has access to necessary learning resources (Yes/No). Internet Access: Whether the student has access to the internet (Yes/No). Learning Style: Preferred learning style of the student (Visual, Auditory, Kinesthetic). Teacher-Student Relationship: Quality of the relationship between the student and teachers (Positive, Neutral, Negative). Peer Influence: Influence of peers on the student's behavior and performance (Positive, Neutral, Negative). Motivation Level: Student's level of motivation (High, Medium, Low). Hours of Sleep: Average number of hours the student sleeps per night. Diet Quality: Quality of the student's diet (Good, Average, Poor). Transportation Mode: Mode of transportation used by the student to commute to school (Bus, Car, Walk, Bike). School Type: Type of school attended by the student (Public, Private). School Location: Location of the school (Urban, Rural). Homework Completion Rate: The rate at which the student completes homework assignments. Reading Proficiency: Proficiency level in reading. Math Proficiency: Proficiency level in mathematics. Science Proficiency: Proficiency level in science. Language Proficiency: Proficiency level in language. Physical Activity Level: The level of physical activity (High, Medium, Low). Screen Time: Average daily screen time in hours. Bullying Incidents: Number of bullying incidents the student has experienced. Special Education Services: Whether the student receives special education services (Yes/No). Counseling Services: Whether the student receives counseling services (Yes/No). Learning Disabilities: Whether the student has any learning disabilities (Yes/No). Behavioral Issues: Whether the student has any behavioral issues (Yes/No). Attendance of Tutoring Sessions: Whether the student attends tutoring sessions (Yes/No). School Climate: Overall perception of the school's environment (Positive, Neutral, Negative). Parental Employment Status: Employment status of the student's parents (Employed, Unemployed). Household Size: Number of people living in the student's household. Performance Score: Overall performance score of the student (Low, Medium, High).

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.

In the fall of 2022, about ****** students who were enrolled exclusively in distance education courses in postsecondary institutions were located outside of the United States. This is compared to around **** 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.

In this project, we aim to analyze and gain insights into the performance of students based on various factors that influence their academic achievements. We have collected data related to students' demographic information, family background, and their exam scores in different subjects.

**********Key Objectives:*********

1. Performance Evaluation: Evaluate and understand the academic performance of students by analyzing their scores in various subjects.

2. Identifying Underlying Factors: Investigate factors that might contribute to variations in student performance, such as parental education, family size, and student attendance.

3. Visualizing Insights: Create data visualizations to present the findings effectively and intuitively.

Dataset Details:

• The dataset used in this analysis contains information about students, including their age, gender, parental education, lunch type, and test scores in subjects like mathematics, reading, and writing.

Analysis Highlights:

• We will perform a comprehensive analysis of the dataset, including data cleaning, exploration, and visualization to gain insights into various aspects of student performance.

• By employing statistical methods and machine learning techniques, we will determine the significant factors that affect student performance.

Why This Matters:

Understanding the factors that influence student performance is crucial for educators, policymakers, and parents. This analysis can help in making informed decisions to improve educational outcomes and provide support where it is most needed.

Acknowledgments:

We would like to express our gratitude to [mention any data sources or collaborators] for making this dataset available.

Please Note:

This project is meant for educational and analytical purposes. The dataset used is fictitious and does not represent any specific educational institution or individuals.

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

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.

The dataset contains Academic-year-wise historically compiled data on the number of Indian students enrolled in United States of America (USA) for pursuing different studies

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

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

From 2013 to 2014, the number of university students in Denmark increased by around 5,000, reaching nearly 162,000 students. However, it decreased slightly in the following years, dropping below 144,000 in 2023. Both among students enrolled at bachelor's and master's courses, there were more female than male students.

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

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

In the fall semester of 2023, 383,000 students were registered in universities and other higher education institutions in Sweden. The number of students remained relatively stable until 2018, and rose sharply in 2020 as many chose to take up studies during the COVID-19 pandemic. Around 60 percent of the higher education students in Sweden are women. Financial aid for studies Sweden has a long tradition of state financial aid for students. Swedish students can apply for both student grants and loans at a low interest rate, or apply only for subsidies but no loans. The financial aid for students is managed by the Swedish Board of Student Finance (CSN). In 2021, more than 207,000 students in Sweden received both subsidies and loans. Moreover, there are no tuition fees at universities and high schools in Sweden. Stockholm largest university The Stockholm University had the highest number of registered students in Sweden in 2022, followed by the Uppsala University and the University of Gothenburg. Stockholm University is ranked among the world’s top 100 universities and located in the Swedish capital, which is also the largest city in Sweden.

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.

In 2023, roughly ** percent of surveyed college students in the United States said they would be using their savings to finance their return to the classroom. Approximately **** of those surveyed said they intended to use student finance plans.

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

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).