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

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.

Dataset

This dataset was created by Mira Küçük

Contents

This table contains 1044 series, with data for years 1994 - 1998 (not all combinations necessarily have data for all years), and was last released on 2007-01-29. This table contains data described by the following dimensions (Not all combinations are available): Geography (29 items: Austria; Belgium (French speaking); Canada; Belgium (Flemish speaking) ...) Sex (2 items: Males; Females ...) Age groups (3 items: 11 years; 15 years; 13 years ...) Student response (6 items: Much too thin; About the right size; A bit too fat; A bit too thin ...).

This dataset is about books. It has 226 rows and is filtered where the book subjects is Commercial statistics. It features 9 columns including author, publication date, language, and book publisher.

While open data concepts become more important in our society, education about its benefits and technical issues is still behind the practice. Students of STEM disciplines should be introduced to open data during their education. The Open Computing course, completely redesigned in the new Computing curriculum, introduces open data concepts, providing both the basics and advanced topics, from technical to social and legal viewpoints. Among the several educational activities, one was particularly useful for understanding the needs and implications of using open data: a synchronous group activity where students had to choose a societal issue, find and analyze two open datasets that would help gaining insight into this issue, assess interdisciplinarity approaches and stakeholders, and finally propose the added value emerging from the solution. In a short amount of time needed, this activity – which tackled multiple aspects of the problem - brought a clearer insight into the topic, building upon the conventional lectures. Students highly graded such an approach to their education, where they had to construct their knowledge by the group experience. A similar group activity appeared to be useful in the context of open data PhD training and might also be used in other disciplines and domains.

Dataset

This dataset was created by almaas izdihar

Contents

Third grade English Language Arts (ELA) and Math test results for the 2016-2017 school year by census tract for the state of Michigan. Data Driven Detroit obtained these datasets from MI School Data, for the State of the Detroit Child tool in July 2017. Test results were originally obtained on a school level and aggregated to census tract by Data Driven Detroit. Student data was suppressed when less than five students were tested per school.Click here for metadata (descriptions of the fields).

This dataset contains responses from 31 students enrolled in vocational schools in Indonesia, gathered through three distinct questionnaires measuring Computational Thinking Skills (CTS), Curiosity (CIAC - Children Images and Attitudes Curiosity), and Student Engagement in School (SES). The CTS data assesses students' problem-solving and logical reasoning abilities, while the CIAC data evaluates their levels of curiosity and attitudes toward learning. The SES data captures students' emotional, behavioral, and cognitive engagement with their school environment. This dataset offers valuable insights into how students perceive the quality of education in vocational schools, providing critical information for educational researchers, policymakers, and practitioners seeking to enhance learning experiences and student engagement in these institutions.

1. The number of full-time faculty in various departments of colleges and universities.2. The data is the same as that on the "Teaching 1-1. Number of Full-time Faculty - Statistics by Department (Institute)" and "Teaching 1-2. Number of Full-time Faculty - Statistics by School" of the college and university affairs information open platform.

The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.

Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.

This collection of datasets originates from the Statistics Center's service interface, known as Tilastokeskus (Statistics Finland), in Finland. The collection is composed of related data tables, with each table presenting a variety of related data in a structured format of columns and rows. The data in this collection is highly detailed and organized, providing a valuable resource for those seeking to understand specific statistical areas. The datasets in this collection are current as of 2024. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).

Analysis of ‘2019 Public Data File - Students’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/f7cc1ebe-7bdc-453a-959d-10c6147e27e9 on 30 September 2021.

--- Dataset description provided by original source is as follows ---

To collect feedback on their learning environment from families, students and teachers. Aids in facilitating the understanding of families perceptions, students, and teachers regarding their school. School leaders use feedback from the survey to reflect and make improvements to schools and programs. Each year all parents, teachers and students in grades 6-12 take the NYC School Survey. The survey is aligned to the DOE's Framework for Great Schools. It is designed to collect important information about each school's ability to support student success.

--- Original source retains full ownership of the source dataset ---

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

About this dataset

Context

The dataset tabulates the population of State College by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of State College across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 53.83% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the State College is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of State College total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for State College Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of New Point by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for New Point. The dataset can be utilized to understand the population distribution of New Point by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in New Point. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for New Point.

Key observations

Largest age group (population): Male # 60-64 years (26) | Female # 40-44 years (16). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the New Point population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the New Point is shown in the following column.
• Population (Female): The female population in the New Point is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in New Point for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Point Population by Gender. You can refer the same here

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_cbf3ca5a10b8c503733d4e644c536d52/view

About this dataset

Context

The dataset tabulates the population of Spring Lake Heights by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Spring Lake Heights. The dataset can be utilized to understand the population distribution of Spring Lake Heights by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Spring Lake Heights. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Spring Lake Heights.

Key observations

Largest age group (population): Male # 60-64 years (284) | Female # 60-64 years (320). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Spring Lake Heights population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Spring Lake Heights is shown in the following column.
• Population (Female): The female population in the Spring Lake Heights is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Spring Lake Heights for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Spring Lake Heights Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of New Hudson town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for New Hudson town. The dataset can be utilized to understand the population distribution of New Hudson town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in New Hudson town. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for New Hudson town.

Key observations

Largest age group (population): Male # 0-4 years (56) | Female # 0-4 years (50). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the New Hudson town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the New Hudson town is shown in the following column.
• Population (Female): The female population in the New Hudson town is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in New Hudson town for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Hudson town Population by Gender. You can refer the same here