https://www.insidehighered.com/sites/default/files/2024-02/GettyImages-1072191138.jpg" alt="image"> The dataset includes:

Gender: Useful for analyzing performance differences between male and female students.

Race/Ethnicity: Allows analysis of academic performance trends across different racial or ethnic groups.

Parental Level of Education: Indicates the educational background of the student's family.

Lunch: Shows whether students receive a free or reduced lunch, which is often a socioeconomic indicator.

Test Preparation Course: This tells whether students completed a test prep course, which could impact their performance.

Math Score: Provides a measure of each student’s performance in math, used to calculate averages or trends across various demographics.

Reading Score: Measures performance in reading, allowing for insights into literacy and comprehension levels among students.

Writing Score: Evaluates students' writing skills, which can be analyzed to assess overall literacy and expression.

This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details).

Attributes for both Maths.csv (Math course) and Portuguese.csv (Portuguese language course) datasets:

These grades are related with the course subject, Math or Portuguese:

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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.

Description: This repository contains the datasets used as part of the OC2 lab's work on Student Performance prediction and student engagement prediction in eLearning environments using machine learning methods.

Description

This dataset provides a comprehensive overview of various factors affecting student performance in exams. It includes information on study habits, attendance, parental involvement, and other aspects influencing academic success.

Column Descriptions

This dataset includes the attendance rate for public school students PK-12 by student group and by district during the 2021-2022 school year. Student groups include: Students experiencing homelessness Students with disabilities Students who qualify for free/reduced lunch English learners All high needs students Non-high needs students Students by race/ethnicity (Hispanic/Latino of any race, Black or African American, White, All other races) Attendance rates are provided for each student group by district and for the state. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

In "Sample Student Data", there are 6 sheets. There are three sheets with sample datasets, one for each of the three different exercise protocols described (CrP Sample Dataset, Glycolytic Dataset, Oxidative Dataset). Additionally, there are three sheets with sample graphs created using one of the three datasets (CrP Sample Graph, Glycolytic Graph, Oxidative Graph). Each dataset and graph pairs are from different subjects. · CrP Sample Dataset and CrP Sample Graph: This is an example of a dataset and graph created from an exercise protocol designed to stress the creatine phosphate system. Here, the subject was a track and field athlete who threw the shot put for the DeSales University track team. The NIRS monitor was placed on the right triceps muscle, and the student threw the shot put six times with a minute rest in between throws. Data was collected telemetrically by the NIRS device and then downloaded after the student had completed the protocol. · Glycolytic Dataset and Glycolytic Graph: This is an example of a dataset and graph created from an exercise protocol designed to stress the glycolytic energy system. In this example, the subject performed continuous squat jumps for 30 seconds, followed by a 90 second rest period, for a total of three exercise bouts. The NIRS monitor was place on the left gastrocnemius muscle. Here again, data was collected telemetrically by the NIRS device and then downloaded after he had completed the protocol. · Oxidative Dataset and Oxidative Graph: In this example, the dataset and graph are from an exercise protocol designed to stress the oxidative system. Here, the student held a sustained, light-intensity, isometric biceps contraction (pushing against a table). The NIRS monitor was attached to the left biceps muscle belly. Here, data was collected by a student observing the SmO2 values displayed on a secondary device; specifically, a smartphone with the IPSensorMan APP displaying data. The recorder student observed and recorded the data on an Excel Spreadsheet, and marked the times that exercise began and ended on the Spreadsheet.

This dataset has been collected to support research on predicting the academic performance of Secondary School Certificate (SSC) and Higher Secondary Certificate (HSC) students in Bangladesh. It comprises responses from many students across various institutions in the country.

The dataset includes a diverse set of features that are believed to influence academic outcomes. These features cover a wide range of domains such as:

Demographic Information: Age, gender, parental education, and occupation.

Academic History: Previous grades, subject preferences, study time, tutoring, etc.

Socioeconomic Factors: Family income, number of siblings, living location (urban/rural).

Institutional Factors: Type of school/college (public/private), distance from home, teacher-student ratio, etc.

Lifestyle and Behavioral Aspects: Sleep habits, screen time, daily routines, mental health indicators, and parental support.

The dataset is labeled with the actual academic performance (grades or GPA) of students in SSC and HSC examinations. The goal is to facilitate the development of predictive models and interpretability studies, with a focus on early intervention and academic counseling.

The dataset is anonymized and free from personally identifiable information. It is intended for academic research, education policy analysis, and machine learning experimentation.

if you use the dataset, please cite "A. A. Maruf, R. Ara Rumy, R. I. Sony and Z. Aung, "Predictive Analysis of Bangladeshi Students’ Academic Performances Using Ensemble Machine Learning with Explainable AI Techniques," 2024 27th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2024, pp. 1200-1205, doi: 10.1109/ICCIT64611.2024.11021990."

This dataset is crafted for beginners to practice data cleaning and preprocessing techniques in machine learning. It contains 157 rows of student admission records, including duplicate rows, missing values, and some data inconsistencies (e.g., outliers, unrealistic values). It’s ideal for practicing common data preparation steps before applying machine learning algorithms.

The dataset simulates a university admission record system, where each student’s admission profile includes test scores, high school percentages, and admission status. The data contains realistic flaws often encountered in raw data, offering hands-on experience in data wrangling.

The dataset contains the following columns:

Name: Student's first name (Pakistani names). Age: Age of the student (some outliers and missing values). Gender: Gender (Male/Female). Admission Test Score: Score obtained in the admission test (includes outliers and missing values). High School Percentage: Student's high school final score percentage (includes outliers and missing values). City: City of residence in Pakistan. Admission Status: Whether the student was accepted or rejected.

Student Admission Dataset

Description: The "Student Admission Dataset" is a simulated dataset that provides information related to student admissions to an educational institution. It includes a variety of data points relevant to the admission process and aims to serve as a sample dataset for educational and analysis purposes.

Dataset Description:

1. GPA (Grade Point Average): This continuous variable represents the student's GPA on a scale ranging from 2.5 to 4.0, reflecting their academic performance.

2. SAT Score (Scholastic Assessment Test): This continuous variable represents the student's SAT score, an important standardized test used in college admissions in the United States. SAT scores are measured on a scale between 900 and 1600.

3. Extracurricular Activities: This continuous variable indicates the number of extracurricular activities or involvements a student is engaged in outside of their academic commitments. It provides insights into a student's overall engagement and interests.

4. Admission Status: This categorical variable represents the outcome of the admission process for each student. It includes three categories:

   • "Accepted": Students who have been granted admission to the institution.
   • "Waitlisted": Students whose admission status is pending, and they may be considered if additional spots become available.
   • "Rejected": Students who were not granted admission to the institution.

The dataset consists of 250 student records and has been generated for illustrative purposes. It can be used for various data analysis, modeling, and visualization tasks related to student admissions, such as predicting admission outcomes based on GPA and SAT scores, or studying the impact of extracurricular activities on admission decisions.

Please note that this dataset is entirely synthetic and not based on real-world data. It is intended for educational and practice purposes.

Research in assessing the Mental Health Problems (MHPs), e.g., stress, anxiety, and depression of university students has had much interest worldwide for the last decade. This article provides a large and comprehensive dataset concerning the MHPs of 2028 students from 15 top-ranked universities in Bangladesh, including 9 government/public universities and 6 private universities. To collect the data, the GAD-7 (for Anxiety), PSS-10 (for Stress), and PHQ-9 (for Depression) models are adopted to reflect equivalent academic perspectives. Additionally, student sociodemographic data are collected. The adoption of these three models are done by a team of five professors and a student psychologist to best capture the academic and socio-demographic factors that influence MHPs among university students. To conduct the survey, a google form is developed and circulated among the 15 faculty representatives from the participating universities who further circulated and conducted the survey with the students. Collected data is evaluated to ensure the sufficiency of sample size, and internal consistency and reliability of the response. Furthermore, the levels of anxiety, stress, and depression are calculated using the data to demonstrate its' applicability. This dataset can be used to measure the trajectory of students’ the mental and psychosocial stressors, to adopt required mental health and counselling services, and to conduct data intensive Machine Learning (ML) model development to predictive MPH assessment.

Analysis of ‘Student Performance Data Set’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/impapan/student-performance-data-set on 13 February 2022.

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

About this dataset

This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd period grades.

Attribute Information:

# Attributes for both student-mat.csv (Math course) and student-por.csv (Portuguese language course) datasets:
1 school - student's school (binary: 'GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira)
2 sex - student's sex (binary: 'F' - female or 'M' - male)
3 age - student's age (numeric: from 15 to 22)
4 address - student's home address type (binary: 'U' - urban or 'R' - rural)
5 famsize - family size (binary: 'LE3' - less or equal to 3 or 'GT3' - greater than 3)
6 Pstatus - parent's cohabitation status (binary: 'T' - living together or 'A' - apart)
7 Medu - mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
8 Fedu - father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
9 Mjob - mother's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')
10 Fjob - father's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')
11 reason - reason to choose this school (nominal: close to 'home', school 'reputation', 'course' preference or 'other')
12 guardian - student's guardian (nominal: 'mother', 'father' or 'other')
13 traveltime - home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour)
14 studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours)
15 failures - number of past class failures (numeric: n if 1<=n<3, else 4)
16 schoolsup - extra educational support (binary: yes or no)
17 famsup - family educational support (binary: yes or no)
18 paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)
19 activities - extra-curricular activities (binary: yes or no)
20 nursery - attended nursery school (binary: yes or no)
21 higher - wants to take higher education (binary: yes or no)
22 internet - Internet access at home (binary: yes or no)
23 romantic - with a romantic relationship (binary: yes or no)
24 famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)
25 freetime - free time after school (numeric: from 1 - very low to 5 - very high)
26 goout - going out with friends (numeric: from 1 - very low to 5 - very high)
27 Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high)
28 Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)
29 health - current health status (numeric: from 1 - very bad to 5 - very good)
30 absences - number of school absences (numeric: from 0 to 93)

# these grades are related with the course subject, Math or Portuguese:
31 G1 - first period grade (numeric: from 0 to 20)
31 G2 - second period grade (numeric: from 0 to 20)
32 G3 - final grade (numeric: from 0 to 20, output target)

Acknowledgements

If you use this dataset in your research, please credit the authors

Citations

P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.

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

This data archive shares publicly available datasets and syntax files used to produce results in the paper "Racial Economic Segregation across U.S. Public Schools, 1991-2022, " where I describes trends in racial economic segregation over the last three decades and decomposes these trends into different geographic scales (e.g., between-state, between-district, and within-district segregation). In doing so, I use the Longitudinal Imputed Student Dataset, a newly released dataset that imputes low-quality free lunch eligibility enrollment data in the Common Core of Data.

This dataset contains the 100 level first semester results of 229 students in South East University in Nigeria. The average score for each student is computed based on 8 courses offered in that semester. The dataset contains both the CA and Exam scores respectively. The CA amd Exam score were subsequently conveerted to percentage

Dataset Overview: This dataset pertains to the examination results of students who participated in a series of academic assessments at a fictitious educational institution named "University of Exampleville." The assessments were administered across various courses and academic levels, with a focus on evaluating students' performance in general management and domain-specific topics.

Columns: The dataset comprises 12 columns, each representing specific attributes and performance indicators of the students. These columns encompass information such as the students' names (which have been anonymized), their respective universities, academic program names (including BBA and MBA), specializations, the semester of the assessment, the type of examination domain (general management or domain-specific), general management scores (out of 50), domain-specific scores (out of 50), total scores (out of 100), student ranks, and percentiles.

Data Collection: The examination data was collected during a standardized assessment process conducted by the University of Exampleville. The exams were designed to assess students' knowledge and skills in general management and their chosen domain-specific subjects. It involved students from both BBA and MBA programs who were in their final year of study.

Data Format: The dataset is available in a structured format, typically as a CSV file. Each row represents a unique student's performance in the examination, while columns contain specific information about their results and academic details.

Data Usage: This dataset is valuable for analyzing and gaining insights into the academic performance of students pursuing BBA and MBA degrees. It can be used for various purposes, including statistical analysis, performance trend identification, program assessment, and comparison of scores across domains and specializations. Furthermore, it can be employed in predictive modeling or decision-making related to curriculum development and student support.

Data Quality: The dataset has undergone preprocessing and anonymization to protect the privacy of individual students. Nevertheless, it is essential to use the data responsibly and in compliance with relevant data protection regulations when conducting any analysis or research.

Data Format: The exam data is typically provided in a structured format, commonly as a CSV (Comma-Separated Values) file. Each row in the dataset represents a unique student's examination performance, and each column contains specific attributes and scores related to the examination. The CSV format allows for easy import and analysis using various data analysis tools and programming languages like Python, R, or spreadsheet software like Microsoft Excel.

Here's a column-wise description of the dataset:

Name OF THE STUDENT: The full name of the student who took the exam. (Anonymized)

UNIVERSITY: The university where the student is enrolled.

PROGRAM NAME: The name of the academic program in which the student is enrolled (BBA or MBA).

Specialization: If applicable, the specific area of specialization or major that the student has chosen within their program.

Semester: The semester or academic term in which the student took the exam.

Domain: Indicates whether the exam was divided into two parts: general management and domain-specific.

GENERAL MANAGEMENT SCORE (OUT of 50): The score obtained by the student in the general management part of the exam, out of a maximum possible score of 50.

Domain-Specific Score (Out of 50): The score obtained by the student in the domain-specific part of the exam, also out of a maximum possible score of 50.

TOTAL SCORE (OUT of 100): The total score obtained by adding the scores from the general management and domain-specific parts, out of a maximum possible score of 100.

Student Classroom Activity

## Overview

Student Classroom Activity is a dataset for object detection tasks - it contains Student annotations for 779 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).

The Federal School Code List contains the unique codes assigned by the Department of Education for schools participating in the Title IV federal student aid programs. Students can enter these codes on the Free Application for Federal Student Aid (FAFSA) to indicate which postsecondary schools they want to receive their financial application results. The Federal School Code List is a searchable document in Excel format. The list will be updated on the first of February, May, August, and November of each calendar year.

This dataset includes the attendance rate for public school students PK-12 by town during the 2022-2023 school year.

Attendance rates are provided for each town for the overall student population and for the high needs student population. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

Increase the percentage of students covered under a 24/7 tobacco-free school policy from 74% in 2012 to 86% by 2018.

The Free Application for Federal Student Aid, 2010-11 (FAFSA 2010-11), is part of the Free Application for Federal Student Aid (FAFSA) program; program data is available since 2006-07 at . FAFSA 2010-11 (https://studentaid.ed.gov/) is a universe data collection of eligible incoming postsecondary education students, along with a subset of eligible continuing postsecondary education students, that collects financial information to determine the need and eligibility for financial assistance during postsecondary education. FAFSA 2010-11 applications are accepted via web and paper submission. Citizen and specified noncitizen students demonstrating financial need and planning to attend eligible degree or certificate programs in the 50 United States, the District of Columbia, Puerto Rico, and the outlying areas as regular students are eligible to apply for FAFSA 2010-11. FAFSA 2010-11 resulted in an expected family contribution (EFC) for each applying student. Statistics produced from FAFSA 2010-11 include application volumes by postsecondary school, state of legal residence, and completion by high school; and recipient and volume data by program for each school participating in Title IV programs.