If this Data Set is useful, and upvote is appreciated. 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).

The Iowa School Performance Profiles is an online tool showing how public schools performed on required measures. The website was developed to meet both federal and state requirements for publishing online school report cards: The federal Every Student Succeeds Act and House File 215, adopted by Iowa lawmakers in 2013. The website includes: Scores on school accountability measures required under ESSARatings based on those scores: Exceptional, High Performing, Commendable, Acceptable, Needs Improvement, and PriorityIdentification of schools for support and improvement based on accountability scores (Comprehensive and Targeted schools)Additional education data that must be reported by law but do not figure into school accountability scores To learn more about school scores, measures, rankings and other data, visit the “Help” section for a user guide, technical guide and other resources.

The dataset is related to student data, from an educational research study focusing on student demographics, academic performance, and related factors. Here’s a general description of what each column likely represents:

Sex: The gender of the student (e.g., Male, Female). Age: The age of the student. Name: The name of the student. State: The state where the student resides or where the educational institution is located. Address: Indicates whether the student lives in an urban or rural area. Famsize: Family size category (e.g., LE3 for families with less than or equal to 3 members, GT3 for more than 3). Pstatus: Parental cohabitation status (e.g., 'T' for living together, 'A' for living apart). Medu: Mother's education level (e.g., Graduate, College). Fedu: Father's education level (similar categories to Medu). Mjob: Mother's job type. Fjob: Father's job type. Guardian: The primary guardian of the student. Math_Score: Score obtained by the student in Mathematics. Reading_Score: Score obtained by the student in Reading. Writing_Score: Score obtained by the student in Writing. Attendance_Rate: The percentage rate of the student’s attendance. Suspensions: Number of times the student has been suspended. Expulsions: Number of times the student has been expelled. Teacher_Support: Level of support the student receives from teachers (e.g., Low, Medium, High). Counseling: Indicates whether the student receives counseling services (Yes or No). Social_Worker_Visits: Number of times a social worker has visited the student. Parental_Involvement: The level of parental involvement in the student's academic life (e.g., Low, Medium, High). GPA: The student’s Grade Point Average, a standard measure of academic achievement in schools.

This dataset provides a comprehensive look at various factors that might influence a student's educational outcomes, including demographic factors, academic performance metrics, and support structures both at home and within the educational system. It can be used for statistical analysis to understand and improve student success rates, or for targeted interventions based on specific identified needs.

1) Data Introduction • The Student Performance Dataset is a survey of secondary school mathematics students and is a dataset containing a variety of information in a table format, including student demographics, family environment, parents' education and occupation, health, family relationships, and grades.

2) Data Utilization (1) Student Performance Dataset has characteristics that: • Each row contains a total of 33 different characteristics, including school ID, gender, age, family size, parents' educational level and occupation, family relationship, health status, and grades. • It is suitable for a variety of data analysis and prediction exercises, including regression analysis and categorical variable imbalance analysis, including the target variable Grade. (2) Student Performance Dataset can be used to: • Analyzing academic achievement prediction and influencing factors: It can be used to analyze the impact of various factors such as student's background, family environment, and parental characteristics on grades and to develop a grade prediction model. • Establishing educational policies and customized support strategies: Based on student-specific characteristics and grade data, it can be applied to establishing educational policies such as closing educational gaps, supporting vulnerable student groups, and providing customized learning guidance.

This dataset contains the school performance indices (SPIs) for 2009-10 (2010), 2010-11 (2011), and 2011-12 (2012) for all schools that administered the Connecticut Mastery Test (CMT). These data were published in the School Performance Reports released by the CT State Department of Education (CSDE) in December 2013 (see http://www.csde.state.ct.us/public/performancereports/20122013reports.asp) Note: Cells are left blank if there is no SPI, which happens when there are small N sizes for a particular subgroup or subject.

This dataset contains the details of key school performance indicators like the drop-out rate, retention rate, repetition rate, and the promotion rate by levels of education for all schools.

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.

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

This is a synthesized dataset based on real academic performance data of high school students in several schools in Vietnam. This data can be useful for analysis, training prediction models on academic performance, personalized study planning, and career counseling, among other applications.

The data used contains only anonymized and non-identifiable information collected from high school students, including demographic and academic performance attributes. No personally identifying information was collected or used. The data is used exclusively for academic research purposes under ethical guidelines, and no attempt is made to trace or analyze individual-level outcomes.

Performance of NYC High Schools

Student performance

The Student performance dataset from Kaggle.

Configuration Task Description

encoding

Encoding dictionary showing original values of encoded features.

math Binary classification Has the student passed the math exam?

writing Binary classification Has the student passed the writing exam?

reading Binary classification Has the student passed the reading exam?

  Usage

from datasets importload_dataset

dataset =… See the full description on the dataset page: https://huggingface.co/datasets/mstz/student_performance.

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 contains the school classifications, school performance indices (SPIs), and SPI target attainment status for 2012-13 for all schools that administered the Connecticut Academic Performance Test (CAPT). It also includes school classifications assigned to high schools with non-tested grades. These data were published in the School Performance Reports released by the CT State Department of Education (CSDE) in December 2013 (see http://www.csde.state.ct.us/public/performancereports/20122013reports.asp) Note: Target attainment status will say “n/a” if there is no 2012-13 SPI target or if there is no 2012-13 SPI, which happens when there are small N sizes for a particular subgroup or subject.

This dataset contains the school performance indices (SPIs) for 2009-10 (2010), 2010-11 (2011), and 2011-12 (2012) for all schools that administered the Connecticut Academic Performance Test (CAPT). These data were published in the School Performance Reports released by the CT State Department of Education (CSDE) in December 2013 (see http://www.csde.state.ct.us/public/performancereports/20122013reports.asp)

Note: Cells are left blank if there is no SPI, which happens when there are small N sizes for a particular subgroup or subject.

The School Quality Reports share information about school performance, set expectations for schools, and promote school improvement. Due to size constraints only partial data is reflected, to view entire data open up the excel file that shown with data set name. These reports include information from multiple sources, including Quality Reviews, the NYC School Survey, and student performance. The School Quality Reports are organized around the Framework for Great Schools, which includes six elements Rigorous Instruction, Collaborative Teachers, Supportive Environment, Effective School Leadership, Strong FamilyCommunity Ties, and Trust—that drive student achievement and school improvement.

The Student Performance Dataset is designed to evaluate and predict student outcomes based on various factors that can influence academic success. This synthetic dataset includes features that are commonly considered in educational research and real-world scenarios, such as attendance, study habits, previous academic performance, and participation in extracurricular activities. The goal is to understand how these factors correlate with the final grades of students and to build a predictive model that can forecast student performance.

Dataset Features: StudentID: A unique identifier for each student. Name: The name of the student. Gender: The gender of the student (Male/Female). AttendanceRate: The percentage of classes attended by the student. StudyHoursPerWeek: The number of hours the student spends studying each week. PreviousGrade: The grade the student achieved in the previous semester (out of 100). ExtracurricularActivities: The number of extracurricular activities the student is involved in. ParentalSupport: A qualitative assessment of the level of support provided by the student's parents (High/Medium/Low). FinalGrade: The final grade of the student (out of 100), which serves as the target variable for prediction. Use Cases: Predicting Student Performance: The dataset can be used to build machine learning models that predict the final grade of students based on the other features. This can help educators identify students who may need additional support to improve their outcomes.

Exploratory Data Analysis: Researchers and data scientists can explore the relationships between different factors (like attendance or study habits) and student performance. For example, understanding whether higher attendance correlates with better grades.

Feature Importance Analysis: The dataset allows for the examination of which features are most predictive of student success, providing insights into key areas of focus for educational interventions.

Educational Interventions: By identifying patterns in the data, schools and educational institutions can implement targeted interventions to help students improve in specific areas, such as increasing study hours or encouraging participation in extracurricular activities.

Potential Insights: Correlation Between Study Habits and Performance: The dataset can be used to determine how much study time contributes to academic success.

Impact of Attendance on Grades: Analysis can reveal the extent to which regular attendance influences final grades.

Role of Extracurricular Activities: The dataset can help assess whether participation in extracurricular activities positively or negatively impacts academic performance.

Influence of Parental Support: The data allows for the examination of how different levels of parental support affect student outcomes.

Conclusion: The Student Performance Dataset is a versatile tool for educators, data scientists, and researchers interested in understanding and predicting student success. By analyzing this data, stakeholders can gain valuable insights into the factors that contribute to academic performance and develop strategies to enhance educational outcomes

This dataset is used to produce the School Performance Profile scores found at http://paschoolperformance.org. It is for School Year 2015. School Performance Profile scores are calculated for all open public schools in Pennsylvania. These include regular schools, charter schools, cyber charter schools, and full-time career and technical education centers. The scores reflect one of many indicators of a school’s academic performance.

In April 2020, ** percent of parents whose children switched to online education in Russian schools because of the coronavirus (COVID-19) pandemic noticed improvements in their grades over that period. Six percent of respondents reported lower academic performance.