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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).
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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.
Performance of NYC High Schools
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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.
Attribute | Description |
---|---|
Hours_Studied | Number of hours spent studying per week. |
Attendance | Percentage of classes attended. |
Parental_Involvement | Level of parental involvement in the student's education (Low, Medium, High). |
Access_to_Resources | Availability of educational resources (Low, Medium, High). |
Extracurricular_Activities | Participation in extracurricular activities (Yes, No). |
Sleep_Hours | Average number of hours of sleep per night. |
Previous_Scores | Scores from previous exams. |
Motivation_Level | Student's level of motivation (Low, Medium, High). |
Internet_Access | Availability of internet access (Yes, No). |
Tutoring_Sessions | Number of tutoring sessions attended per month. |
Family_Income | Family income level (Low, Medium, High). |
Teacher_Quality | Quality of the teachers (Low, Medium, High). |
School_Type | Type of school attended (Public, Private). |
Peer_Influence | Influence of peers on academic performance (Positive, Neutral, Negative). |
Physical_Activity | Average number of hours of physical activity per week. |
Learning_Disabilities | Presence of learning disabilities (Yes, No). |
Parental_Education_Level | Highest education level of parents (High School, College, Postgraduate). |
Distance_from_Home | Distance from home to school (Near, Moderate, Far). |
Gender | Gender of the student (Male, Female). |
Exam_Score | Final exam score. |
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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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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This dataset tracks annual distribution of students across grade levels in Performance Conservatory High School
In 2024, around **** percent of South Korean students in the top ten percent in terms of school performance were participating in private education after school. The average participation rate stood at about **** percent.
3-year Average Academic Performance Indicator data based on test results of the Standardized Testing and Reporting (STAR) Program, the California High School Exit Examination (CAHSEE), and the California Alternate Performance Assessment (CAPA).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Analysis of ‘2015 - 2016 School Quality Report Results for High School Transfer’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/f2ce861c-6109-4633-a6de-895b8249ec53 on 12 November 2021.
--- Dataset description provided by original source is as follows ---
New York City Department of Education 2015 - 2016 School Quality Report Results for High School Transfer. The Quality Review is a process that evaluates how well schools are organized to support student learning and teacher practice. It was developed to assist New York City Department of Education (NYCDOE) schools in raising student achievement by looking behind a school’s performance statistics to ensure that the school is engaged in effective methods of accelerating student learning.
--- Original source retains full ownership of the source dataset ---
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This dataset tracks annual total students amount from 2007 to 2023 for Performance Conservatory High School
The District Analysis and Review Tools (DARTs) offer snapshots of district and school performance, allowing users to easily track select data elements over time, and make sound, meaningful comparisons to the state or to "comparable" organizations. The waterfall data shows a cohort of high school students and their progression through high school graduation, college enrollment and persistence in higher education to a second year or college completion.
This is a companion dataset to the main DART: Success After High School dataset. It contains two indicators published separately from the main dataset since the data are in a different format: "Student progression from high school through second year of postsecondary education" and "Student progression from high school through postsecondary degree completion". For all other DART: Success After High School indicators, please visit the main DART: Success After High School dataset.
This dataset contains the same data that is also published on our DART Detail: Success After High School Online Dashboard
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Results-based financing has guided the development of policies with measurable results improving learning outcomes at micro/macro levels. However, it is then necessary to identify factors which predict early and accurately favorable or challenging conditions for learning. Learning outcomes depend on complex interactions between multiple variables, many of which are not fully understood. The objective was to develop valid and accurate models predicting low and high levels of math performance and Vietnamese language, using machine-learning algorithms, as part of an international large-scale project in primary education in Vietnam. The models achieved very high accuracy (95–100%). A strong common pattern has been found for both Math and Vietnamese language, for the low and high levels of performance: the individual cognitive characteristics, physical factors and daily routines/ activities of the child are very important predictive factors of academic performance, as measured by student performance in the final Grade 5 test in math and Vietnamese, respectively. Parental expectations, pre-school attendance and school trajectory of students have added relative importance in the classification. In order to accurately identify an expected low or high academic performance outcome, it is the full pattern of variables contained in the vector of information from each case that should be considered. Because, although each variable in a particular vector has a small contribution to the total predictive weight, it is the overall pattern containing the interactions between these variables that carries the necessary information for the accurate predictions. In addition, the identification of specific patterns for extreme groups of performance provides the necessary guidance for more focused educational interventions/investment and sound educational policies.
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Student Performance Dataset
Dataset Description
This dataset contains ten million synthetically generated student performance records, designed to mimic real-world educational data at the high-school level. It includes detailed demographic, socioeconomic, academic, behavioral, and school-context features for each student, suitable for benchmarking, machine learning, educational research, and exploratory data analysis.
File Information
Split File Name… See the full description on the dataset page: https://huggingface.co/datasets/neuralsorcerer/student-performance.
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 include six elements Rigorous Instruction, Collaborative Teachers, Supportive Environment, Effective School Leadership, Strong FamilyCommunity Ties, and Trust—that drive student achievement and school improvement.
The Quality Review is a process that evaluates how well schools are organized to support student learning and teacher practice. It was developed to assist New York City Department of Education (NYCDOE) schools in raising student achievement by looking behind a school’s performance statistics to ensure that the school is engaged in effective methods of accelerating student learning.
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We use the data set for training, validation, and testing of high school students performance prediction. We use the data set for training, validation, and testing of high school students performance prediction.
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We analyze admissions and transcript records for students at multiple Ivy-Plus colleges to study the relationship between standardized (SAT/ACT) test scores, high school GPA, and first-year college grades. Standardized test scores predict academic outcomes with a normalized slope four times greater than that from high school GPA, all conditional on students’ race, gender, and socioeconomic status. Standardized test scores also exhibit no calibration bias, as they do not underpredict college performance for students from less advantaged backgrounds. Collectively these results suggest that standardized test scores provide important information to measure applicants’ academic preparation that is not available elsewhere in the application file.
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This dataset tracks annual asian student percentage from 2007 to 2023 for Performance Conservatory High School vs. New York and New York City Geographic District #12
New York City Department of Education 2015 - 2016 School Quality Report Results for High School. The Quality Review is a process that evaluates how well schools are organized to support student learning and teacher practice. It was developed to assist New York City Department of Education (NYCDOE) schools in raising student achievement by looking behind a school’s performance statistics to ensure that the school is engaged in effective methods of accelerating student learning.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
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).