100+ datasets found
  1. Student Performance Data Set

    • kaggle.com
    Updated Mar 27, 2020
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    Data-Science Sean (2020). Student Performance Data Set [Dataset]. https://www.kaggle.com/datasets/larsen0966/student-performance-data-set
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 27, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Data-Science Sean
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    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).

  2. US Highschool students dataset

    • kaggle.com
    zip
    Updated Apr 14, 2024
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    peter mushemi (2024). US Highschool students dataset [Dataset]. https://www.kaggle.com/datasets/petermushemi/us-highschool-students-dataset
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    zip(0 bytes)Available download formats
    Dataset updated
    Apr 14, 2024
    Authors
    peter mushemi
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    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.

  3. N

    2014-2015 DOE High School Performance-Directory

    • data.cityofnewyork.us
    • catalog.data.gov
    • +2more
    application/rdfxml +5
    Updated Oct 29, 2014
    + more versions
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    Department of Education (DOE) (2014). 2014-2015 DOE High School Performance-Directory [Dataset]. https://data.cityofnewyork.us/Education/2014-2015-DOE-High-School-Performance-Directory/xahu-rkwn
    Explore at:
    application/rdfxml, xml, application/rssxml, csv, tsv, jsonAvailable download formats
    Dataset updated
    Oct 29, 2014
    Dataset authored and provided by
    Department of Education (DOE)
    Description

    Performance of NYC High Schools

  4. Student Performance Factors

    • kaggle.com
    Updated Nov 26, 2024
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    Practice Data Analysis With Me (2024). Student Performance Factors [Dataset]. https://www.kaggle.com/datasets/lainguyn123/student-performance-factors
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 26, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Practice Data Analysis With Me
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    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

    AttributeDescription
    Hours_StudiedNumber of hours spent studying per week.
    AttendancePercentage of classes attended.
    Parental_InvolvementLevel of parental involvement in the student's education (Low, Medium, High).
    Access_to_ResourcesAvailability of educational resources (Low, Medium, High).
    Extracurricular_ActivitiesParticipation in extracurricular activities (Yes, No).
    Sleep_HoursAverage number of hours of sleep per night.
    Previous_ScoresScores from previous exams.
    Motivation_LevelStudent's level of motivation (Low, Medium, High).
    Internet_AccessAvailability of internet access (Yes, No).
    Tutoring_SessionsNumber of tutoring sessions attended per month.
    Family_IncomeFamily income level (Low, Medium, High).
    Teacher_QualityQuality of the teachers (Low, Medium, High).
    School_TypeType of school attended (Public, Private).
    Peer_InfluenceInfluence of peers on academic performance (Positive, Neutral, Negative).
    Physical_ActivityAverage number of hours of physical activity per week.
    Learning_DisabilitiesPresence of learning disabilities (Yes, No).
    Parental_Education_LevelHighest education level of parents (High School, College, Postgraduate).
    Distance_from_HomeDistance from home to school (Near, Moderate, Far).
    GenderGender of the student (Male, Female).
    Exam_ScoreFinal exam score.
  5. c

    Student Performance Dataset

    • cubig.ai
    Updated May 28, 2025
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    CUBIG (2025). Student Performance Dataset [Dataset]. https://cubig.ai/store/products/358/student-performance-dataset
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    Dataset updated
    May 28, 2025
    Dataset authored and provided by
    CUBIG
    License

    https://cubig.ai/store/terms-of-servicehttps://cubig.ai/store/terms-of-service

    Measurement technique
    Synthetic data generation using AI techniques for model training, Privacy-preserving data transformation via differential privacy
    Description

    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.

  6. p

    Distribution of Students Across Grade Levels in Performance Conservatory...

    • publicschoolreview.com
    Updated May 18, 2024
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    Public School Review (2024). Distribution of Students Across Grade Levels in Performance Conservatory High School [Dataset]. https://www.publicschoolreview.com/performance-conservatory-high-school-profile
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    Dataset updated
    May 18, 2024
    Dataset authored and provided by
    Public School Review
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset tracks annual distribution of students across grade levels in Performance Conservatory High School

  7. High school students' private education South Korea 2024, by performance

    • statista.com
    Updated Jun 12, 2025
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    Statista (2025). High school students' private education South Korea 2024, by performance [Dataset]. https://www.statista.com/statistics/1044025/south-korea-high-school-student-private-education-by-school-performance/
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    Dataset updated
    Jun 12, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    South Korea
    Description

    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.

  8. r

    3-Year Average Academic Performance Indicator (API)

    • stanford.redivis.com
    • redivis.com
    Updated Aug 9, 2025
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    Stanford Center for Population Health Sciences (2025). 3-Year Average Academic Performance Indicator (API) [Dataset]. https://stanford.redivis.com/datasets/kxa3-bbw2dknma/tables?tablesList-sampled=sampled
    Explore at:
    Dataset updated
    Aug 9, 2025
    Dataset authored and provided by
    Stanford Center for Population Health Sciences
    Time period covered
    2011 - 2013
    Description

    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).

  9. A

    ‘2015 - 2016 School Quality Report Results for High School Transfer’...

    • analyst-2.ai
    Updated Dec 10, 2015
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2015). ‘2015 - 2016 School Quality Report Results for High School Transfer’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-2015-2016-school-quality-report-results-for-high-school-transfer-737d/457b21fb/?iid=081-258&v=presentation
    Explore at:
    Dataset updated
    Dec 10, 2015
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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 ---

  10. p

    Trends in Total Students (2007-2023): Performance Conservatory High School

    • publicschoolreview.com
    Updated May 18, 2024
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    Public School Review (2024). Trends in Total Students (2007-2023): Performance Conservatory High School [Dataset]. https://www.publicschoolreview.com/performance-conservatory-high-school-profile
    Explore at:
    Dataset updated
    May 18, 2024
    Dataset authored and provided by
    Public School Review
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset tracks annual total students amount from 2007 to 2023 for Performance Conservatory High School

  11. T

    Student Progression from High School through Postsecondary Education

    • educationtocareer.data.mass.gov
    application/rdfxml +5
    Updated Apr 22, 2025
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    Department of Elementary and Secondary Education (2025). Student Progression from High School through Postsecondary Education [Dataset]. https://educationtocareer.data.mass.gov/w/sg4g-eg2n/default?cur=C2a2JXpg7Gw
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    csv, json, application/rssxml, xml, tsv, application/rdfxmlAvailable download formats
    Dataset updated
    Apr 22, 2025
    Dataset authored and provided by
    Department of Elementary and Secondary Education
    Description

    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

  12. f

    Table_1_Identifying Reliable Predictors of Educational Outcomes Through...

    • frontiersin.figshare.com
    docx
    Updated May 31, 2023
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    Mariel F. Musso; Eduardo C. Cascallar; Neda Bostani; Michael Crawford (2023). Table_1_Identifying Reliable Predictors of Educational Outcomes Through Machine-Learning Predictive Modeling.DOCX [Dataset]. http://doi.org/10.3389/feduc.2020.00104.s001
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    docxAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Frontiers
    Authors
    Mariel F. Musso; Eduardo C. Cascallar; Neda Bostani; Michael Crawford
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  13. h

    student-performance

    • huggingface.co
    Updated May 28, 2025
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    Soumyadip Sarkar (2025). student-performance [Dataset]. http://doi.org/10.57967/hf/5412
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    Dataset updated
    May 28, 2025
    Authors
    Soumyadip Sarkar
    License

    https://choosealicense.com/licenses/cc0-1.0/https://choosealicense.com/licenses/cc0-1.0/

    Description

    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.

  14. d

    2019-20 School Quality Guide High School Transfer

    • catalog.data.gov
    • data.cityofnewyork.us
    Updated Nov 29, 2024
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    data.cityofnewyork.us (2024). 2019-20 School Quality Guide High School Transfer [Dataset]. https://catalog.data.gov/dataset/2019-20-school-quality-guide-high-school-transfer
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    Dataset updated
    Nov 29, 2024
    Dataset provided by
    data.cityofnewyork.us
    Description

    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.

  15. d

    2014-2015 School Quality Reports Results For High Schools

    • datasets.ai
    • catalog.data.gov
    53
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    City of New York, 2014-2015 School Quality Reports Results For High Schools [Dataset]. https://datasets.ai/datasets/2014-2015-school-quality-reports-results-for-high-schools
    Explore at:
    53Available download formats
    Dataset authored and provided by
    City of New York
    Description

    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.

  16. H

    Student Performance Prediction Data set

    • dataverse.harvard.edu
    Updated May 31, 2020
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    Ephrem Admasu Yekun (2020). Student Performance Prediction Data set [Dataset]. http://doi.org/10.7910/DVN/WHBU4P
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 31, 2020
    Dataset provided by
    Harvard Dataverse
    Authors
    Ephrem Admasu Yekun
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    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.

  17. o

    Data and Code for: Standardized Test Scores and Academic Performance at...

    • openicpsr.org
    Updated May 8, 2025
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    John Friedman; Bruce Sacerdote; Douglas Staiger; Michele Tine (2025). Data and Code for: Standardized Test Scores and Academic Performance at Ivy-Plus Colleges [Dataset]. http://doi.org/10.3886/E228945V1
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    Dataset updated
    May 8, 2025
    Dataset provided by
    American Economic Association
    Authors
    John Friedman; Bruce Sacerdote; Douglas Staiger; Michele Tine
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  18. p

    Trends in Asian Student Percentage (2007-2023): Performance Conservatory...

    • publicschoolreview.com
    Updated May 18, 2024
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    Public School Review (2024). Trends in Asian Student Percentage (2007-2023): Performance Conservatory High School vs. New York vs. New York City Geographic District #12 [Dataset]. https://www.publicschoolreview.com/performance-conservatory-high-school-profile
    Explore at:
    Dataset updated
    May 18, 2024
    Dataset authored and provided by
    Public School Review
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    New York
    Description

    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

  19. d

    2015 - 2016 School Quality Report Results for High School

    • datasets.ai
    23, 40, 55, 8
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    City of New York, 2015 - 2016 School Quality Report Results for High School [Dataset]. https://datasets.ai/datasets/2015-2016-school-quality-report-results-for-high-school
    Explore at:
    23, 40, 55, 8Available download formats
    Dataset authored and provided by
    City of New York
    Description

    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.

  20. g

    2014-2015 DOE High School Performance-Directory | gimi9.com

    • gimi9.com
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    2014-2015 DOE High School Performance-Directory | gimi9.com [Dataset]. https://gimi9.com/dataset/data-gov_2014-2015-doe-high-school-performance-directory/
    Explore at:
    Description

    🇺🇸 미국

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Data-Science Sean (2020). Student Performance Data Set [Dataset]. https://www.kaggle.com/datasets/larsen0966/student-performance-data-set
Organization logo

Student Performance Data Set

Student achievement in secondary education of two Portuguese schools.

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Mar 27, 2020
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Data-Science Sean
License

https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

Description

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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