Student Performance

Donated on 11/26/2014

Predict student performance in secondary education (high school).

Dataset Characteristics

Multivariate

Subject Area

Social Science

Associated Tasks

Classification, Regression

Feature Type

Integer

# Instances

649

# Features

30

Dataset Information

Additional Information

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).

Has Missing Values?

No

Introductory Paper

Using data mining to predict secondary school student performance

By P. Cortez, A. M. G. Silva. 2008

Published in Proceedings of 5th Annual Future Business Technology Conference

Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
schoolFeatureCategoricalstudent's school (binary: 'GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira)no
sexFeatureBinarySexstudent's sex (binary: 'F' - female or 'M' - male)no
ageFeatureIntegerAgestudent's age (numeric: from 15 to 22)no
addressFeatureCategoricalstudent's home address type (binary: 'U' - urban or 'R' - rural)no
famsizeFeatureCategoricalOtherfamily size (binary: 'LE3' - less or equal to 3 or 'GT3' - greater than 3)no
PstatusFeatureCategoricalOtherparent's cohabitation status (binary: 'T' - living together or 'A' - apart)no
MeduFeatureIntegerEducation Levelmother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education)no
FeduFeatureIntegerEducation Levelfather's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)no
MjobFeatureCategoricalOccupationmother's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')no
FjobFeatureCategoricalOccupationfather's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')no

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Additional Variable 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)

Papers Citing this Dataset

The Latent of Student Learning Analytic with K-mean Clustering for Student Behaviour Classification

By Andi Mansur, Norazah Yusof. 2018

Published in Journal of Information Systems Engineering and Business Intelligence.

Adding bias to reduce variance in psychological results: A tutorial on penalized regression

By Nathaniel Helwig. 2017

Published in The Quantitative Methods for Psychology.

Feature Selection based on the Local Lift Dependence Scale

By Diego Marcondes, Adilson Simonis, Junior Barrera. 2017

Published in ArXiv.

Data Mining of Students' Performance : Turkish Students as a Case Study

By Oyebade Oyedotun, Sam Tackie, Ebenezer Olaniyi, Adnan Khashman. 2015

Published in International Journal of Intelligent Systems and Applications.

A Wrapper for Reweighting Training Instances for Handling Imbalanced Data Sets

By M. Karagiannopoulos, D. Anyfantis, Sotiris Kotsiantis, Panayiotis Pintelas. 2007

Published in AIAI.

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Creators

Paulo Cortez

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