Student Performance

About

Predict student performance in secondary education (high school). 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).

Subject Area

Social Science

Instances

649

Features

Data Types

Multivariate

Tasks

Classification, Regression

Feature Types

Integer

Features

Name	Role	Type	Units	Missing Values
school	Feature	Categorical	-	No
└ student's school (binary: 'GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira)
sex	Feature	Binary	-	No
└ student's sex (binary: 'F' - female or 'M' - male)
age	Feature	Integer	-	No
└ student's age (numeric: from 15 to 22)
address	Feature	Categorical	-	No
└ student's home address type (binary: 'U' - urban or 'R' - rural)
famsize	Feature	Categorical	-	No
└ family size (binary: 'LE3' - less or equal to 3 or 'GT3' - greater than 3)
Pstatus	Feature	Categorical	-	No
└ parent's cohabitation status (binary: 'T' - living together or 'A' - apart)
Medu	Feature	Integer	-	No
└ mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education)
Fedu	Feature	Integer	-	No
└ father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 â€“ 5th to 9th grade, 3 â€“ secondary education or 4 â€“ higher education)
Mjob	Feature	Categorical	-	No
└ mother's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')
Fjob	Feature	Categorical	-	No
└ father's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')
reason	Feature	Categorical	-	No
└ reason to choose this school (nominal: close to 'home', school 'reputation', 'course' preference or 'other')
guardian	Feature	Categorical	-	No
└ student's guardian (nominal: 'mother', 'father' or 'other')
traveltime	Feature	Integer	-	No
└ home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour)
studytime	Feature	Integer	-	No
└ weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours)
failures	Feature	Integer	-	No
└ number of past class failures (numeric: n if 1<=n<3, else 4)
schoolsup	Feature	Binary	-	No
└ extra educational support (binary: yes or no)
famsup	Feature	Binary	-	No
└ family educational support (binary: yes or no)
paid	Feature	Binary	-	No
└ extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)
activities	Feature	Binary	-	No
└ extra-curricular activities (binary: yes or no)
nursery	Feature	Binary	-	No
└ attended nursery school (binary: yes or no)
higher	Feature	Binary	-	No
└ wants to take higher education (binary: yes or no)
internet	Feature	Binary	-	No
└ Internet access at home (binary: yes or no)
romantic	Feature	Binary	-	No
└ with a romantic relationship (binary: yes or no)
famrel	Feature	Integer	-	No
└ quality of family relationships (numeric: from 1 - very bad to 5 - excellent)
freetime	Feature	Integer	-	No
└ free time after school (numeric: from 1 - very low to 5 - very high)
goout	Feature	Integer	-	No
└ going out with friends (numeric: from 1 - very low to 5 - very high)
Dalc	Feature	Integer	-	No
└ workday alcohol consumption (numeric: from 1 - very low to 5 - very high)
Walc	Feature	Integer	-	No
└ weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)
health	Feature	Integer	-	No
└ current health status (numeric: from 1 - very bad to 5 - very good)
absences	Feature	Integer	-	No
└ number of school absences (numeric: from 0 to 93)
G1	Target	Categorical	-	No
└ first period grade (numeric: from 0 to 20)
G2	Target	Categorical	-	No
└ second period grade (numeric: from 0 to 20)
G3	Target	Integer	-	No
└ final grade (numeric: from 0 to 20, output target)

Introductory Paper

Using data mining to predict secondary school student performance

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

Proceedings of 5th Annual Future Business Technology Conference

Additional Metadata

Keywords

–

Authors

Paulo Cortez

Year Created

2008

DOI

10.24432/C5TG7T

License

CC BY 4.0

Student Performance

About

Features

Introductory Paper

Additional Metadata

Authors

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