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Table 1 Description of investigated dataset, with Context1 and Context2 denoting two problem contexts of before- and after-first-year prediction

From: Generating descriptive model for student dropout: a review of clustering approach

Feature

Data type

Context1

Context2

Description

Sex

Nominal

Applicable

Applicable

Student’s sex

Province

Nominal

Applicable

Applicable

Student’s home province

Type

Nominal

Applicable

Applicable

Type of university entry

Department

Nominal

Applicable

Applicable

Academic department

S-GPAX

Numerical

Applicable

Applicable

School grade (Overall)

S-GPA1

Numerical

Applicable

Applicable

School grade (English)

S-GPA2

Numerical

Applicable

Applicable

School grade (Mathematics)

S-GPA3

Numerical

Applicable

Applicable

School grade (Science)

S-GPA4

Numerical

Applicable

Applicable

School grade (General)

GPAX

Numerical

n/a

Applicable

Student’s university grade

A ratio

Numerical

n/a

Applicable

Ratio of subject with grade A

B+ ratio

Numerical

n/a

Applicable

Ratio of subject with grade B+

B ratio

Numerical

n/a

Applicable

Ratio of subject with grade B

C+ ratio

Numerical

n/a

Applicable

Ratio of subject with grade C+

C ratio

Numerical

n/a

Applicable

Ratio of subject with grade C

D+ ratio

Numerical

n/a

Applicable

Ratio of subject with grade D+

D ratio

Numerical

n/a

Applicable

Ratio of subject with grade D

F ratio

Numerical

n/a

Applicable

Ratio of subject with grade F

S ratio

Numerical

n/a

Applicable

Ratio of subject with grade S

U ratio

Numerical

n/a

Applicable

Ratio of subject with grade U

W ratio

Numerical

n/a

Applicable

Ratio of withdrawn subject

  1. Note that ‘n/a’ is the abbreviation of ‘not applicable’