Skip to main content

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W ratio Numerical n/a Applicable Ratio of withdrawn subject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’