Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review

Table 7

General information about the experimental dataset of the reviewed studies (BDC: Before data cleaning, ADC: After data cleaning, DS: Dataset).

Paper

Dataset size

No. of variables

Independent variable categories

Highly influential factors

Dependent variable

DM task

Sample’s scope

[21]

12969

28

1. Personal information

1.1 Age

Binary class: dropout/graduated

Classification

Unspecified

2.1 GPA

2.2 Total course hours

2.3 Final status

2.4 Years enrolled

2.5 Language exam

2. University features

2.6 Writing exam

2.7 Science exam

2.8 Humanities exam

2.9 Maths exam

Withdraws

2.10 Applied year

2.11 Applied semester

[23]

206

40

1. University features

1.1 Weighted average grade

Binary class: dropout/retention

Classification

Engineering

1.2 Academic index

1.3 Average success rate

1.4 Success-to-failure ratio

2.Social & economic features

1.5 Efficiency

1.6 Progress factor

2.1 Family income quintile

[24]

ADC: 561

18

1. Student’s academic competencies

1.1 Delay index

9 clusters

Clustering

Unspecified

1.2 Average grade

1.3 Course year

1.4 Perceived delay index

2. Motivations

1.5 Difficulty in understanding

1.6 Difficulty in memorization

2.1 Motivation level

3. Academic resilience

2.2 Employment chances

3.1 ARS item1 … ARS item 6

4.1 Satisfaction with the study method

4. Student satisfaction

4.2 Satisfaction with achievements

4.3 Satisfaction with lectures

4.4 Satisfaction with learning

[25]

499

51

1. Family information

1.1 Qualification of father

Binary class: at-risk/not at-risk

Classification

Computer

1.2 Mother’s income

2.1 Previous semester’s percentage

2. University features

2.2 Present semester attendance

3.1 Impact of friends’ circle

3. Social

3.2 Extracurricular activities and location of residence

4. Behavioral features

4.1 Self-study time

4.2 Behavior of assignment completion

[26]

38,842

>15

1. University features

1.1 Course credit (maths, chemistry, finance, economics, first-year seminar, etc.)

Binary class: completed/dropout

Classification

Arts & science

1.2 Course numeric grade

[27]

10,196

41

1. Personal information

3.1 First-semester academic achievement

Binary class: dropout/graduation

Classification

Technology & economics

2. Preuniversity features

3. University features

[28]

BDC: 178

12

1. Personal information

1.1 Gender

Binary class: dropout/not dropout

Classification

Engineering

1.2 Origin

2. Family information

2.1 Father’s occupation

2.2 Mother’s education

2.3 Mother’s occupation

ADC: 104

3. Preuniversity features

3.1 Senior high school department

4.1 First-semester attendance

4. University features

4.2 First-semester GPA

[31]

32593

32

1. Personal information

2.1 Clickstream features: mean of clicks, sum of clicks

Multiclass: withdrawn, fail, pass, and distinction

Classification

Unspecified

2. Behavioral features

3.1 Assessment features: average score, relative score

3. Academic features

[32]

ADC: 52

17

1. University features

1.1 Exercise grades (exercise 1.1 to exercise 3.5.

Multiclass: good, fair, and weak

Classification

Engineering

[32]

486

71

1.2 Tasks grade (first two assignments, first quiz, and midterm exam)

Multiclass: good, fair, and weak

Classification

Science

[33]

Adc: 499

11

1. Personal information

1.1 Gender

Binary class: pass/fail

Classification

Computer

1.2 Date of birth

1.3 Employment

2.1 Course

University features

2.2 Registration

2.3 Activity grade

2.4 Exam grade

[34]

289

3

University features

1.1 Assessment tasks (exercise and homework) grades

Binary class: at-risk/not at-risk

Classification

Computer

[35]

Unspecified

17

1. Personal information

2.1 Student grade level

Multiclass: low, medium, or high

Classification & clustering

Various fields

2.2 No. of days absent

2. University features

2.3 Course title

3.1 Raising hands

3.2 Visiting resources

3. Behavioral features

3.3 Viewing announcement

3.4 Discussion participates

4. Extra features

4.1 Parent answering survey

4.2 Parent school satisfaction

[36]

145

33

1. University features

Five courses grades (PRVT-333, Maj OP-2, PRVT-338, PUBL-220, and PUBL-2223)

—

Regression

Various fields

[37]

101

5

1. Personal information

2.1 National College Entrance Examination (NCEE)

Multiclass: fail, pass, good, or outstanding

Classification

Art (English)

2. Preuniversity features

3.1 Learning attitude

3. Behavioral features

[40]

DS1: 38

23

1. University features

Grades for Course-2, Course-1, Course-5, Course-6, and Course-3

Multiclass: A, B, C, and F

Classification

Computer & engineering

DS2: 273

25

2. Personal information

Age, bachelor GPA, and specialization

[41]

1044

33

1. University features

1.1 Grade of the second term

Binary class: pass/fail

Classification

Mathematics & Portuguese

2. Personal and family features

1.2 Grade of the first term

1.3 No. of fails

3. Social features

1.4 No. of absent days

4. Preuniversity features

2.1 Father’s qualification

[42]

240

10

1. University features

1.1 Test 1 in week 5

Multiclass; high, medium, or low risk

Classification

Engineering

1.2 Test 2 in week 10

[43]

592

19

1. University features

1.1 Grades for eight courses from the first semester

—

Regression

Technology

2. Personal information

1.2 No. of attempts student fails the course

2.1 Gender

[44]

DS1: 262

23

1. University features

1.1 Exam 1

Binary class: fail/pass

Classification

Computer

1.2 Activity of week 5

1.3 Activity of week 2

1.4 Activity of week 4

2 Personal information

1.5 Activity of week 3

1.6 Blog

2.1 City

2.2 Age

3.1 Access

DS2: 161

16

3.2 Exercises

Behavioral features

[45]

225

28

1.Preuniversity features

1.1 Entry test

—

Regression

Engineering

2. University features

1.2 HSSC

[46]

700

26

1. Personal information

1.1 Gender

Multiclass: poor, average, good, or excellent

Classification

Computer

2. Family information

2.1 Parent’s qualification

2.2 Family income

3. Preuniversity features

3.1 Grade in 10^th class

4. University features

3.2 Grade in 12^th class

5. Behavioral features

4.1 Grades in each semester in the program

[5]

339

15

1. Preuniversity features

2.1 Preparatory year’s CGPA

Multiclass: high, average, or below average

Classification

Computer

2.2 Introductory math grade

2. University features

2.3 Computer skills grade

3. Personal information

Communication skills grade.

[6]

530

64

1. Preuniversity features

3.1 operating systems grade

Multiclass: excellent, very good, good, or pass

Classification

Computer

3.2 Statistics grade

2. University features

3.3 General physics grade

3. Personal information

3.4 Computer programming grade

3.5 Algorithms grade

[48]

1841

6

1. University features

1.1 3^rd year GPA

Multiclass: first class, second class upper, second class lower, or third class.

Classification

Engineering

1.2 2^nd year GPA

1.3 1^st year GPA

1.4 enrollment program

1.5 entry year

[49]

2281

7

1. University features

1.1 Previous background

Multiclass: excellent, very good, good, or poor.

Classification

Unspecified

1.2 Type of admission

2. Preuniversity features

2.1 Loan

2.2 Gender

2.3 Talent

[50]

100

Unspecified

1. Preuniversity features

Second-year courses including

Binary class: weak/good GPA

Classification

Technology

2.1 Human computer interaction course 2.2 Information security

2.3 Networks 1

2.4 Data mining

2. University features

2.5 Intelligent systems

2.6 Advanced human computer interaction

2.7 Operating systems

2.8 Networks 2

[51]

957

6

1. Preuniversity features

1.1 High school GPA

Unspecified

Classification

Technology

2. University features

[54]

1491

13

1. Personal information

1.1 Gender

Binary class: pass/fail

Classification

Unspecified

1.2 Age

1.3 Ethnicity

2.1 School system

2.2 Math level

2. Preuniversity features

2.3 English level

2.4 Scholarship

2.5 Transfer status

3. University features

2.6 Admitted on probation

2.7 In dorm

3.1 Program

3.2 Course load

[17]

233

45

1. Personal information

Unspecified

Multiclass: excellent, good, average, or poor

Classification

Engineering & art

2. Learning pattern

3. Behavioral features

4. Emotional and cognitive features

[15]

1,569

4

1. Preuniversity features

1.1 GAT

Multiclass: excellent, very good, good, average, or poor.

Classification

Computer

1.2 SAAT

1.3 High school GPA

[55]

9458

14

1. Personal information

1.1 Gender

Multiclass: low, medium, or good.

Classification

Various fields

1.2 Student status

2. Family information

2.1 Parent status

2.2 Father’s career

2.3 Father’s income

3. Preuniversity features

2.4 Mother’s career

2.5 Mother’s income

4. University features

3.1 Admission year

4.1 Course

4.1 GPA enrolled

[56]

1445

5

1. Personal information

1.1 Entry age

Multiclass: first class, second class upper, second class lower, or third class.

Classification

Engineering

2.1 WAEC score

2. Preuniversity features

2.2 JAMB score

2.3 CUSAS score

[57]

BDC:781

17

1. Preentry information

1.1 Age

—

Regression

Science

1.2 Gender

ADC:519

2. Cognitive variables

1.3 prior education

3. Noncognitive variables

2.1 Math knowledge

2.2 Chemistry knowledge

[58]

ADC:300

9

1. University features

1.1 Second semester’s CGPA

Binary class: at-risk/not at-risk

Classification

Social sciences

Bdc: 708

2. Learning pattern

3. Behavioral features

[16]

161

61

1. Personal information

2.1 Father’s work

Binary class: pass/fail.

Classification

Computer

2. Social features

3. University features

3.1 GPA

4. Health

5. Study skills features

3.2 Number of credits

6. Motivation

7. Relationship

8. Money management

4.1 Healthy food

9. Career planning

[59]

50

Unspecified

1. Personal information

1.1 Application ID

Binary class: Yes/No

Classification

Engineering

1.2 Student name

1.3 Gender

2.1 Second-year GPA

2. University features

2.2 Grade of entrance exam

2.3 Type of admission

[60]

309

4

1. Personal information

1.1 Age

Unspecified

Classification

Science

2. Preuniversity features

1.2 Gender

2.1 High school achievement

[29]

1854

4

1. University features

1.1 Midterm grade

Multiclass: “< 32.5,” “32.5–55,” “55–77.5,” or “≥ 77.5.”

Classification

Art

1.2 Department

1.3 Faculty

[53]

2407

18

1. Personal information

1.1 Mother’s job

Binary class: excellent/poor

Classification

Technical

1.2 Department

1.3 Father’s job

1.4 The main source of living expenses

1.5 The admission status