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

Table 6

Factors for predicting student’s performance.

Student features’ categories		Student features values	Studies	No. of studies (percentage of occurrence)
Demographic features	Personal information	Gender, age, date of birth, nationality, motivation level, ethnicity, and employment.	[5, 6, 16, 17, 21, 25, 27, 28, 30, 31, 33, 35, 37, 40, 41, 43, 44, 46, 52–57, 59, 60]	26 (60%)
	Family information	Father’s qualification, mother’s qualification, father’s occupation, and mother’s occupation.	[16, 23, 28, 30, 41, 46, 52, 53, 55]	9 (21%)
Academic features	University features	Assessment grades (final exam, project, midterm exam, quizzes, lab grades), student attendance, and CGPA.	[5, 6, 16, 21, 23, 25–29, 31–34, 36, 40–46, 48–52, 54, 55, 58, 59]	31 (72%)
	Preuniversity features	High school courses grades, high school CGPA, preadmission requirement, and entry test.	[5, 6, 15, 27, 28, 37, 41, 45, 46, 49–52, 54–57, 60]	18 (42%)
Extra features	Social and economic features	Impact of friends and family income.	[23, 25, 30, 41, 53]	5 (12%)
	Behavioral features	Discussion participates and self-study time.	[17, 25, 30, 31, 35, 37, 44, 46, 53, 58]	10 (23%)