Students Performance

For what purpose was the dataset created?

The purpose is to predict students' end-of-term performances using ML techniques.

Additional Information

1-10 of the data are the personal questions, 11-16. questions include family questions, and the remaining questions include education habits.

Class Labels

Student ID

1- Student Age (1: 18-21, 2: 22-25, 3: above 26)

2- Sex (1: female, 2: male)

3- Graduated high-school type: (1: private, 2: state, 3: other)

4- Scholarship type: (1: None, 2: 25%, 3: 50%, 4: 75%, 5: Full)

5- Additional work: (1: Yes, 2: No)

6- Regular artistic or sports activity: (1: Yes, 2: No)

7- Do you have a partner: (1: Yes, 2: No)

8- Total salary if available (1: USD 135-200, 2: USD 201-270, 3: USD 271-340, 4: USD 341-410, 5: above 410)

9- Transportation to the university: (1: Bus, 2: Private car/taxi, 3: bicycle, 4: Other)

10- Accommodation type in Cyprus: (1: rental, 2: dormitory, 3: with family, 4: Other)

11- Mothers’ education: (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.)

12- Fathers’ education: (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.)

13- Number of sisters/brothers (if available): (1: 1, 2:, 2, 3: 3, 4: 4, 5: 5 or above)

14- Parental status: (1: married, 2: divorced, 3: died - one of them or both)

15- Mothers’ occupation: (1: retired, 2: housewife, 3: government officer, 4: private sector employee, 5: self-employment, 6: other)

16- Fathers’ occupation: (1: retired, 2: government officer, 3: private sector employee, 4: self-employment, 5: other)

17- Weekly study hours: (1: None, 2: <5 hours, 3: 6-10 hours, 4: 11-20 hours, 5: more than 20 hours)

18- Reading frequency (non-scientific books/journals): (1: None, 2: Sometimes, 3: Often)

19- Reading frequency (scientific books/journals): (1: None, 2: Sometimes, 3: Often)

20- Attendance to the seminars/conferences related to the department: (1: Yes, 2: No)

21- Impact of your projects/activities on your success: (1: positive, 2: negative, 3: neutral)

22- Attendance to classes (1: always, 2: sometimes, 3: never)

23- Preparation to midterm exams 1: (1: alone, 2: with friends, 3: not applicable)

24- Preparation to midterm exams 2: (1: closest date to the exam, 2: regularly during the semester, 3: never)

25- Taking notes in classes: (1: never, 2: sometimes, 3: always)

26- Listening in classes: (1: never, 2: sometimes, 3: always)

27- Discussion improves my interest and success in the course: (1: never, 2: sometimes, 3: always)

28- Flip-classroom: (1: not useful, 2: useful, 3: not applicable)

29- Cumulative grade point average in the last semester (/4.00): (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49)

30- Expected Cumulative grade point average in the graduation (/4.00): (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49)

31- Course ID

32- OUTPUT Grade (0: Fail, 1: DD, 2: DC, 3: CC, 4: CB, 5: BB, 6: BA, 7: AA)

Citation Requests/Acknowledgements

Yılmaz N., Sekeroglu B. (2020) Student Performance Classification Using Artificial Intelligence Techniques. In: Aliev R., Kacprzyk J., Pedrycz W., Jamshidi M., Babanli M., Sadikoglu F. (eds) 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019. ICSCCW 2019. Advances in Intelligent Systems and Computing, vol 1095. Springer, Cham