Presenter : Wun-Huei Su Authors : Huseyin Guruler , Ayhan Istanbullu ,

1. A new student performance analysing system using knowledge discovery in higher educational databases Presenter : Wun-Huei Su Authors :Huseyin Guruler , Ayhan Istanbullu , Mehmet Karahasan 國立雲林科技大學 National Yunlin University of Science and Technology CE 2010

2. Outline • Motivation • Objective • Method • Results • Conclusion • Comments

3. Motivation • Recently the discovery methodologies were used to enhance and evaluate the higher education tasks. • Universities record data containing valuable information about students, which are usually only used individually and for official uses • In this direction, some models have been proposed and implemented.

4. Objective • Knowledge discovery was conducted on the available databases to evaluate the current academic standings of the students and to make viable predictions for the future. • A system that integrates the knowledge discovery process with the Database Management System (DBMS) • Information is obtained by using students’ demographical data.

5. Method • Knowledge discovery process

6. Method • discover individual student characteristics that are associated with their success by using a Microsoft Decision Trees. • Step1:Preparation of database • knowledge discovery research was conducted on demographical data of the students • Student data obtained from the database of the directory of student affairs of the university consist of many tables • These are: the registered information for state, high school information, Turkish university entrance exam degree and university placement information, family’s living conditions and financial status. • GPAs of the students are the best indicators for the success level of the education, was used as target column

7. Method Setp2:MUSKUP (Mugla University Student Knowledge discovery Unit Program) a task sharing mechanism between SQL server and Analysis Services has been developed so as to implement the tasks involved in every individual KDD step.

8. Method Architecture of MUSKUP

9. Results of knowledge discovery in databases • Correlations of some of the input columns with target columns • the values of ±0.01 was accepted to be the lowest limit in the correlation matrix 9

10. Results of knowledge discovery in databases • Columns used in modeling and decision tree views 10

11. Results of knowledge discovery in databases • Model validation and lift graphics • Accordingly the lift value becomes 87/50 = 1.74 for Model I and 68/50 = 1.36 for Model II. • This indicates that these models have prediction capacity. 11

12. Conclusion • The paper presents a knowledge discovery applied on university students’ demographical data • In order to explore the factors having impact on the success of university students, MUSKUP has been developed and tested on this data • The classifications attempt to find out which demographic data is most influential on student GPA. • In checking performances of the models, lift graphics were used 12

13. Comments • Advantage • Drawback • Application • KDD and DM in higher educational system