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

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

A new student performance analysing system using knowledge discovery in higher educational databases

Presenter : Wun-Huei Su

Authors : Huseyin Guruler , Ayhan Istanbullu ,

Mehmet Karahasan

CE 2010

國立雲林科技大學National Yunlin University of Science and Technology

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N.Y.U.S.T.

I. M.Outline

Motivation Objective Method Results Conclusion Comments

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N.Y.U.S.T.

I. M.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.

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N.Y.U.S.T.

I. M.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.

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N.Y.U.S.T.

I. M.Method

Knowledge discovery process

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N.Y.U.S.T.

I. M.Method

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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


N.Y.U.S.T.

I. M.Method

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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.


N.Y.U.S.T.

I. M.Method

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Architecture of MUSKUP


N.Y.U.S.T.

I. M.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

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N.Y.U.S.T.


Columns used in modeling and decision tree views

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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.

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N.Y.U.S.T.

I. M.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

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N.Y.U.S.T.

I. M.Comments

Advantage

Drawback

Application KDD and DM in higher educational system

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

Documents

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