SourceofKnowledgeBloomingLike a Lotus

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The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












Source

of

Knowledge

Blooming

Like

a
Lotus

Knowledge is the competitive weapon of the 21
st

century


I
ntellectual


Professional

C
heerfulness


Morality


The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand













Forecasting Model for the Students’ Job
Turnover in Thai Industries


Pirapat

Chantron

Prasong

Praneetpolgrang



Master of Science Program in Information Technology

Sripatum

University, Bangkok, Thailand


2

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand













Background of the Research

1

Research Objective

2

Theories &
Related Research


3

4

Conclusions


5

6

Agenda

Experiments


Future Works

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












4


A number of students transfer their majors
of studies or change their majors, drop or
resign from the university.



Background of the Research

Many students in the university are not
aware whether they should choose to study,
any field of studies that match for them in
order to work directly with their interests.

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












5

After graduating from the university and
get into work, a number of students change
their work or resign for the reasons that
they cannot find the appropriate or proper
work with their major of studies or their
interests.


Background of the Research (Cont…)

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












6


These are the reasons that students do not
have experience and lack of information in
their majors of studies. They unknown
individual disciplines well enough, and
they found afterward that their studies or
their majors and their work didn’t fit with
them. It is too late for them to start again.


Background of the Research (Cont…)

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand














Research Objective


The purpose of this study is to develop
forecasting model for the students’ job
turnover in Thai industries.

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












8



Data Mining



Bayesian Networks



Cross
-
validation



Evaluation

Theories and
Related Research


The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












Data Mining

9




Data mining technique is based on
statistical analysis, it has been used in
finding and describing structural
patterns in data segmentation and
predictions (
Witten and Frank,2005
).



This technique has been applied
extensively in many industries including
banking and finances, education,
medical sciences and manufacturing.


Theories

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












Bayesian Networks

10




Specific type of graphical model


which is a directed acyclic graph (
Kijsirikul,2003
).



All of the edges in the graph are directed and
there are no cycles.



Used as a classifier that gives the posterior
probability distribution of the class node given the
values of other attributes
.

Theories
(cont.)


The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












Bayesian Networks
(cont.)





Example of Bayesian Networks



11

Theories
(cont.)



C


A


B

P
(
A
,
B
,
C
) =
P
(
A
|
B
)


P
(
B
)


P
(
C
|
B
)

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












Cross
-
validation

12




Some of the data are removed before
training begins.



When training is done, the data that
were removed can be used to test the
performance of the learned model.



The Data set is separated into two sets,
called the training set and the testing set.


Theories
(cont.)


The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












13




Correct Percentage
=








Number of correct classification

Total number of classifications

Theories
(cont.)



Precision =



Recall


=



F
-
measure

=

Number of documents relevant and retrieved

Total number of documents that are retrieved

Number of documents relevant and retrieved

Total number of documents that are relevant

2 x Precision x Recall

Precision + Recall

Evaluation in this System

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












14



Related Research

Research in Data Mining Techniques

Research

Author

Year

Method

Prediction of Higher Education
Students’ Graduation with
Bayesian Learning and Data
Mining


Yingkuachat

et al


2006

Bayesian
Networks

Course Planning of extension
education to meet market
demand by using data mining
techniques
-
an example of a
university in Taiwan


Hsia et al.


2008

Decision Tree,
Association
rules, and
Decision Forest

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












15


Related Research
(cont.)


Research in Data Mining Techniques

Research

Author

Yea
r

Method

Evaluating Bayesian
networks’ precision for
detecting students’
learning styles


Garcia et al.


2007

Bayesian Networks

Data Mining Techniques
for Developing Education
in Faculty

of
Engineering

Waiyamai

et al

2001

association rule
,

decision tree

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












16

Student

Database

Data Pre
-
processing

Bayesian

Networks


Model

1

2

3

System Framework for the research methodology

Data Pre
-
processing


Post
-
processing


Data Mining


Research Experiments

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












17


Data mining techniques (Data Mining) were used in
this research to create a relationship model between
their majors, having and changing their jobs of
persons in public and private organizations by
studying from academic performance, profiles, and
work background. Data from the total sample set
were 2,536.


The table of
Krejcie and Morgan was used to define
the sample size






Research Experiments
(cont.)

Dataset

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












Population



Sample size

Population

Sample size

Population

Sample size

10

10

45

40

80

66

15

14

50

44

85

70

20

19

55

48

90

73

25

24

60

52

95

76

30

28

65

56

100

80

35

32

70

59

110

86

40

36

75

63

120

92

18


Random Sample Size from the Population which based


on Morgan & Krejcie Table




Research Experiments
(cont
.
)

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand














POPULATION OF THIS STUDY WILL BE REFERRED WITH THE
CALCULATING METHOD OF TARO YAMANE

19


n

=
Sample size


N

=
Population size


e

=
The error of sampling


This study allows the error of sampling on 0.05


Formula
,








)
1
(
2
Ne
N
n


Research Experiments
(cont
.
)

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












Data were used in this study and the modeling consisted of:


-

Information from 6 universities: 3 public and 3 private universities,
Kasetsart University. Rajabhat PranakonUniversity, Rajabhat Lopburi
University and private universities including Sripatum University, Durakit
Bundit University and Saint John's University.


-

Data from 6 organizations: The CP (Research and Development), The
DTAC,


The Department of Transportation,


Thai International Airways
(Aviation Management),


the Department of Cooperative, The Auditing Office
and the Office of Bangkhen District Office and The Office of Disease
Prevention area 1.


20

Research Experiments
(cont
.
)

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












21

Universities



Population

(N)


Sampling Size


(n)

Kasetsart University

10,558

385

Phranakhon Rajabhat
University

4,358

366

Thepsatri Rajabhat
University

1,936

331

Sripatum University

4,820

369

Dhurakij Pundit
University

3,400

358

Saint john's University

2,862

350

Research Experiments
(cont
.
)

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












22


Company



Population

(N)


Sampling size

(n)

Charoen Pokphand


1400


311

Dtac


6000


375

Department of Land Transport


1370


309

Thai Airways


1700


324

Cooperative Auditing Department


2400


342

Bangkhen

District office


850


272

22

Research Experiments
(cont
.
)

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












23

Universities

sample

Kasetsart University

237

Phranakhon

Rajabhat

University

241

Thepsatri Rajabhat University

228

Sripatum University

245

Dhurakij Pundit University

242

Saint john's University

238

Total

1431

Company

sample

Charoen Pokphand

270

Dtac

130

Department of Land
Transport

190

Thai Airways

252

Cooperative Auditing
Department

137

Bangkhen District office

126

Total

1105

23

Data from the total sample set were

2,536



Research Experiments
(cont
.
)

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












Attribute

Description

MatchEdu

Function match with the
studying field

BROTHER

Rank order in the family

STATUS

Student status

LOCATION

Location

DOMICILE

Home town

PARENT_STATUS

Parent status

OCC_FAT

Father occupation

OCC_MOT

Mother occupation

FAM_INCOME

Family income

Work Change

Work Changing

Attribute

Description

Gender

Gender

Uni Type


University Type

Major

Field of Education

GpaLevel

Accumulate Grade point average at
the last semester

TimeFindWork

Period of experience

Position

Position of the job

CompanyType

Company

Salary

Job salary rate

GPA_Old

GPA

24

ATTRIBUTE OF DATASET

Research Experiments
(cont.)

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












Experimental Results

Research Experiments
(cont.)


Work Change



Salary



Major



Position


Model of the variable that effect to the work changing.

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












26

== Run information ===

Test mode: 10
-
fold cross
-
validation

=== Classifier model (full training set) ===

Naïve Bayes Classifier

not using ADTree

=== Summary ===

Correctly Classified Instances 2280 97.2634 %

Incorrectly Classified Instances 256 2.7366 %

Kappa statistic



0.8633

Mean absolute error 0.0742

Root mean squared error 0.1872

Relative absolute error 25.1745 %

Root relative squared error 48.9402 %

Total Number of Instances 2536.0000




The predicting model for work changing was constructed in order to prove the accuracy
of data mining technique by using Bayesian Networks. The result indicated that the
accuracy was 97.26%. This study suggests the graduated student to used the factors that
effect to his working, those are field of study, Major, Position and Salary. These variables
are suitable for model constructing to predict the changing of work opportunity.


Research Experiments
(cont.)

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












27


In conclusion, it was found that variables
effect the description of the factors affecting
the change of the job: major, position of the
job and job salary rate.

CONCLUSION

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand













Applying data mining technique for prediction.


In order to increase the prediction power of


classification, alternative feature selection might be



applied to select importance attributes before


classification.



Increase sampling size in the next research, include


universities sampling and organizations in order to


develop the model more effectively
.

Future works

The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












References




[1] K. Waiyamai, T. Rakthanmanon and C. ngsiri, “Data Mining Techniques for


Developing Education in Engineering Faculty,” NECTEC Technical Journal,


volume III, no.11, 2001, pp. 134
-
142.



[2] B. Kijsirikul, Artificial Intelligence, Department of Computer Engineering,


Faculty of Engineering, Chulalongkorn


University, 2003.



[3] J. Yingkuachat, B. Kijsirikul and P. Praneetpolgrang, “A Prediction of higher


Education Students’ Graduation with Bayesian Learning and Data Mining,” in


Research and Innovations for Sustainable Development Conference, 2006.



[4] T. Hsia, A. Shie and L. Chen, “Course Planning of extension education to meet


market demand by using data mining techniques
-
an example of chinkuo


technology university in Taiwan,” Expert Systems with Applications, volume 34,


Issue 1, 2008, pp. 596
-
602.





The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












References
(cont.)




[5] I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and


Techniques, Second Edition, Morgan Kaufmann, San Francisco, 2005.



[6] WEKA, http://www.cs.waikato.ac.nz/ml/weka, 17 September 2007.




[7] P. Garcia, A. Amandi, S. Schiaffino and M.Campo, “Evaluating Bayesian


networks’ precision for detecting students’ learning styles,”Computer &


Education, Volume 49, Issue 3, 2007, pp. 794
-
808.




[8] M. Xenos, “Prediction and assessment of student behaviour in open and distance


education in computers using Bayesian networks,” Computer & Education,


Volume 43, Issue 4, 2004, pp. 345
-
359.


The Sixth International Conference on eLearningfor Knowledge
-
Based Society

17
-
18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand


Master of Science Program in Information Technology ,
Sripatum

University, Bangkok, Thailand












Thank You for your kind attention

Parinya.ch@spu.ac.th

Prasong.pr@spu.ac.th