the sixth international conference on elearningfor knowledge-based society 17-18 december 2009,...

The Sixth International Conference on eLearningfor Knowledge-Based Society17-18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi Campus, Bangkok Metro, Thailand

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Source of Knowledge Blooming Like a LotusKnowledge is the competitive weapon of the 21st century

Intellectual

Professional

Cheerfulness

Morality


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Forecasting Model for the Students’ Job

Turnover in Thai Industries

Pirapat Chantron

Prasong Praneetpolgrang

Master of Science Program in Information TechnologySripatum University, Bangkok, Thailand

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Background of the Research1

Research Objective2

Theories & Related Research 3

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Conclusions 5

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Agenda

Experiments

Future Works


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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.


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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…)


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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…)


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Research Objective

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


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Data Mining

Bayesian Networks

Cross-validation

Evaluation

Theories and Related Research


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Data Mining

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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


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Bayesian Networks

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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.)


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Bayesian Networks (cont.)

Example of Bayesian Networks

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Theories (cont.)

C A

B

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


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Cross-validation

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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.)


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Correct Percentage =

Number of correct classificationTotal number of classifications

Theories (cont.)

Precision =

Recall =

F-measure =

Number of documents relevant and retrievedTotal number of documents that are retrieved

Number of documents relevant and retrievedTotal number of documents that are relevant

2 x Precision x RecallPrecision + Recall

Evaluation in this System


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Related Research

Research in Data Mining TechniquesResearch 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


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Related Research (cont.)

Research in Data Mining Techniques

Research Author Year

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


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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


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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


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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

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Random Sample Size from the Population which based

on Morgan & Krejcie Table



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n = Sample sizeN = Population sizee = The error of sampling

This study allows the error of sampling on 0.05

Formula,)1( 2Ne

Nn



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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.

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.

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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 2,862350



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Company Population (N)

Sampling size(n)

Charoen Pokphand1400 311

Dtac6000 375

Department of Land Transport1370 309

Thai Airways1700 324

Cooperative Auditing Department2400 342

Bangkhen District office850 272

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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

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Data from the total sample set were 2,536



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AttributeDescripti

on

MatchEdu

Function match with the studying field

BROTHERRank order

in the family

STATUSStudent

status

LOCATION Location

DOMICILE Home town

PARENT_STATUS

Parent status

OCC_FATFather

occupation

OCC_MOTMother

occupation

FAM_INCOMEFamily

income

Work ChangeWork

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

PositionPosition of the

job

CompanyType

Company

Salary Job salary rate

GPA_Old GPA

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ATTRIBUTE OF DATASET



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Experimental Results


Work ChangeWork Change

SalarySalaryMajorMajor

PositionPosition

Model of the variable that effect to the work changing.


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== Run information ===Test mode: 10-fold cross-validation=== Classifier model (full training set) ===Naïve Bayes Classifiernot using ADTree=== Summary ===Correctly Classified Instances 2280 97.2634 %Incorrectly Classified Instances 256 2.7366 %Kappa statistic 0.8633Mean absolute error 0.0742Root mean squared error 0.1872Relative 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.



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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


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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


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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.


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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.


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Thank You for your kind attentionThank You for your kind attention

[email protected]@[email protected]

the sixth international conference on elearningfor knowledge-based society 17-18 december 2009,...

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assumption university

bangkok metro

thailand background

international conference

suvarnabhumi campus

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thailand forecasting

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