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The Sixth International Conference on eLearningfor Knowledge-Based Society 17-18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi.

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Presentation on theme: "The Sixth International Conference on eLearningfor Knowledge-Based Society 17-18 December 2009, Srisakdi Charmonman IT Center,Assumption University,Suvarnabhumi."— Presentation transcript:

1 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 Intellectual Professional Cheerfulness Morality

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 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

3 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

4 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.

5 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…)

6 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 didnt fit with them. It is too late for them to start again. Background of the Research (Cont…)

7 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.

8 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

9 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

10 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.)

11 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)

12 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.)

13 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

14 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 ResearchAuthorYearMethod Prediction of Higher Education Students Graduation with Bayesian Learning and Data Mining Yingkuachat et al2006Bayesian 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

15 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 ResearchAuthorYea 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 al2001association rule, decision tree

16 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

17 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

18 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 sizePopulationSample sizePopulationSample size 10 45408066 151450448570 201955489073 252460529576 3028655610080 3532705911086 4036756312092 18 Random Sample Size from the Population which based on Morgan & Krejcie Table Research Experiments (cont.)

19 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 19 n = Sample size N= Population size e= The error of sampling This study allows the error of sampling on 0.05 Formula, Research Experiments (cont.)

20 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.)

21 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 University10,558385 Phranakhon Rajabhat University 4,358366 Thepsatri Rajabhat University 1,936331 Sripatum University4,820369 Dhurakij Pundit University 3,400358 Saint john's University2,862 350 Research Experiments (cont.)

22 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 1400311 Dtac 6000375 Department of Land Transport 1370309 Thai Airways 1700324 Cooperative Auditing Department 2400342 Bangkhen District office 850272 22 Research Experiments (cont.)

23 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 Universitiessample Kasetsart University237 Phranakhon Rajabhat University 241 Thepsatri Rajabhat University228 Sripatum University245 Dhurakij Pundit University242 Saint john's University238 Total1431 Company sample Charoen Pokphand270 Dtac130 Department of Land Transport 190 Thai Airways252 Cooperative Auditing Department 137 Bangkhen District office126 Total1105 23 Data from the total sample set were 2,536 Research Experiments (cont.)

24 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 AttributeDescription MatchEdu Function match with the studying field BROTHER Rank order in the family STATUSStudent status LOCATIONLocation DOMICILEHome town PARENT_STATUS Parent status OCC_FAT Father occupation OCC_MOT Mother occupation FAM_INCOMEFamily income Work Change Work Changing Attribut e Description Gender Uni Type University Type Major Field of Education GpaLevel Accumulate Grade point average at the last semester TimeFindW ork Period of experience Position Position of the job CompanyT ype Company Salary Job salary rate GPA_Old GPA 24 ATTRIBUTE OF DATASET Research Experiments (cont.)

25 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.

26 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.)

27 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

28 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

29 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.

30 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.

31 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


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