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Presented by Miss. Rakwarinn Wannasin and Mr.Krittachai Boonsivanon

A Mobile Learning by Decision Tree for Provisional Diagnosis on Smartphone. Presented by Miss. Rakwarinn Wannasin and Mr.Krittachai Boonsivanon. Outline. Background. Related works. Objectives. Methodology. Result. Conclusion.

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Presented by Miss. Rakwarinn Wannasin and Mr.Krittachai Boonsivanon

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  1. A Mobile Learning by Decision Tree for Provisional Diagnosis on Smartphone Presented by Miss. RakwarinnWannasin and Mr.KrittachaiBoonsivanon

  2. Outline Background Related works Objectives Methodology Result Conclusion

  3. ICT(Information and Communication Technology) (Traxler, 2005; Kukulska-Hulme & Shield, 2008)

  4. ICT(Information and Communication Technology) (Garrison& Kanuka, 2004; Masie, 2006; Kumar, 2007 )

  5. E-Learning • An innovation of teaching and learning. (Soh, Park & Chang, 2009) • The students to search and retrieve the information through the computer with low expenses.(TissanaKaemanee, 2004)

  6. E-Learning (Eke, 2011)

  7. The Limitations of E-Learning

  8. Training Methodologies

  9. Mobile phone (Reuters, 2008)

  10. Internet (Miniwatts Marketing Group, 2008)

  11. M-Learning or Mobile Learning (Park, 2011)

  12. The Advantages of M-Learning (Geddes, 2004)

  13. Decision Tree http://www.tuesdayconsultingllc.com/decision-tree-model-vs-effective-delegation/ http://sasdkmitl09.blogspot.com/2009/07/blog-post_23.html

  14. Related works Ensemble decision tree classifier for breast cancer data. (D.Lavanya & Dr.K.Usha Rani, 2012.) Cost effectiveness of outpatient treatment for febrile neutropaenia in adult cancer patients. (Oteuffel et al.,2011) The application of decision tree inthe research  of anemia among rural children under 3-year-old (Zhonghua Yu Fang Yi Xue Za Zhi, 2009.) Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness. (Lukas Tanner et al.,2008)

  15. Objectives To study the result before and after studying decision-tree via smartphone to provisional diagnose 20 diseases. To develop and improve mobile learning to provisional diagnose for basic Traditional Thai Medicine.

  16. Methodology • Experimental set-up • Sampling: • 85 first-year Thai Traditional Medicine students. Group 3 M-Learning • 45 persons Group 1 Not yet learning 20 persons Group 2 General class room activities • 20 persons

  17. Methodology • Experimental set-up • Hardware and software: • Xcode software ,SQLite and iOS Simulator • Running under Apple iOS,iPhone platform

  18. Methodology • Implementation: • M-learning programming: Java and Decision tree algorithm. • Database: Xcode and SQLite • Contents based on: 10-012-203 Thai Traditional medicine1 • Title:“Provisional diagnosis”.

  19. Methodology Pre-test Pre-test Group 1 Not yet learning Group2 General Class room activities Group3 M-Learning

  20. Methodology Post-test T-test was used to analyze the data and compare the student’s learning achievement. 1.General learning method 2.M-Learning method Group 1 Not yet learning Group2 General Class room activities Group3 M-Learning

  21. Result of General Learning

  22. Result of Learning M-Learning 24. 7%

  23. Result of General Learning and M-Learning @ * @Represented a significant different when compared to the control. * Represented a significant different when compared to the general learning. @ General Learning M-Learning

  24. Result of Learning M-Learning

  25. Result of Learning M-Learning

  26. AChE ACh Choline + acetate Cholinergic pathway - ACh Anticholinesterase Acetylcholinesterase inhibitors Discussion Memory ?

  27. Conclusion The results of this study demonstrated that the learning through mobile learning score could significantly enhance ability provisional diagnose through mobile learning by the decision-tree in the first year Traditional Thai Medicine students.

  28. Thank you for your attention Miss. RakwarinnWannasin Lecturer, Dept. Traditional Thai Medicine, Faculty of  Natural Resources, Rajamangala University of Technology IsanSakonnakhon Campus,Thailand. Tel: 087-4499332 Email: rakwarinn@outlook.com Mr. KrittachaiBoonsivanon Lecturer, Dept. Computer Engineering, Faculty of Creative Industry, KalasinRajabhatUniversity,Thailand. Tel: 087-4236374 Email: krittachai@fci.ksu.ac.th

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