Transcript Moments of Truth with Jesus

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
ICT
(Information and Communication Technology)
(Traxler, 2005; Kukulska-Hulme & Shield, 2008)
ICT
(Information and Communication Technology)
(Garrison & Kanuka, 2004; Masie, 2006; Kumar, 2007 )
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. (Tissana Kaemanee, 2004)
E-Learning
(Eke, 2011)
The Limitations of E-Learning
Training Methodologies
Mobile phone
(Reuters, 2008)
Internet
(Miniwatts Marketing Group, 2008)
M-Learning or Mobile Learning
(Park, 2011)
The Advantages of M-Learning
(Geddes, 2004)
Decision Tree
http://www.tuesdayconsultingllc.com/decision-tree-model-vseffective-delegation/
http://sasdkmitl09.blogspot.com/2009/07/blog-post_23.html
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)
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.
Methodology
• Experimental set-up
• Sampling:
•
85 first-year Thai Traditional Medicine
students.
Group 1
Not yet
learning
20 persons
Group 3
M-Learning
Group 2
General
class room
activities
20 persons
45 persons
Methodology
• Experimental set-up
• Hardware and software:
•
•
Xcode software ,SQLite and iOS Simulator
Running under Apple iOS, iPhone platform
Methodology
• Implementation:
•M-learning programming: Java and Decision tree algorithm.
•Database: Xcode and SQLite
•Contents based on: 10-012-203 Thai Traditional medicine 1
•Title:“Provisional diagnosis”.
Methodology
Pre-test
Pre-test
Group 1
Not yet
learning
Group2
General
Class room
activities
Group3
MLearning
Methodology
Post-test
T-test was used to analyze the data and compare the student’s
learning achievement.
1.General
learning
method
Group 1
Not yet
learning
Group2
General
Class room
activities
2.M-Learning
method
Group3
MLearning
Result of General Learning
Result of Learning M-Learning
24. 7%
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
Result of Learning M-Learning
Result of Learning M-Learning
Acetylcholinesterase inhibitors
Discussion
Cholinergic pathway - ACh
ACh
AChE
Choline
+ acetate
Anticholinesterase
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.
Thank you for your attention
Miss. Rakwarinn Wannasin
Lecturer, Dept. Traditional Thai Medicine, Faculty of Natural Resources,
Rajamangala University of Technology Isan Sakonnakhon Campus,Thailand.
Tel: 087-4499332
Email: [email protected]
Mr. Krittachai Boonsivanon
Lecturer, Dept. Computer Engineering, Faculty of Creative Industry,
Kalasin Rajabhat University,Thailand.
Tel: 087-4236374
Email: [email protected]