a mobile learning by decision tree for provisional diagnosis on smartphone presented by miss....
TRANSCRIPT
A Mobile Learning by Decision Tree
for Provisional Diagnosis on Smartphone
A Mobile Learning by Decision Tree
for Provisional Diagnosis on Smartphone
Presented byMiss. Rakwarinn Wannasin and
Mr.Krittachai Boonsivanon
Presented byMiss. Rakwarinn Wannasin and
Mr.Krittachai Boonsivanon
OutlineOutline
Background
Related works
Objectives
Methodology
Result
Conclusion
(Traxler, 2005; Kukulska-Hulme & Shield, 2008)
ICT (Information and Communication Technology)
ICT (Information and Communication Technology)
ICT (Information and Communication Technology)
ICT (Information and Communication Technology)
(Garrison & Kanuka, 2004; Masie, 2006; Kumar, 2007 )
• An innovation of teaching and learning. (Soh, Park & Chang, 2009)
E-LearningE-Learning
• The students to search and retrieve the information through the computer with low expenses. (Tissana Kaemanee, 2004)
E-LearningE-Learning
(Eke, 2011)
The Limitations of E-LearningThe Limitations of E-Learning
Training Methodologies
Training Methodologies
Mobile phoneMobile phone
(Reuters, 2008)
InternetInternet
(Miniwatts Marketing Group, 2008)
M-Learning or Mobile LearningM-Learning or Mobile Learning
(Park, 2011)
The Advantages of M-LearningThe Advantages of M-Learning
(Geddes, 2004)
Decision TreeDecision Tree
http://www.tuesdayconsultingllc.com/decision-tree-model-vs-effective-delegation/
http://sasdkmitl09.blogspot.com/2009/07/blog-post_23.html
The application of decision tree inthe research of anemia among rural children under 3-year-old
(Zhonghua Yu Fang Yi Xue Za Zhi, 2009.)
Ensemble decision tree classifier for breast cancer data. (D.Lavanya & Dr.K.Usha Rani, 2012.)
(Oteuffel et al., 2011)
(Lukas Tanner et al., 2008)
Cost effectiveness of outpatient treatment for febrileneutropaenia in adult cancer patients.
Decision tree algorithms predict the diagnosis andoutcome of dengue fever in the early phase of illness.
Related works
Objectives
Objectives
To develop and improve mobile learning to provisional diagnose for basic Traditional Thai Medicine.
To study the result before and after studying decision-tree via smartphone to provisional diagnose 20 diseases.
Methodology
Methodology
Group 1Not yet learning
20 persons
Group 2General
class roomactivities20 persons
Group 3M-Learning
45 persons
• Experimental set-up• Sampling:
• 85 first-year Thai Traditional Medicine students.
Methodology
Methodology
• Experimental set-up• Hardware and
software:• Xcode
software ,SQLite and iOS Simulator
• Running under Apple iOS, iPhone platform
Methodology
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
Methodology
Pre
-tes
t Group 1
Not yet learning
Group2GeneralClass room
activities
Group3
M-Learning
Pre-testPre-test
2.M-Learningmethod
1.General learningmethod
Methodology
Methodology
Group 1Not yet learning
Group2General
Class room
activities
Group3M-
Learning
T-test was used to analyze the data and compare the student’s learning achievement.
Post-testPost-test
Result of General Learning
Result of General Learning
Result of Learning M-Learning
Result of Learning M-Learning
24. 7%
Result of General Learning and M-Learning
Result of General Learning and M-Learning
General Learning
M-Learni
ng
@
@@ Represented a significant different when compared to the control.
* Represented a significant different when compared to the general learning.
*
Result of Learning M-Learning
Result of Learning M-Learning
Result of Learning M-Learning
Result of Learning M-Learning
Cholinergic pathway - ACh
ACh Choline + acetate
AChE
Acetylcholinesterase inhibitors
Anticholinesterase
Discussion
ConclusionConclusion
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 WannasinLecturer, Dept. Traditional Thai Medicine, Faculty of Natural Resources,Rajamangala University of Technology
Isan Sakonnakhon Campus,Thailand.Tel: 087-4499332Email: [email protected]
Miss. Rakwarinn WannasinLecturer, Dept. Traditional Thai Medicine, Faculty of Natural Resources,Rajamangala University of Technology
Isan Sakonnakhon Campus,Thailand.Tel: 087-4499332Email: [email protected]
Mr. Krittachai BoonsivanonLecturer, Dept. Computer Engineering, Faculty of Creative Industry,Kalasin Rajabhat University,Thailand.Tel: 087-4236374Email: [email protected]
Mr. Krittachai BoonsivanonLecturer, Dept. Computer Engineering, Faculty of Creative Industry,Kalasin Rajabhat University,Thailand.Tel: 087-4236374Email: [email protected]