GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
OPTIONAL ISSUES IN INSTRUCTIONAL TECHNOLOGIES/4960222
Course Title: OPTIONAL ISSUES IN INSTRUCTIONAL TECHNOLOGIES
Credits 3 ECTS 8
Semester 2 Compulsory/Elective Elective
COURSE INFO
 -- LANGUAGE OF INSTRUCTION
  Türkçe
 -- NAME OF LECTURER(S)
  Assoc.Prof. Tolga GÜYER
 -- WEB SITE(S) OF LECTURER(S)
  www.tolgaguyer.com
 -- EMAIL(S) OF LECTURER(S)
  guyer@gazi.edu.tr
 -- LEARNING OUTCOMES OF THE COURSE UNIT
From each student who completed this course will be expected to write an original research article about at least one of the topics discussed.








 -- MODE OF DELIVERY
  The mode of delivery of this course is Face to face
 -- PREREQUISITES AND CO-REQUISITES
  Basic mathematics and probability skills
 -- RECOMMENDED OPTIONAL PROGRAMME COMPONENTS
  There is no recommended optional programme component for this course.
 --COURSE CONTENT
1. Week  Introduction to the probability theory and conditional probability concept
2. Week  Introduction to the probability theory and conditional probability concept
3. Week  Bayes theorem
4. Week  Bayes theorem
5. Week  Bayesian networks
6. Week  Bayesian networks
7. Week  Bayesian networks
8. Week  Applications of the Bayesian networks to the adaptive hypermedia
9. Week  Applications of the Bayesian networks to the adaptive hypermedia
10. Week  Applications of the Bayesian networks to the adaptive hypermedia
11. Week  Applications of the Bayesian networks to the adaptive hypermedia
12. Week  Sample application-1
13. Week  Sample application-2
14. Week  Sample application-3
15. Week  Sample application-4
16. Week  Sample application-5
 -- RECOMMENDED OR REQUIRED READING
  1. Bayesian Networks and Influence Diagrams - A Guide to Construction and Analysis, Kjærulff, U. B. and Madsen, A. L., Springer, 2008. 2. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists, Lynch, S. M., Springer, 2007. 3. Learning Bayesian Networks, Neapolitan, R. E. 4. The Bayesian Choice - From Decision-Theoretic Foundations to Computational Implementation, Robert, C. P., Springer, 2007. 5. Yapay Öğrenme, Alpaydın, E., Boğaziçi Üniversitesi Yayınevi, 2011. 6. Bulanık Mantık – İlke ve Temeller, Baykal, N., Beyan, T., Bıçaklar Kitabevi, 2004. 7. Bulanık Mantık – Uzman Sistemler ve Denetleyiciler, Baykal, N., Beyan, T., Bıçaklar Kitabevi, 2004.
 -- PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
  Lecture, Question & Answer, Demonstration, Drill - Practise
 -- WORK PLACEMENT(S)
  -
 -- ASSESSMENT METHODS AND CRITERIA
 
Quantity
Percentage
 Mid-terms
1
40
 Assignment
1
20
 Exercises
1
20
 Projects
1
20
 Practice
0
0
 Quiz
0
0
 Contribution of In-term Studies to Overall Grade  
40
 Contribution of Final Examination to Overall Grade  
60
 -- WORKLOAD
 Efficiency  Total Week Count  Weekly Duration (in hour)  Total Workload in Semester
 Theoretical Study Hours of Course Per Week
14
3
42
 Practising Hours of Course Per Week
0
0
0
 Reading
12
3
36
 Searching in Internet and Library
12
2
24
 Designing and Applying Materials
6
4
24
 Preparing Reports
8
2
16
 Preparing Presentation
6
3
18
 Presentation
1
1
1
 Mid-Term and Studying for Mid-Term
7
2
14
 Final and Studying for Final
12
2
24
 Other
0
0
0
 TOTAL WORKLOAD: 
199
 TOTAL WORKLOAD / 25: 
7.96
 ECTS: 
8
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
PROGRAM LEARNING OUTCOMES
1
2
3
4
5
1X
2X
3X
4X
5
6X
7
8X
9X
10
11
12
13
14
15
16
17