The aim of this course is to provide prospective teachers with knowledge and skills in the following subjects.
Intelligence and its characteristics,
History, current status and application areas of artificial intelligence,
Expert systems, usage areas, components, features and design of expert systems, use of expert systems in education,
Intelligent learning systems,
Big data in education,
Learning analytics,
Educational agent,
Adaptive learning and adaptive measurement
Program development in logical programming
20
Contribution of the Course to Professional Development
21
Learning Outcomes:
1
To be able to explain the concept of artificial intelligence.;
2
Being able to identify the structure and components of expert systems.;
3
To explain intelligent learning systems and components;
4
To be able to explain the properties of logical programming languages.;
5
To be able to use a logical programming language at a basic level.;
22
Course Content:
Week
Theoretical
Practical
1
Basic concepts of natural intelligence and artificial intelligence
2
Historical development of artificial intelligence
3
The relationship between natural intelligence and artificial intelligence
4
Expert systems
5
Learning analytics
6
Data mining and its use in education
7
Intelligent teaching systems
8
Educational agent
9
Adaptive learning
10
Programming applications
11
Programming applications
12
Programming applications
13
Programming applications
14
Programming applications
23
Textbooks, References and/or Other Materials:
Vasif Vagifoğlu Nabiyev, Yapay Zeka, 5. baskı, Nisan 2016, Seçkin Yayıncılık. Introduction to Artificial Inteligence, Eugene Charniak, Drew McDermott, Addison-Wesley Pub. Stuart Russell, ?Peter Norvig, Artificial Intelligence: A Modern Approach, Global Edition, Pearson, 2016.
24
Assesment
TERM LEARNING ACTIVITIES
NUMBER
PERCENT
Midterm Exam
1
40
Quiz
0
0
Homeworks, Performances
0
0
Final Exam
1
60
Total
2
100
Contribution of Term (Year) Learning Activities to Success Grade
40
Contribution of Final Exam to Success Grade
60
Total
100
Measurement and Evaluation Techniques Used in the Course
Information
25
ECTS / WORK LOAD TABLE
Activites
NUMBER
TIME [Hour]
Total WorkLoad [Hour]
Theoretical
14
2
28
Practicals/Labs
0
0
0
Self Study and Preparation
12
3
36
Homeworks, Performances
0
0
0
Projects
5
5
25
Field Studies
0
0
0
Midtermexams
1
10
10
Others
0
0
0
Final Exams
1
21
21
Total WorkLoad
130
Total workload/ 30 hr
4
ECTS Credit of the Course
4
26
CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
