COURSE UNIT TITLE

: FIELD ELC. 6 (ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS IN EDUCATION )

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
TEK 3011 FIELD ELC. 6 (ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS IN EDUCATION ) ELECTIVE 2 0 0 4

Offered By

Computer and Instructional Technologies Teacher Education

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR KÜRŞAT ARSLAN

Offered to

Computer and Instructional Technologies Teacher Education

Course Objective

The aim of this course is to examine the concepts of intelligence, intelligence and artificial intelligence, and to illustrate their use in education and their applications with examples. In addition, expert systems, learning systems, large data in education, and program development in logical programming are among the main topics of artificial intelligence applications.

Learning Outcomes of the Course Unit

1   Explain the concepts of intelligence and artificial intelligence
2   Explain the history of artificial intelligence
3   Know the structure and components of expert systems
4   Tells the design elements necessary for the use of expert systems in education
5   Knowing the structure and components of learning systems
6   Describes the characteristics of logical programming languages
7   Know a logical programming language at a basic level

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introducing the course (information about the course, introducing the objectives and content), and explaining the rules of the course in detail
2 Intelligence, intelligence tests and disclosure of natural intelligenc
3 Artificial Intelligence (Turing test, Chinenese Room), philosophy underlying artificial intelligence, history of artificial intelligence, artificial intelligence applications (language processing, expert systems, robots, perceptual problems, ...)
4 Objectives and related disciplines of artificial intelligence, artificial intelligence problems
5 Expert systems (components and features of expert systems)
6 Use of expert systems in education
7 Logical Programming (Features of logical programming, logical programming and applications, simple examples, logical programming languages)
8 General Review, Course Evaluation, Midterm Exam
9 Prolog language applications - 1
10 Prolog language applications - 2
11 Prolog language applications - 3
12 Prolog language applications - 4
13 Use of Expert Systems in Education (Application example)
14 Final Exam

Recomended or Required Reading

Ertel, W. & Black, N.T. (2018). Introduction to Artificial Intelligence. Springer International Publishin
AKERKAR, R. (2014). INTRODUCTION TO ARTIFICIAL INTELLIGENCE. PHI Learning g
Kaplan, J. (2016). Artificial Intelligence: What Everyone Needs to Know. Oxford University Press

Planned Learning Activities and Teaching Methods

Lecture, individual and group work.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 VZ Midterm
2 FN Semester final exam
3 BNS BNS Student examVZ * 0.40 + Student examFN * 0.60
4 BUT Make-up note
5 BBN End of make-up grade Student examVZ * 0.40 + Student examBUT * 0.60


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

Regular attendance is expected; a minimum of 70% attendance is required.
Active participation in lab sessions is mandatory.
Cheating, plagiarism, and academic misconduct are not tolerated under any circumstances.
No make-up exams will be given except in documented medical or official cases.
Late assignments may be accepted with a penalty at the instructor s discretion.

Contact Details for the Lecturer(s)

Dr. Kürşat Arslan
Bilgisayar ve Öğretim Teknolojileri Eğitiminde Doçent, BEF, DEU
Uğur Mumcu Cad. 135. Sk. No:5 35380 Buca-IZMIR
kursat.arslan@deu.edu.tr
+902323012064
galloglu.com

Office Hours

-

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 2 26
Preparations before/after weekly lectures 13 5 65
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 95

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.15454435454
LO.25554454545
LO.33555454445
LO.45544543534
LO.55555425453
LO.64545444554
LO.73544545554