COURSE UNIT TITLE

: ARTIFICIAL INTELLIGENCE APPLICATIONS IN EDUCATION

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
BTE 5003 ARTIFICIAL INTELLIGENCE 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 intelligence
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

This basic instructional approach is based on narrative technique. Teaching of theoretical knowledge through presentations by the responsible teaching staff is the basic teaching method. In addition, an activity-based teaching method will be used to develop a small-scale artificial intelligence application in education for better understanding of artificial intelligence applications.

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


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Examination, Oral exam, Homework and Performance Task

Language of Instruction

Turkish

Course Policies and Rules

It is not compulsory to attend classes during the semester. If the evaluation is done through the project, 10% points can be deducted per day for the assignments submitted after the date announced by the course instructor. Weekly assignments or projects delivered over a total of 4 days will not be considered. On the other hand, the course instructor may consider giving up bonus points of up to 10% to students for positive situations such as positive and active participation, regular attendance to classes and participation in class activities.

Contact Details for the Lecturer(s)

Dr. Kürşat ARSLAN
Dokuz Eylül Üniversitesi
Bilgisayar ve Öğretim Teknolojileri Eğitimi Bölümü
Buca Eğitim Fakültesi-Sosyal Bina, Izmir, Türkiye
Email: kursat.arslan@deu.edu.tr (http://www.galloglu.com/)
Telefon: 0232 3012136
Oda: 207

Office Hours

not defined

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 2 26
Preparations before/after weekly lectures 13 1 13
Preparation for midterm exam 1 3 3
Preparation for final exam 1 3 3
Preparing presentations 13 3 39
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 88

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.11311131311
LO.21111131311
LO.31411141111
LO.41411141311
LO.51411141111
LO.61411141111
LO.71411141111

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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