SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code Course Name Year Semester Theoretical Practical Credit ECTS
YTN5209 Türk Mitolojisi 2 Fall 3 0 3 6
Course Type : University Elective
Cycle: Associate      TQF-HE:5. Master`s Degree      QF-EHEA:Short Cycle      EQF-LLL:5. Master`s Degree
Language of Instruction: Turkish
Prerequisities and Co-requisities: N/A
Mode of Delivery:
Name of Coordinator: Instructor ZEHRA KAYA
Dersin Öğretim Eleman(lar)ı: Instructor ZEHRA KAYA
Dersin Kategorisi: Competency Development (University Elective)

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives: This course aims to introduce the fundamental concepts, divine beings, heroic narratives, and symbolic representations of Turkish mythology, enabling students to understand mythological thought within its historical and cultural contexts and to evaluate this heritage in the fields of contemporary art, literature, and popular culture.
Course Content: The course covers the conceptual foundations of Turkish mythology, including divine beings, shamanism, nature and animal cults, heroic narratives, and mythic images of space and time. It concludes with the founding myths of Turkic tribes and the reflections of these narratives in art, literature, and popular culture.

Course Learning Outcomes (CLOs)

Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:
Knowledge (Described as Theoritical and/or Factual Knowledge.)
Skills (Describe as Cognitive and/or Practical Skills.)
  1) Illustrate and interpret nature and animal cults, zoomorphic images, and female figures.
Competences (Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)
  1) Define the fundamental concepts, belief systems, and symbolic narratives of Turkish mythology.
  2) Explain divine beings such as Gök Tanrı, Umay, and Erlik, as well as rituals related to shamanism.
  3) Analyze heroic narratives (e.g., Oğuz Kağan, Alp Er Tunga, Manas) and conduct comparative evaluations.
  4) Interpret the founding myths of Turkic tribes (such as tamga, dream, and color) within the context of cultural identity.

Weekly Course Schedule

Week Subject
Materials Sharing *
Related Preparation Further Study
1) Giriş ve Kavramsal Çerçeve
2) Türk Mitolojisinin Temel Özellikleri
3) İlahi Varlıkların Tasviri: Gök Tanrı, Umay ve Erlik
4) Şaman, Kam ve Mitik Yolculuk İmgeleri
5) Doğa Kültleri ve Peyzajın Mitik Anlamı
6) Türk İmgeleminde Zoomorfik Temsil
7) Kadın Figürleri ve Dişil Mitoloji
8) Midterm Exam
9) Ölüm, Ruh ve Öte Dünya Temsilleri
10) Kahramanlar I – Oğuz Kağan ve Görsel Motifleri
11) Heroes 2- Alp Er Tunga, Manas, and Others
12) Mythic Spaces and Images of Time
13) Totemic Myths among Turkic Tribes- Tamga, Dream, and Color
14) Reflections of Turkish Mythology in Art and Popular Culture
*These fields provides students with course materials for their pre- and further study before and after the course delivered.

Recommended or Required Reading & Other Learning Resources/Tools

Course Notes / Textbooks:
References: Türk Mitolojisi, Bahattin ÖGEL, TTK.
Türk Mitolojisinin Ana Hatları, Yaşar ÇORUHLU, Özgür Yy.
Ana Hatlarıyla Türk Şamanlığı, Fuzuli BAYAT. Ötüken Yy.
Ruh YolculuklarınınŞematik Haritaları, Olard DIKSON, Gece Yy.

DERS ÖĞRENME ÇIKTILARI - PROGRAM ÖĞRENME ÇIKTILARI İLİŞKİSİ

Contribution of The Course Unit To The Programme Learning Outcomes

Ders Öğrenme Çıktıları (DÖÇ)

1

2

4

5

3

Program Öğrenme Çıktıları (PÖÇ)
1) It explains fundamental concepts in mathematics, statistics, and probability; and applies this knowledge to data analysis, modeling, and interpretation of results.
2) It explains the principles of algorithm design and develops software for solving problems using at least one programming language.
3) It compares machine learning and data mining algorithms, selects the appropriate method, and applies it to real data.
4) Big data platforms utilize distributed systems and cloud computing architectures to perform data processing operations.
5) They apply natural language processing techniques to text data and develop basic NLP-based applications.
6) It analyzes different data sources, transforms them into meaningful outputs, and presents them using appropriate visualization tools.
7) It creates data-driven decision models using decision support systems.
8) It develops optimization models and produces solutions for industrial and sectoral problems.
9) In professional practice, we operate within the framework of ethical principles, data security, and social responsibility.
10) They keep up with current technological developments in their field, actively participate in teamwork, and develop a lifelong learning awareness.

SECTION III: RELATIONSHIP BETWEEN COURSE UNIT AND COURSE LEARNING OUTCOMES (CLOs)

Level of Contribution of the Course to PLOs

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Programme Learning Outcomes Contribution Level (from 1 to 5)
1) It explains fundamental concepts in mathematics, statistics, and probability; and applies this knowledge to data analysis, modeling, and interpretation of results.
2) It explains the principles of algorithm design and develops software for solving problems using at least one programming language.
3) It compares machine learning and data mining algorithms, selects the appropriate method, and applies it to real data.
4) Big data platforms utilize distributed systems and cloud computing architectures to perform data processing operations.
5) They apply natural language processing techniques to text data and develop basic NLP-based applications.
6) It analyzes different data sources, transforms them into meaningful outputs, and presents them using appropriate visualization tools.
7) It creates data-driven decision models using decision support systems.
8) It develops optimization models and produces solutions for industrial and sectoral problems.
9) In professional practice, we operate within the framework of ethical principles, data security, and social responsibility.
10) They keep up with current technological developments in their field, actively participate in teamwork, and develop a lifelong learning awareness.

SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE

Teaching & Learning Methods of the Course

(All teaching and learning methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)
Teaching and Learning Methods defined at the Programme Level
Teaching and Learning Methods Defined for the Course
Lectures
Discussion
Case Study
Problem Solving
Demonstration
Views
Reading
Homework
Project Preparation
Seminar
Course Conference
Brain Storming
Questions Answers
Individual and Group Work
Role Playing-Animation-Improvisation
Active Participation in Class

Assessment & Evaluation Methods of the Course

(All assessment and evaluation methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)
Aassessment and evaluation Methods defined at the Programme Level
Assessment and Evaluation Methods defined for the Course
Midterm
Final Exam
Homework Evaluation
Active Participation in Class

Contribution of Assesment & Evalution Activities to Final Grade of the Course

Measurement and Evaluation Methods # of practice per semester Level of Contribution
Quizzes 1 % 5.00
Homework Assignments 1 % 5.00
Seminar 1 % 10.00
Midterms 1 % 30.00
Semester Final Exam 1 % 50.00
Total % 100
PERCENTAGE OF SEMESTER WORK % 50
PERCENTAGE OF FINAL WORK % 50
Total % 100

SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE

WORKLOAD OF TEACHING & LEARNING ACTIVITIES
Teaching & Learning Activities # of Activities per semester Duration (hour) Total Workload
Course 14 42 588
Laboratory 0 0 0
Application 0 0 0
Special Course Internship (Work Placement) 0 0 0
Field Work 0 0 0
Study Hours Out of Class 1 3 3
Presentations / Seminar 1 3 3
Project 0 0 0
Homework Assignments 1 5 5
Total Workload of Teaching & Learning Activities - - 599
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES
Assesment & Evaluation Activities # of Activities per semester Duration (hour) Total Workload
Quizzes 1 5 5
Midterms 1 6 6
Semester Final Exam 1 10 10
Total Workload of Assesment & Evaluation Activities - - 21
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities) 620
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h) 6