SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code	Course Name	Year	Semester	Theoretical	Practical	Credit	ECTS
ETP5206	Social Media and Content Marketing	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:	Face to face
Name of Coordinator:	Instructor ALPER ŞEN
Dersin Öğretim Eleman(lar)ı:	Instructor ALPER ŞEN
Dersin Kategorisi:	Competency Development (University Elective)

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives:

The primary aim of this course is to equip students with the competence to manage social media platforms not merely as users, but as professional marketing tools. From an e-commerce and marketing-oriented perspective, the course aims to teach digital strategy development, target audience analysis, the operational logic of algorithms, and platform-specific content creation processes. In addition, it seeks to develop skills in integrating contemporary artificial intelligence tools into content marketing and in data-driven performance reporting. Ultimately, the course aims to prepare students to be industry-ready.

Course Content:

The topics covered within the scope of this course include the historical development of social media and its transformation in marketing communication; the technical setup, algorithmic structures, and optimization strategies of Meta platforms (Facebook, Instagram, WhatsApp), YouTube, X, LinkedIn, TikTok, and other contemporary platforms. The course also addresses the fundamental principles of content marketing, creative copywriting and visual content production, and video marketing processes. In addition, the use of generative artificial intelligence tools in content creation is examined. Furthermore, the course covers social media strategy development, social commerce, and e-commerce integration, as well as campaign management, KPI definition, data analysis, and social media performance reporting techniques.

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.)*
1) Explains the development process of social media, its fundamental concepts, and its role in marketing communication.
2) Identifies the basic operating logic, algorithmic principles, and marketing-oriented use cases of social media platforms.
Skills *(Describe as Cognitive and/or Practical Skills.)*
1) Explains the effects of social media and content marketing on branding, advertising, reputation, crisis management, customer relations, and corporate identity.
2) Performs the setup, optimization, and management of social media accounts.
3) Applies techniques that transform social media platforms into e-commerce sales channels (e.g., store setup, product tagging).
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) Analyzes social media performance data (KPIs) using digital tools and produces meaningful reports to support strategic decision-making processes.
2) Develops a holistic strategy and implementation plan for social media and content marketing by ensuring alignment among objectives, platforms, and content.

Weekly Course Schedule

Week	Subject	Materials Sharing *
		Related Preparation	Further Study
1)	Development of Social Media and Fundamental Concepts	Course notes
2)	Use of Social Media in Marketing Communication (Contribution to Promotion, Branding, Advertising, Crisis Management, Reputation Management, Customer Relations, Corporate Identity Alignment)	Course notes
3)	Social Media Platforms, Algorithms, and Account Setup/Optimization I (Meta Dashboards, Facebook, Instagram, WhatsApp)	Course notes
4)	Social Media Platforms, Algorithms and Account Setup/Optimization II (YouTube, X, LinkedIn)	Course notes
5)	Social Media Platforms, Algorithms, and Account Setup/Optimization III (TikTok, Pinterest, Twitch, etc.)	Course notes
6)	Social Media Strategy Development	Course notes
7)	Content Types and Principles of Effective Production I (Text-Based Content)	Course notes
8)	Midterm Exam
9)	Content Types and Principles of Effective Production II (Static Visual Content)	Course notes
10)	Content Types and Principles of Effective Production III (Video and Audio Content)	Course notes
11)	Artificial Intelligence Tools in Content Production	Course notes
12)	Content Strategy Development and Content Calendar Planning	Course notes
13)	E-Commerce Applications of Social Media and Content Marketing	Course notes
14)	Analysis and Performance Reporting	Course notes

*These fields provides students with course materials for their pre- and further study before and after the course delivered.

DERS ÖĞRENME ÇIKTILARI - PROGRAM ÖĞRENME ÇIKTILARI İLİŞKİSİ

Contribution of The Course Unit To The Programme Learning Outcomes

Ders Öğrenme Çıktıları (DÖÇ)	1	2	3	4	5	6	7
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
Demonstration
Reading
Homework
Peer Education
Course Conference
Brain Storming
Questions Answers
Individual and Group Work
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
Homework Assignments	1	% 15.00
Midterms	1	% 35.00
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	13	3	39
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	10	1	10
Presentations / Seminar	0	0	0
Project	0	0	0
Homework Assignments	2	20	40
Total Workload of Teaching & Learning Activities	-	-	89
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES
Assesment & Evaluation Activities	# of Activities per semester	Duration (hour)	Total Workload
Quizzes	0	0	0
Midterms	1	30	30
Semester Final Exam	1	35	35
Total Workload of Assesment & Evaluation Activities	-	-	65
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities)			154
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h)			6