TURKISH LANGUAGE I PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
TRD101	TURKISH LANGUAGE I	1	2	2	2

GENERAL INFORMATION
Language of Instruction :	Turkish
Level of the Course Unit :	BACHELOR'S DEGREE, TYY: + 6.Level, EQF-LLL: 6.Level, QF-EHEA: First Cycle
Type of the Course :	Compulsory
Mode of Delivery of the Course Unit	-
Coordinator of the Course Unit	Assist.Prof. ONUR TÜRKER
Instructor(s) of the Course Unit
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	The aim of this course is to make the students comprehend the properties of Turkish language and rules of operation. Students written and oral texts develop their vocabulary, language skills through. At the end of this course, students should follow the rules of spelling, to gain the habit of using punctuation in place, to gain the habit of reading books, to gain the habit of scientific, critical, questioning, interpretive, creative, constructive thinking.
Contents of the Course Unit:	Language and language features, language-thought relationship, language power, mother tongue-local language-nation language-state language-language of education-language of culture-international language, language-culture relationship, language families-language groups, world languages ​​of Turkish The course includes the historical periods and development of the Turkish language, grammar and its parts, sounds and features in Turkish, types of words-root-appendix, elements of sentence and sentence, rules and applications of spelling, punctuation and use of punctuation marks.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
Examines the works of Turkish language researchers.
Distinguishes the place of Turkish in language families.
Classifies the development of Turkish language according to historical periods.
Distinguishes the root-additional difference in Turkish words.
Shows the root-additional distinction in Turkish words on the sample text.
Shows the elements of Turkish sentences on the sample text.
Checks the compatibility of the sample texts with the spelling rules.
Uses punctuation in accordance with spelling rules.
Evaluate Turkish texts according to different thinking systems.

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	Video viewing / reading Web-based research, Individual research	Language and Characteristics of Language	Programmed Learning - Video Conference
2	Video viewing / reading Web-based research, Individual research	Classification of Languages, Place of Turkish in World Languages	Programmed Learning - Video Conference
3	Video viewing / reading Web-based research, Individual research	Historical Development of Turkish and Its Stages	Programmed Learning - Video Conference
4	Video viewing / reading Web-based research, Individual research	Sounds and their characteristics	Programmed Learning - Video Conference
5	Video viewing / reading Web-based research, Individual research	Sounds in Turkish and their Classification	Programmed Learning - Video Conference
6	Video viewing / reading Web-based research, Individual research	Word Structure and Conjugation	Programmed Learning - Video Conference
7	Video viewing / reading Web-based research, Individual research	Word Structure and Conjugation	Programmed Learning - Video Conference
8	-	MID-TERM EXAM	-
9	Video viewing / reading Web-based research, Individual research	Word Structure and Conjugation	Programmed Learning - Video Conference
10	Video viewing / reading Web-based research, Individual research	Types of Words	Programmed Learning - Video Conference
11	Video viewing / reading Web-based research, Individual research	Types of Words	Programmed Learning - Video Conference
12	Video viewing / reading Web-based research, Individual research	Types of Words	Programmed Learning - Video Conference
13	Video viewing / reading Web-based research, Individual research	Verbs and Moods	Programmed Learning - Video Conference
14	Video viewing / reading Web-based research, Individual research	Complementary Verbs and Verbs in Terms of Structure	Programmed Learning - Video Conference
15	Video viewing / reading Web-based research, Individual research	Verbals, Elements of Sentence	Programmed Learning - Video Conference
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING
Aksan D. (1977-86), Her Yönüyle Dil, 1, 2, 3, TDK Yayınları, Ankara.
Aksoy Ö. A. (1990), Dil Yanlışları, Emel Matbaacılık, Ankara.
Banguoğlu T. (2000), Türkçenin Grameri, TDK Yayınları, Ankara.
Ergin M. (1993), Üniversiteler İçin Türk Dili, İstanbul, Bayrak Basım Yayın Dağıtım, İstanbul.
Levent A. S. (1973), Dil Üstüne, TDK Yayınları, Ankara.
Turan F. (2014), Stages of Evolution: Studies in Turkish Language and Literature, The Isis Press, İstanbul.
Aksaraylı, İ. (2011). Yükseköğretim Öğrencileri İçin Türk Dili. (5.Baskı). Manisa: Emek.

ASSESSMENT
Assessment & Grading of In-Term Activities	Number of Activities	Degree of Contribution (%)	Description	Examination Method

Level of Contribution
0	1	2	3	4	5

KNOWLEDGE
Theoretical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student gains proficiency in the fundamental components of data science and analytics, and is able to practically apply methods related to statistical analysis, data mining, and machine learning.
2	The student is capable of analyzing both structured and unstructured data types and can effectively utilize analytical methods to derive meaningful insights from large datasets.
3	The student can utilize programming languages such as Python, R, and SQL in data analysis and modeling processes and is able to effectively manage data processing and automation tasks.

KNOWLEDGE
Factual
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student can express analytical findings clearly and effectively by using data visualization and result reporting techniques, contributing meaningfully to decision-making processes.
2	The student can analyze complex data-driven problems, develop appropriate solutions, and make creative, data-based decisions through the use of scientific research methods.

SKILLS
Cognitive
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student can analyze problems encountered in the field of data science and analytics, develop solutions by selecting appropriate data analysis techniques, and critically evaluate statistical, algorithmic, and artificial intelligence-based methods.

SKILLS
Practical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student can effectively use programming languages such as Python, R, and SQL in data science and analytics applications; they are capable of developing practical solutions using data mining, machine learning, big data processing, data visualization, and modeling tools, and can work with real-world datasets.

OCCUPATIONAL
Autonomy & Responsibility
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student is able to take responsibility in individual or team-based projects related to data science and analytics, independently plan and execute complex data-driven tasks, and play an active role in decision-making processes by developing analytical and creative solutions to encountered problems.

OCCUPATIONAL
Learning to Learn
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student possesses the competence for continuous self-improvement with an awareness of lifelong learning by following current developments, technologies, and methods in the field of data science and analytics; they can rapidly acquire new knowledge and skills and apply them effectively.

OCCUPATIONAL
Communication & Social
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student can communicate their work in data science and analytics clearly and effectively through written, oral, and visual means; they are capable of working efficiently in multidisciplinary teams, engaging in effective communication, and developing collaborative solutions.

OCCUPATIONAL
Occupational and/or Vocational
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student has a strong command of the concepts, methods, algorithms, and tools specific to the field of data science and analytics; they can carry out data collection, processing, analysis, and interpretation processes in accordance with ethical principles, and act with a sense of responsibility regarding data privacy and security.

WORKLOAD & ECTS CREDITS OF THE COURSE UNIT
Workload for Learning & Teaching Activities
Type of the Learning Activites	Learning Activities (# of week)	Duration (hours, h)	Workload (h)
Lecture & In-Class Activities	14	2	28
Preliminary & Further Study	13	1	13
Land Surveying	0	0	0
Group Work	0	0	0
Laboratory	0	0	0
Reading	0	0	0
Assignment (Homework)	0	0	0
Project Work	0	0	0
Seminar	0	0	0
Internship	0	0	0
Technical Visit	0	0	0
Web Based Learning	0	0	0
Implementation/Application/Practice	0	0	0
Practice at a workplace	0	0	0
Occupational Activity	0	0	0
Social Activity	0	0	0
Thesis Work	0	0	0
Field Study	0	0	0
Report Writing	0	0	0
Final Exam	1	1	1
Preparation for the Final Exam	1	5	5
Mid-Term Exam	1	1	1
Preparation for the Mid-Term Exam	1	3	3
Short Exam	0	0	0
Preparation for the Short Exam	0	0	0
TOTAL	31	0	51