ECTS Information Guide

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Information on the Institution

General Description of the Institution Address of Namık Kemal University Academic Calendar Academic Authorities Recognition of Prior Learning Recognition of Study Abroad List of Programmes Offered at TNKÜ General Admission Requirements General Registration Procedures ECTS Credit Allocation Arrangements for Academic Guidance

Degrees and Programmes

Doctorate(Third Cycle) Master's Degree(Second Cycle) Bachelor's Degree(First Cycle) Associate(Short Cycle)

General Information For Students

About Trakya and Tekirdağ Cost of Living Accommodation Meals Medical Facilities Facilities for Students with Special Needs Insurance Financial Support for Students Student Affairs Office Learning Facilities International Programmes Practical Information for Exchange Students Language Courses Internship Sports and Leisure Facilities Student Clubs Useful Phone Numbers

Diploma Supplement

National Qualifications

Robotics and Artificial Intelligence Pr. Associate Degree Program

Academic Unit Presenting the Program

Robotics and Artificial Intelligence Pr.

Program Director

Öğr. Gör. Zafer BAYRAM

Program Type

Associate Degree Program

Level of Degree Earned

Associate Degree Programme

Degree Earned

Robotics and Artificial Intelligence Programme

Education Type

Tam Zamanı

Registration Acceptance Conditions

Graduated from Secondary Education

Recognition of Prior Learning

The process of recognising prior learning in Turkish Higher Education Institutions is still in its early stages. For this reason, the recognition of prior learning has not been fully initiated in all programmes of Tekirdağ Namik Kemal University. However, exemption exams are organised at the beginning of each academic year for the compulsory foreign language courses in the curricula of the departments. Students who have completed the learning process of these courses on their own or who think that they have achieved the learning outcomes in these courses in different ways have the right to take these exams. Students who are successful in the exam are exempted from the relevant course in the syllabus.

Degree Requirements and Rules

The students studying in this undergraduate program are required to have a Cumulative Grade Points Average (Cum.GPA) of not less than 2.00/4.00 and have completed all the courses with at least a letter grade of DD/S in the program in order to graduate. The minimum number of ECTS credits required for graduation is 120. It is also mandatory for the students to complete their compulsory internship in a specified duration and quality.

Program Profile

The aim of the Robotics and Artificial Intelligence Programme is to train qualified technical staff who can combine theoretical knowledge with practical skills in the fields of artificial intelligence, robotic systems and automation technologies, integrate into digital transformation processes, think analytically and produce solutions. The programme aims to contribute to regional development and to create a human resource that can direct technological developments on a national scale through individuals who can adapt to Industry 4.0 and advanced production technologies, have lifelong learning awareness, and observe professional ethical values.

Occupational Profiles of Graduates

Our Graduates,  They can work as technical staff or experts in the fields of design, integration and programming of robotic systems.,  They can work in the development of artificial intelligence-supported systems, data analysis, machine learning applications and autonomous system projects.,  They can transfer to undergraduate programmes with DGS or they can take the opportunity to start their own business.

Access To Upper Degree

Those who successfully complete the Robotics and Artificial Intelligence associate degree programme will have the opportunity to complete a bachelor's degree by enrolling in undergraduate programmes that can be made Vertical Transfer in the guide published by ÖSYM, if they are successful in the Vertical Transfer Examination held by ÖSYM.

Exams, Assessment and Grading

There is at least one midterm exam each semester. In addition to midterms, projects and assignments are given at the beginning of each term, the dates of which are specified in the term content given at the beginning of each term. At the end of each semester, the student has to take the final exam. Criteria (such as midterm, project, homework and final) and their effect on the final grade are clearly stated in the course content distributed at the beginning of the semester and / or published on the website. According to the student regulations and academic calendar, final exams are held on the dates, places and times determined and announced by the university. The student's final grade is given by the course instructor according to the results of the midterm, project, homework and final exam.

The passing grade at Tekirdag Namik Kemal University is 60 out of 100. However, the final or make-up exam result must be at least 50. Exams are evaluated on a full grade of 100. The semester / year-end grade of a course is the sum of 30% of the arithmetic average of the midterm or midterm exams and 70% of the grade taken in the semester / year-end exam or make-up exam. However, the semester / year-end grade of a course; Provided that a decision is taken by the relevant faculty / school board and announced at the beginning of the semester, it can be calculated between 30% and 50% of the arithmetic average of the midterm or midterm exams, between 70% and 50% of the grade taken in the semester / year-end or make-up exam, and the sum of the rates can be calculated as 100%. As a result of the calculation, if the first number after the decimal point is less than five, it is increased to the lower integer, and if it is five or more than five, it is increased to the upper integer and finalised.

The final and make-up grades are submitted to Student Affairs by the course instructor together with the evaluation criteria. Final and make-up grades are published and announced in the student information system.

Notes:

For each course taken, the student is given the following letter grades as semester grades. Letter grades, coefficient and ratio equivalents are as follows. The grading system taken as basis in the evaluation of students' achievements is expressed in the following grades and letters

Grade	Letter Grade	Coefficient	Status
90-100	AA	4.00	Başarılı
80-89	BA	3.50	Başarılı
70-79	BB	3.00	Başarılı
65-69	CB	2.50	Başarılı
60-64	CC	2.00	Başarılı
50-59	DD	1.50	Başarısız
30-49	FD	1.00	Başarısız
0-29	FF	0.00	Başarısız

Students are required to retake the courses from which they received DD, FD or FF during the first oncoming semester in which these courses are offered.

Successful Students

Students who complete their associate or undergraduate degree with a grade point average of 3.00 – 3.49 graduate as honor students; 3.50 and above as high honor students.

Graduation Requirements

Teaching Methods

Teaching-learning methods and strategies are selected in a way to increase students' skills such as self-study, lifelong learning, observation, teaching others, presentation, critical thinking, teamwork, effective use of informatics.

In addition, attention is paid to ensure that the teaching style supports students with different abilities. The teaching and learning methods used in the programme are listed below *:

TEACHING METHODS*	LEARNING ACTIVITIES	MEANS
Course	Listening and interpretation	Standard classroom technologies, multimedia devices, projector, computer, overhead projector
Discussion Course	Listening and interpretation, observation/situation handling, critical thinking, question development	Standard classroom technologies, multimedia devices, projector, computer, overhead projector
Special Support / Structural Examples	Special skills planned beforehand
Playing a Role / Drama	Special skills planned beforehand	Standard classroom technologies, special equipment
Problem Solving	Special skills planned beforehand
Case Study	Special skills planned beforehand
Brainstorming	Listening and interpretation, observation/situation handling, critical thinking, question development, team work	Standard classroom technologies, multimedia devices, projector, computer, overhead projector
Small Group Discussion	Listening and interpretation, observation/situation handling, critical thinking, question development	Standard classroom technologies, multimedia devices, projector, computer, overhead projector
Presentation	Listening and interpretation, observation/situation handling	Real or virtual environment suitable for observation
Simulation	Listening and interpretation, observation/situation handling, informatics skills	Real or virtual environment suitable for observation
Seminar	Research – lifelong learning, writing, reading, informatics, listening and interpretation, management skills	Standard classroom technologies, multimedia devices, projector, computer, overhead projector, special equipment
Group Study	Research – lifelong learning, writing, reading, informatics, critical thinking, question development, management skills, team work
Field / Land Study	Observation / situation handling, research – lifelong learning, writing, reading
Laboratory	Observation/situation handling, informatics, management skills, team work	Special equipment
Homework	Research – lifelong learning, writing, reading, Informatics	Internet database, library database, e-mail
Oral Exam
Survey and Questionnaire Study	Research – lifelong learning, writing, reading
Panel	Listening and interpretation, observation/situation handling	Standard classroom technologies, multimedia devices, projector, computer, overhead projector, special equipment
Guest Speaker	Listening and interpretation, observation/situation handling	Standard classroom technologies, multimedia devices, projector, computer, overhead projector, special equipment
Student Club Activity / Projects	Observation/situation handling, critical thinking, question development, team work, research – lifelong learning, writing, reading, management skills, special skills planned beforehand

(*)One or more of the listed methods can be used depending on the specificity of the course.

Program Outcomes

1-To be able to follow the current developments in the field, to act with the awareness of continuous improvement of professional knowledge and skills; makes conscious choices in career planning.
2-To be able to follow professional publications using a foreign language and communicate effectively with colleagues at national and international level.
3-Can take an active role in system design, application development and integration processes in the fields of embedded systems, robotic hardware and industrial automation.
4-Can process digital data with image processing and analytical thinking competence; can interpret and report the results effectively.
5-Acts in line with occupational health and safety, environmental awareness, quality management and professional ethical principles; bears scientific and social responsibility.
6-Evaluate the professional problems encountered from an analytical and critical point of view and develop solutions independently.
7-Demonstrate proficiency in programming, algorithms, data structures, object oriented software and database management; design and develop complex software systems.
8-Have basic, up-to-date and applied knowledge in the fields of robotics and artificial intelligence; can use this knowledge effectively in professional activities.
9-To be able to use oral and written communication skills effectively; to be able to take responsibility in multidisciplinary teams and work in co-operation, to be able to manage large-scale projects.
10-By understanding the principles of artificial intelligence and machine learning, develop technological solutions that can solve real world problems.

Curriculum

Robotics and Artificial Intelligence Pr.
Total ECTS:					0

Robotics and Artificial Intelligence Pr. Associate Degree Program

Program Outcomes

Curriculum

Robotics and Artificial Intelligence Pr.

Matrix of Course - Program Outcomes