| 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 | At Tekirdağ Namık Kemal University, procedures for the recognition of prior learning are carried out in accordance with the “TNKÜ Directive on the Recognition of Prior Learning.” Knowledge, skills and competencies acquired outside a higher education institution through personal interests and self-directed learning, workplace experience, or certificates issued by accredited institutions or institutions approved by the relevant University boards may be recognized under the applicable regulations. These prior learning outcomes are assessed through exemption examinations, documentation and/or portfolio evaluation.
Applications must be submitted during the first academic year following the student’s final enrolment at the University, on the dates determined and announced by the relevant academic unit. Applications are reviewed by the Commission for the Recognition of Previously Acquired Competencies, evaluated in line with the recommendations of the relevant department or programme boards, and finalized by a decision of the relevant Administrative Board.
Applications for the recognition of prior learning may be submitted under the categories of personal interests, certificates or portfolios, using the application form and supporting documents specified by the University. All procedures are conducted within the scope, time limits and conditions set out in the relevant University directive. |
| 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 |
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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
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Grade
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Letter Grade
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Coefficient
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Status
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90-100
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AA
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4.00
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Başarılı
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80-89
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BA
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3.50
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Başarılı
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70-79
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BB
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3.00
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Başarılı
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65-69
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CB
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2.50
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Başarılı
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60-64
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CC
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2.00
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Başarılı
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50-59
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DD
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1.50
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Başarısız
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30-49
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FD
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1.00
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Başarısız
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0-29
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FF
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0.00
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Başarısız
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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.
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| Graduation Requirements | |
| Teaching Methods |
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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 *:
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TEACHING METHODS*
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LEARNING ACTIVITIES
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MEANS
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Course
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Listening and interpretation
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Standard classroom technologies, multimedia devices, projector, computer, overhead projector
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Discussion Course
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Listening and interpretation, observation/situation handling, critical thinking, question development
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Standard classroom technologies, multimedia devices, projector, computer, overhead projector
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Special Support / Structural Examples
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Special skills planned beforehand
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Playing a Role / Drama
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Special skills planned beforehand
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Standard classroom technologies, special equipment
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Problem Solving
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Special skills planned beforehand
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Case Study
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Special skills planned beforehand
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Brainstorming
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Listening and interpretation, observation/situation handling, critical thinking, question development, team work
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Standard classroom technologies, multimedia devices, projector, computer, overhead projector
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Small Group Discussion
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Listening and interpretation, observation/situation handling, critical thinking, question development
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Standard classroom technologies, multimedia devices, projector, computer, overhead projector
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Presentation
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Listening and interpretation, observation/situation handling
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Real or virtual environment suitable for observation
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Simulation
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Listening and interpretation, observation/situation handling, informatics skills
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Real or virtual environment suitable for observation
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Seminar
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Research – lifelong learning, writing, reading, informatics, listening and interpretation, management skills
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Standard classroom technologies, multimedia devices, projector, computer, overhead projector, special equipment
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Group Study
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Research – lifelong learning, writing, reading, informatics, critical thinking, question development, management skills, team work
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Field / Land Study
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Observation / situation handling, research – lifelong learning, writing, reading
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Laboratory
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Observation/situation handling, informatics, management skills, team work
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Special equipment
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Homework
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Research – lifelong learning, writing, reading, Informatics
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Internet database, library database, e-mail
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Oral Exam
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Survey and Questionnaire Study
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Research – lifelong learning, writing, reading
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Panel
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Listening and interpretation, observation/situation handling
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Standard classroom technologies, multimedia devices, projector, computer, overhead projector, special equipment
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Guest Speaker
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Listening and interpretation, observation/situation handling
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Standard classroom technologies, multimedia devices, projector, computer, overhead projector, special equipment
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Student Club Activity / Projects
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Observation/situation handling, critical thinking, question development, team work, research – lifelong learning, writing, reading, management skills, special skills planned beforehand
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(*)One or more of the listed methods can be used depending on the specificity of the course.
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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.
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Curriculum
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