Course Summary Course Objectives Learning Outcomes Course Materials Teaching Methods Weekly Topics Course Schedule Office Hours Assestment ECTS Calculation Course Policies Learning Tips Print Syllabi Download as PNG

PSY690 PhD Dissertation

Syllabus   |  International University of Sarajevo  -  Last Update on Oct 10, 2025

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Psychology

Spring 2024 - 2025 | 120 ECTS Credits | International University of Sarajevo

Academic Year
2024 - 2025
Semester
Spring
Course Code
PSY690
Weekly Hours
Teaching + Practice
ECTS
120
Prerequisites
None
Teaching Mode Delivery
Face-to-face
Prerequisite For
-
Teaching Mode Delivery Notes
-
Cycle
III Cycle
Prof. Jane Doe

Orkun Aydin

Course Lecturer

Position
Associate Professor Dr.
Email
oaydin@ius.edu.ba
Phone
033 957 305
Assistant(s)
-
Assistant E-mail
-

Course Objectives

PhD Dissertation

Learning Outcomes

After successful completion of the course, the student will be able to:

Course Materials

Required Textbook

none

Additional Literature
none

Teaching Methods

Weekly Topics

This weekly planning is subject to change with advance notice.
Week Topic Readings / References
1 N/A
2 N/A
3 N/A
4 N/A
5 N/A
6 N/A
7 N/A
8 N/A
9 N/A
10 N/A
11 N/A
12 N/A
13 N/A
14 N/A
15 N/A

Course Schedule (All Sections)

SectionTypeDay 1Venue 1Day 2Venue 2
PSY690.1 Course - - - -

Office Hours & Room

DayTimeOfficeNotes
Monday 09:00 - 15:00 A B.1
Wednesday 09:00 - 12:00 A B.1
Thursday 09:00 - 15:00 A B.1
Friday 09:00 - 11:00 A B.1

Assessment Methods and Criteria

Assessment Components

100%x1
PhD Dissertation
AI: Not Allowed

Alignment with Learning Outcomes : 

IUS Grading System

Grading Scale IUS Grading System IUS Coeff. Letter (B&H) Numerical (B&H)
0 - 44 F 0 F 5
45 - 54 E 1
55 - 64 C 2 E 6
65 - 69 C+ 2.3 D 7
70 -74 B- 2.7
75 - 79 B 3 C 8
80 - 84 B+ 3.3
85 - 94 A- 3.7 B 9
95 - 100 A 4 A 10

IUS Grading System

Letter marks that do not affect student's CGPA:
  • "IP" – In progress is assigned for recording unfulfilled student obligations related to graduation project/thesis/dissertation and internship.
  • "S" – Satisfactory is assigned to a student who passed the examinations that are not numerically graded or whose written assignment has been accepted.
  • "U" – Unsatisfactory is assigned to a student who failed to pass the examinations that are not numerically graded.
  • "W" – Withdrawal signifies that student has withdrawn from the relevant course.
Additional letter mark that affects student's CGPA:

"N/A" – Not attending, and it is assigned to a student who is suspended from the course or who does not meet the minimal requirement for attendance on lectures or tutorials. The course lecturer must follow the attendance policy and assign "N/A" in each case of a student failing attendance.

Late Work Policy

Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.

ECTS Credit Calculation

📚 Student Workload

This 120 ECTS credit course corresponds to 3000 hours of total student workload, distributed as follows:

PhD Dissertation Defense

3000 hours ⏳ (100 week × 30 h)

3000 Total Workload Hours

120 ECTS Credits


Course Policies

Academic Integrity

All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.

Attendance Policy

Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.

Technology & AI Policy

Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.

Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.

Communication Policy

All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [PSY690] in the subject line.

Academic Quality Assurance Policy

Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.

More info

Article 112: Evaluation of Work of the Academic Staff

  1. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
  2. Evaluation of work of each academic staff member is to be carried out in accordance with the Statute of the institution of higher education by the institution as well as by students.
  3. The institutions of higher education are obliged to carry out a students’ evaluation survey on the academic staff performance after the end of each semester, or after the completed teaching cycle for the subject taught.
  4. Evaluation must evaluate: lecture quality, student-academic staff interaction, correctness of communication, teacher’s attitudes towards students attending the teaching activities and at assessments, availability of suggested reading material, attendance and punctuality of the teacher, along with other criteria which are defined in the Statute.
  5. The institution of higher education by a specific act determines the procedure for evaluation of the academic staff performance, the content of survey forms, the manner of conducting the evaluation, grading criteria for the evaluation, as well as adequate measures for the academic staff who received negative evaluation for two consecutive years.
  6. The evaluation of the academic staff performance is an integral process of establishment the quality assurance system, or self-control and internal quality assurance.
  7. Results of the evaluation of the academic staff performance are to be adequately analyzed by the institution of higher education, and the decision of the head of the organizational unit about the employee’s work performance is an integral part of the personal file of each member of academic staff.

Learning Tips

Engage Actively

Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.

Read and Review Purposefully

Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.

Think Critically in Assignments

Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.

Ask Questions Early

Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.

Course Academic Quality Assurance: Semester Student Survey

Syllabus Last Updated on Oct 10, 2025 | International University of Sarajevo

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Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
T P
PSY690 PhD Dissertation 120
Prerequisite None It is a prerequisite to -
Lecturer Orkun Aydin Office Hours / Room / Phone
Monday:
9:30-15:00
Wednesday:
9:30-12:00
Thursday:
9:00-15:00
Friday:
9:30-11:00
A B.1 - 033 957 305
E-mail oaydin@ius.edu.ba
Assistant Assistant E-mail
Course Objectives PhD Dissertation
Textbook none
Additional Literature
  • none
Learning Outcomes After successful  completion of the course, the student will be able to:
    Teaching Methods
    Teaching Method Delivery Face-to-face Teaching Method Delivery Notes
    WEEK TOPIC REFERENCE
    Week 1 N/A
    Week 2 N/A
    Week 3 N/A
    Week 4 N/A
    Week 5 N/A
    Week 6 N/A
    Week 7 N/A
    Week 8 N/A
    Week 9 N/A
    Week 10 N/A
    Week 11 N/A
    Week 12 N/A
    Week 13 N/A
    Week 14 N/A
    Week 15 N/A
    Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs AI Usage
    PhD Dissertation 1 100 Not Allowed
    Semester Evaluation Components
    ***     ECTS Credit Calculation     ***
     Activity Hours Weeks Student Workload Hours Activity Hours Weeks Student Workload Hours
    PhD Dissertation Defense 30 100 3000
            Total Workload Hours = 3000
    *T= Teaching, P= Practice ECTS Credit = 120
    Course Academic Quality Assurance: Semester Student Survey Last Update Date: 23/10/2025

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