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

EE221 Object Oriented Programming

Syllabus   |  International University of Sarajevo  -  Last Update on Mar 03, 2026

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Electrical and Electronics Engineering

Spring 2023 - 2024 | 6 ECTS Credits | International University of Sarajevo

Academic Year
2023 - 2024
Semester
Spring
Course Code
EE221
Weekly Hours
3 Teaching + 2 Practice
ECTS
6
Prerequisites
ENS213
Teaching Mode Delivery
Face-to-face
Prerequisite For
-
Teaching Mode Delivery Notes
-
Cycle
I Cycle
Prof. Jane Doe

Kanita Karađuzović-Hadžiabdić

Course Lecturer

Position
Associate Professor Dr.
Email
kanita@ius.edu.ba
Phone
033 957 218
Assistant(s)
Kenan Micivoda
Assistant E-mail
230302299@student.ius.edu.ba

Course Objectives

The aims of this course are to: teach students main object-oriented concepts and practices, introduce students to one object-oriented programming language, teach students some of the fundamental data structures and algorithms.

Learning Outcomes

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

1
Solve moderately complex real-world problems using object oriented programming language
2
Verify the performance and correctness of your solutions, and effectively debug the software you have written
3
Define, explain, and use the various data structures discussed in class
4
Identify which abstract data structure could be useful in representing or solving a problem and why

Course Materials

Required Textbook

Walter Savitch, Absolute Java, 6th Edition Pearson; Carrano, Data Structures and Abstractions with Java, 4th Edition

Additional Literature
Class notes

Teaching Methods

Lectures
Class discussion
Practical work
Homework exercises
Lab exercises

Weekly Topics

This weekly planning is subject to change with advance notice.
Week Topic Readings / References
1 Introduction to the course Chapter 1,2,3 (Savitch)
2 Intro to Classes, Methods and Instance Variables Chapter 4 (Savitch)
3 Information Hiding and Encapsulation, Constructors, Static Methods, Static Variables, Wrapper Classes, Chapter 4,5 (Savitch)
4 References and Class Parameters, Copy Constructors, Graded Lab1 Chapter 7 (Savitch)
5 Inheritance, PART I Chapter 7 (Savitch)
6 Inheritance, PART II, Graded Lab2 Chapter 8 (Savitch)
7 Polymorphism and Abstract Classes. Chapter 9 (Savitch)
8 Exception Handling, Graded Lab3
9 MIDTERM WEEK
10 Intro to UML (basics of class diagram), Debugging, Swing I Chapter 12,17 (Savitch)
11 Swing, cont. Graded LAB4 (or next week due to 1st May holiday) Ch 17 (Savitch)
12 Data Strucures Bags, Array Implementation Chapter 1,2 (Savitch)
13 Data Strucures, (Bags, LInk Implemenation, Intro to Stacks. Graded LAB5, or next week Chapter 3,5 Intro (Carrano)
14 Data Strucures, Stack Implementation, Intro to Queues, Deques, and Priority Queues Chapter 6, 10 Intro (Carrano)
15 Revision for the final exam

Course Schedule (All Sections)

SectionTypeDay 1Venue 1Day 2Venue 2
EE221.1 Course - - - -
EE221.2 Course - - - -
EE221.1 Tutorial - - - -
EE221.2 Tutorial - - - -
EE221.3 Tutorial - - - -
EE221.4 Tutorial - - - -
EE221.5 Tutorial - - - -
EE221.6 Tutorial - - - -

Office Hours & Room

DayTimeOfficeNotes
Tuesday 10:00 - 12:00 B F3.13
Wednesday 14:00 - 17:00 B F3.13

Assessment Methods and Criteria

Assessment Components

40%x1
Final Exam
AI: Not Allowed

Alignment with Learning Outcomes :  1  2  3  4

30%x1
Midterm Exam
AI: Not Allowed

Alignment with Learning Outcomes :  1  2

25%x5
In-Lab assignments
AI: Not Allowed

Alignment with Learning Outcomes :  1  2  3  4

5%x5
Homework
AI: Not Allowed

Alignment with Learning Outcomes :  1  2  3  4

%x
I
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 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:

Lecture Hours

45 hours ⏳ (15 week × 3 h)

Active Tutorials

28 hours ⏳ (14 week × 2 h)

Home Study

60 hours ⏳ (15 week × 4 h)

In-term Exam Study

7 hours ⏳ (1 week × 7 h)

Final Exam Study

10 hours ⏳ (1 week × 10 h)

150 Total Workload Hours

6 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 [EE221] 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 Mar 03, 2026 | International University of Sarajevo

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Referencing Curricula Print this page

Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
T P
EE221 Object Oriented Programming 3 2 6
Prerequisite ENS213 It is a prerequisite to -
Lecturer Kanita Karađuzović-Hadžiabdić Office Hours / Room / Phone
Tuesday:
10:00-12:00
Wednesday:
14:00-17:00
B F3.13 - 033 957 218
E-mail kanita@ius.edu.ba
Assistant Kenan Micivoda Assistant E-mail 230302299@student.ius.edu.ba
Course Objectives The aims of this course are to: teach students main object-oriented concepts and practices, introduce students to one object-oriented programming language, teach students some of the fundamental data structures and algorithms.
Textbook Walter Savitch, Absolute Java, 6th Edition Pearson; Carrano, Data Structures and Abstractions with Java, 4th Edition
Additional Literature
  • Class notes
Learning Outcomes After successful  completion of the course, the student will be able to:
  1. solve moderately complex real-world problems using object oriented programming language
  2. verify the performance and correctness of your solutions, and effectively debug the software you have written
  3. define, explain, and use the various data structures discussed in class
  4. identify which abstract data structure could be useful in representing or solving a problem and why
Teaching Methods Lectures, Class discussion, Practical work, Homework exercises, Lab exercises
Teaching Method Delivery Face-to-face Teaching Method Delivery Notes
WEEK TOPIC REFERENCE
Week 1 Introduction to the course Chapter 1,2,3 (Savitch)
Week 2 Intro to Classes, Methods and Instance Variables Chapter 4 (Savitch)
Week 3 Information Hiding and Encapsulation, Constructors, Static Methods, Static Variables, Wrapper Classes, Chapter 4,5 (Savitch)
Week 4 References and Class Parameters, Copy Constructors, Graded Lab1 Chapter 7 (Savitch)
Week 5 Inheritance, PART I Chapter 7 (Savitch)
Week 6 Inheritance, PART II, Graded Lab2 Chapter 8 (Savitch)
Week 7 Polymorphism and Abstract Classes. Chapter 9 (Savitch)
Week 8 Exception Handling, Graded Lab3
Week 9 MIDTERM WEEK
Week 10 Intro to UML (basics of class diagram), Debugging, Swing I Chapter 12,17 (Savitch)
Week 11 Swing, cont. Graded LAB4 (or next week due to 1st May holiday) Ch 17 (Savitch)
Week 12 Data Strucures Bags, Array Implementation Chapter 1,2 (Savitch)
Week 13 Data Strucures, (Bags, LInk Implemenation, Intro to Stacks. Graded LAB5, or next week Chapter 3,5 Intro (Carrano)
Week 14 Data Strucures, Stack Implementation, Intro to Queues, Deques, and Priority Queues Chapter 6, 10 Intro (Carrano)
Week 15 Revision for the final exam
Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs AI Usage
Final Exam 1 40 1,2,3,4 Not Allowed
Semester Evaluation Components
Midterm Exam 1 30 1,2 Not Allowed
In-Lab assignments 5 25 1,2,3,4 Not Allowed
Homework 5 5 1,2,3,4 Not Allowed
I Not Allowed
***     ECTS Credit Calculation     ***
 Activity Hours Weeks Student Workload Hours Activity Hours Weeks Student Workload Hours
Lecture Hours 3 15 45 Active Tutorials 2 14 28
Home Study 4 15 60 In-term Exam Study 7 1 7
Final Exam Study 10 1 10
        Total Workload Hours = 150
*T= Teaching, P= Practice ECTS Credit = 6
Course Academic Quality Assurance: Semester Student Survey Last Update Date: 27/03/2026

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