Outline

Overview

Object-oriented (OO) programming is a technique that arranges code into classes, each encapsulating in one place related data and the operations on that data. Inheritance is used to reuse code from a more general class, in specialised situations. Most modern programming languages provide OO features. Understanding and using these are an essential skill to software developers in industry. This unit provides the student with the concepts and individual programming skills in OO programming, starting from their previous mastery of procedural programming.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	INFO1110 or INFO1910 or ENGG1810
Corequisites ?	None
Prohibitions ?	INFO1103 or INFO1105 or INFO1905
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Mohammad Polash, masbaul.polash@sydney.edu.au
Lecturer(s)	Joseph Lizier, joseph.lizier@sydney.edu.au
	Mohammad Polash, masbaul.polash@sydney.edu.au
	Jiangshan Yu, jiangshan.yu@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam ?	Written Final Exam Pen-and-paper based exam	55%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Creative work	Task 1 Write code from specifications	4%	Week 02 Due date: 15 Aug 2025 at 23:59	N/A	AI allowed
Outcomes assessed:
In-class quiz	Coding Challenge Invigilated coding test during the tutorial. #earlyfeedbacktask	4%	Week 03	40 minutes	AI allowed
Outcomes assessed:
Creative work	Task 2 Write code from specifications	4%	Week 04 Due date: 29 Aug 2025 at 23:59	N/A	AI allowed
Outcomes assessed:
Creative work	Task 3 Write code from specifications	4%	Week 05 Due date: 05 Sep 2025 at 23:59	N/A	AI allowed
Outcomes assessed:
In-class quiz	In-tutorial Quiz Small Pen and Paper based quiz during tutorial	3%	Week 06	15 minutes	AI allowed
Outcomes assessed:
In-class quiz	In-tutorial Quiz Small Pen and Paper based quiz during tutorial	3%	Week 07	15 minutes	AI allowed
Outcomes assessed:
In-class quiz	In-tutorial Quiz Small Pen and Paper based quiz during tutorial	3%	Week 08	15 minutes	AI allowed
Outcomes assessed:
Creative work	Assignment Implement an OOP project from the specification. There will be an in-person viva, during which students must demonstrate their understanding of the submitted code, without reference to generative AI or other materials, as per the Assessment Summary above	20%	Week 11 Due date: 24 Oct 2025 at 23:59	N/A	AI allowed
Outcomes assessed:
= hurdle task ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Task: These tasks are designed to test your programming capability from problem description. You are permitted to use generative AI to learn but not to produce the output of tasks directly. Any use of genAI must be acknowledged (e.g. in header comments in the code). You should develop or edit any generative ideas to a substantial degree to ensure your submission is your own, original work. It may be helpful to use AI as a learning tool to help you develop code, building your expertise towards the viva components of the later assignments and the exam.
Tutorial Quiz: These weekly quizzes are designed to test both knowledge and skills of course materials.
Coding Challenge: This early feedback task tests students' code writing capability. It is an invigilated test during the tutorial.
Assignment: Implementing a project from specification. You are permitted to use generative AI in developing the code for this task - this use must be acknowledged (e.g. in header comments in the code). You should develop or edit any generative ideas to a substantial degree to ensure your submission is your own, original work. Note that you are not allowed to reference generative AI or other materials during the Viva, during which you are required to demonstrate understanding of the code and its design and functionality. As such, you would be advised to utilise AI more as a learning than design tool. Note that you are responsible to independently verify and edit any AI-generated content to ensure the integrity, accuracy, and suitability of the output, and to ensure your overall understanding of it.
Final Exam: The final exam covers all aspects of the course and may involve answering questions about the Object-Oriented programming language used in the course.

Detailed information for each assessment can be found on Canvas/Ed.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard. It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	1. Introduction to the unit; 2. Compilation, Syntax; 3. Conditional statements; 4. Scope and lifetimes	Lecture and tutorial (4 hr)
Week 02	1. Loop and static methods; 2. Arrays and strings	Lecture and tutorial (4 hr)
Week 03	1. Classes and instance methods; 2. File IO	Lecture and tutorial (4 hr)
Week 04	1. Static and Non-static context; 2. Collections and Abstract Data Types	Lecture and tutorial (4 hr)
Week 05	1. Inheritance and encapsulation; 2. Overloading and overriding	Lecture and tutorial (4 hr)
Week 06	1. Abstract classes and interfaces; 2. Polymorphism; 3. Default methods	Lecture and tutorial (4 hr)
Week 07	1. Generics and checked types; 2. Type bounds and collection interfaces	Lecture and tutorial (4 hr)
Week 08	1. Exceptions and error handling; 2. Testing and automation	Lecture and tutorial (4 hr)
Week 09	1. Recursion with OOP; 2. Enums	Lecture and tutorial (4 hr)
Week 10	1. Anonymous Classes and lambdas	Lecture and tutorial (4 hr)
Week 11	1. Java Wildcards 2. Debugging	Lecture and tutorial (4 hr)
Week 12	1. Revision-01	Lecture and tutorial (4 hr)
Week 13	1. Revision-02 2. Overview of Exam Structure	Lecture and tutorial (4 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed through the Library Reading List, available on Canvas.

Walter Savitch – Java: An Introduction to Problem Solving and Programming, 7th Edition. Pearson Higher Ed USA, 2014. 9781292018331

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Demonstrate an understanding of the concept of Object-Orientation: understand and explain key concepts of object-oriented programming, including classes as encapsulating data, object instances, memory model of references, methods and calling them across objects.
LO2. Demonstrate an understanding of Object-Oriented programming language : reading, tracing and writing competence with the following elements of Java programming language: classes, methods, object creation; instance and local variables, parameters and scope; basic types; simple I/O; control flow primitives and understand, modify and add functionality to Java programs
LO3. Read and interpret object-oriented design documents, including basic UML diagrams.
LO4. Create appropriate class/data structure including the data types and methods for simple problems
LO5. Derive a computer program from a design document that uses concepts of OO and memory model, trace and write small examples of code including the following elements: inheritance, polymorphism, abstract classes and interfaces, variables and their type and the relationship between static and dynamic type, exception
LO6. Demonstrate experience in testing Object-Oriented programs, write tests for standalone objects, be able to generate and handle exceptions, create invariants for classes, methods and objects, pre- and post-conditions for methods, and assertions
LO7. Demonstrate experience in testing and debugging Object-Oriented programs, write tests for stand-alone object code, to be run automatically.
LO8. Demonstrate experience with common interfaces and collections in Java.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

The assessment structure has changed since the unit was last offered.

Additional information

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

- In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

- All written assignments submitted in this unit of study will be submitted to the similarity detecting software program known as TurnItIn. TurnItIn searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.

- There will always be some degree of text-matching when using TurnItIn. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

INFO1113: Object-Oriented Programming

Semester 2, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links