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Unit outline_

INFO1113: Object-Oriented Programming

Semester 2, 2022 [Normal day] - Remote

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
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Written Final Exam
Type D
50% Formal exam period 2.5 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10
Online task Task 1
Write code from specification
3% Week 02 N/A
Outcomes assessed: LO1 LO8 LO6 LO4 LO3 LO2
Online task Task 2
Write code from specification
3% Week 03 N/A
Outcomes assessed: LO1 LO8 LO6 LO4 LO3 LO2
Online task Task 3
Write code from specification
3% Week 04 N/A
Outcomes assessed: LO1 LO8 LO6 LO4 LO3 LO2
Online task Task 4
Write code from specification
3% Week 05 N/A
Outcomes assessed: LO1 LO9 LO6 LO4 LO3 LO2
Small test Quiz 1
Written small test
10% Week 06 1.5 hours
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Assignment Programming Assignment
Implement a project from specification
18% Week 11
Due date: 23 Oct 2022 at 23:59
N/A
Outcomes assessed: LO1 LO2 LO4 LO5 LO6 LO8 LO10
Small test Quiz 2
Written small test
10% Week 12 1.5 hours
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Type D final exam = Type D final exam ?

Assessment summary

  • Task: These tasks are designed to test your programming capability from problem description
  • Quiz: These quizzes are designed to test both knowledge and skills of course materials in the semester thus far.
  • Assignment: Demonstrating programming ability from specification.
  • 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.

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. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. 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.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

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. For example, a good assignment that would normally get 9/10 and is 2 days late loses 10% of the full 10 marks, i.e. new mark = 8/10 OR an average assignment that would normally get 5/10 and is 5 days late loses 25% of the full 10 marks, i.e. new mark = 2.5/10. After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

WK Topic Learning activity Learning outcomes
Week 01 1. Introduction to the unit; 2. Compilation; 3. Syntax; 4. Conditional statements; 5. Scope and lifetimes Lecture and tutorial (4 hr) LO1 LO6
Week 02 1. Loop and static methods; 2. Contiguous memory; 3. Arrays and strings Lecture and tutorial (4 hr) LO1 LO3 LO6
Week 03 1. Classes and instance methods; 2. File IO and binary IO Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4
Week 04 1. Static and Non-static context; 2. Memory Layout; 3. Collections and Abstract Data Types Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4
Week 05 1. Inheritance and encapsulation; 2. Overloading and overriding Lecture and tutorial (4 hr) LO1 LO2 LO6 LO8
Week 06 1. Abstract classes and interfaces; 2. Polymorphism; 3. Default methods 4. Gradle, Packaging, Classpath and Java Archives; Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO8
Week 07 1. Generics and checked types; 2. Type bounds and collection onterfaces Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO8
Week 08 1. Exceptions and error handling; 2. Testing and automation Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO6 LO10
Week 09 1. Recursion with OOP; 2. Enums and optionals Lecture and tutorial (4 hr) LO1 LO2 LO3 LO5 LO7
Week 10 1. Anonymous Classes and lambdas Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10
Week 11 1. Java Wildcards 2. Debugging Lecture and tutorial (4 hr) LO5 LO8
Week 12 1. Revision-01 Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10
Week 13 1. Revision-02 2. Overview of Exam Structure Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10

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 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.

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. read and interpret an object oriented design document
  • LO3. demonstrate an understanding of the memory model and differences between locations of variables
  • LO4. 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
  • LO5. 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
  • LO6. create appropriate class/data structure including the data types and methods for simple problems
  • LO7. read, trace and write recursive Object-Oriented programs to perform an operation in a related set of classes that support some nested structure
  • LO8. 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
  • LO9. demonstrate experience writing code with common interfaces and collections in Object-Oriented programming language
  • LO10. demonstrate experience in testing and debugging Object-Oriented programs, write tests for stand-alone object code, to be run automatically.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

Assessment weighting has been changed since this unit was last offered.

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.