COMP9001: Semester 1, 2025

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Units COMP9001 Semester 1 2025 [Normal evening]

Unit outline_

COMP9001: Introduction to Programming

Semester 1, 2025 [Normal evening] - Camperdown/Darlington, Sydney

Overview

This unit is an essential starting point for software developers, IT consultants, and computer scientists to build their understanding of principle computer operation. Students will obtain knowledge and skills with procedural programming. Crucial concepts include defining data types, control flow, iteration, functions, recursion, the model of addressable memory. Students will be able to reinterpret a general problem into a computer problem, and use their understanding of the computer model to develop source code. This unit trains students with software development process, including skills of testing and debugging. It is a prerequisite for more advanced programming languages, systems programming, computer security and high performance computing.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	INFO1110 or INFO1910 or INFO1103 or INFO1903 or INFO1105 or INFO1905 or ENGG1810
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Armin Chitizadeh, armin.chitizadeh@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Supervised exam ?	Final Examination The final exam is a comprehensive assessment at the end of a course that tests students on all the material covered throughout the semester, with a 40% hurdle requirement.	40%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Homework1 Homework assessment	5%	Week 03 Due date: 23 Mar 2025 at 23:59	2 weeks
Outcomes assessed:
Assignment	Homework2 Homework assessment	5%	Week 07 Due date: 13 Apr 2025 at 23:59	2 weeks
Outcomes assessed:
Assignment	Assignment1 Assignment assessment	15%	Week 09 Due date: 04 May 2025 at 23:59	3 weeks
Outcomes assessed:
Assignment	Final Project Final Project	5%	Week 12 Due date: 25 May 2025 at 23:59	13 weeks
Outcomes assessed:
Assignment	Assignment2 Assignment assessment	25%	Week 13 Due date: 01 Jun 2025 at 23:59	3 weeks
Outcomes assessed:
Small test	In Lab Checkpoints At the end of each lab, students are asked to complete a series of simple tasks. The primary purpose of these tasks is to ensure that students understand the topic thoroughly before concluding the tutorial. The highest 8 marks out of a total of 12 will be	5%	Weekly	20 minutes
Outcomes assessed:
= hurdle task ? = restricted AI ? = early feedback task ?

Assessment summary

Conditions for the pass in this course:
- At least 50% total

- At least 40% in the Final Examination

All answers must be provided in the English language, including code comments.

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.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

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

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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 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.

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	Introduction to Programming. First program.	Lecture (2 hr)
Week 02	Getting started with programming basics	Computer laboratory (2 hr)
Week 02	Variables and Data types	Lecture (2 hr)
Week 03	Variables and Data types	Computer laboratory (2 hr)
Week 03	Conditionals	Lecture (2 hr)
Week 04	Conditionals	Computer laboratory (2 hr)
Week 04	Iteration	Lecture (2 hr)
Week 05	Iteration	Computer laboratory (2 hr)
Week 05	Flow of Execution. Functions	Lecture (2 hr)
Week 06	Flow of Execution. Functions	Computer laboratory (2 hr)
Week 06	Collections	Lecture (2 hr)
Week 07	Collections	Computer laboratory (2 hr)
Week 07	Classes and Objects	Lecture (2 hr)
Week 08	Classes and Objects	Computer laboratory (2 hr)
Week 08	File I/O	Lecture (2 hr)
Week 09	File I/O	Computer laboratory (2 hr)
Week 09	More Flow Control	Lecture (2 hr)
Week 10	More Flow Control	Computer laboratory (2 hr)
Week 10	Testing	Lecture (2 hr)
Week 11	Testing	Computer laboratory (2 hr)
Week 11	Recursion	Lecture (2 hr)
Week 12	Recursion	Computer laboratory (2 hr)
Week 12	Iterators and Multidimensional arrays	Lecture (2 hr)
Week 13	Iterators and Multidimensional arrays	Computer laboratory (2 hr)
Week 13	Revision	Lecture (2 hr)
Weekly	Online Weekly Question and Answer Sessions with Lecturers	Seminar (1 hr)

Attendance and class requirements

How to pass COMP9001

First Option: Traditional Path

If your total mark is 50% or higher and your final exam is 40% or high, you will pass the course. Your final grade will be calculated by adding all your weighted marks from the semester.

Second Option: "Competency Path"

If you fail the Traditional Path, the first step that we do is to check whether you qualify for the Competency Path. If you meet the eligibility criteria, your failing grade will be replaced with a final mark of 50%, allowing you to pass the course.

Eligibility for the Competency Path:

You must have scored 80% or higher in all of the following:

2.Homework 2

3.Assignment 1

4.Essential Section of the Final Exam*

All the assessment above represent the essentials in our Introduction to Programming course (COMP9001) and you are required to demonstrate comprehension and the ability to apply the fundamentals.

*The final exam consists of two clearly labelled sections: Essential and Advanced. Essential section covers essential topics and advanced covers advanced topics of the unit.

Second chance to pass through competency

If you fail the course through the traditional path and are not eligible for the competency path, you will have a second chance to complete certain assessments. If, during the semester, your marks make you ineligible for the competency pass—for example, if you score below 80% on Homework 2, Assignment 1, or the Essential Section of your final exam—you will be given an opportunity to redo them. We will also provide extra support to help you prepare.

Retaking these assessments will not impact your total mark if you successfully pass through the traditional path. However, it will make you eligible for the competency path and ensure you receive a minimum mark of 50% if, unfortunately, you fail the traditional path and need to qualify through competency.

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.

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. employ programming style conventions for writing consistently readable code
LO2. design and construct new functionality to existing procedural program or function
LO3. compose a structured algorithmic design to solve the descriptive problem specification
LO4. compose an entire procedural program from descriptive problem specification
LO5. demonstrate knowledge of programming principles, data types, variables and operators, control-flow: simple statement sequence, if-then-else, while functions, stack, input/output, reference memory model
LO6. compose, analyse and trace procedural code. Scoping/variable lifetime, memory of the stack, references and global's, data types, operations on data types
LO7. construct code cliches for input and manipulating arrays, including maximum, minimum, search or traverse, with actions on each element for counting or summation
LO8. construct and assess code for recursively-defined numerical functions, and for recursively described array manipulations
LO9. apply testing methods and assess programs through debugging and write a set of tests for a small program or function
LO10. explain compilation process and debugging mechanism
LO11. use standard library functions.

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.

This section outlines changes made to this unit following staff and student reviews. We have redesigned this unit based on student feedback from previous offerings. The new structure aims to enhance learning and simplify assessment processes for students. We have added scaffolding to assignments, making them more approachable. Lab sizes have been reduced to foster a sense of community among students and encourage greater collaboration and participation.

Additional information

Canvas and Ed are both used in this course for separate purposes. Students are encouraged to engage with the discussions and support on Ed for their learning.

Students are expected to regularly visit these websites to learn of announcements and information concerning format and schedule of assessment.

Lectures and tutorials are essential components of this course. Students are strongly encouraged to actively participate and engage in these learning sessions to maximise their academic success.

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

Assessment summary

Assessment criteria

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

How to pass COMP9001

First Option: Traditional Path

Second chance to pass through competency

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

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