Outline

Overview

This unit introduces students to the fundamental principles that underlie professional practice in computing. It lays the foundation for later studies, and presents to the students challenges common to a multidisciplinary IT environment. The subject also provides students with the opportunity to develop important attributes such as communication skills, an understanding of professional ethics, and of working as a part of a team. Tool use is an important aspect of this unit: students are required to learn to use tools for planning and completing work, managing artefacts including reports, and communicating within the team. A selection of guest speakers will address students on different career paths. Dalyell students may enrol in ENGD1000 Building a Sustainable World in place of INFO1111

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	ENGG1805 or ENGG1111 or ENGD1000 or INFO5990
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Simon Poon, simon.poon@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final Examination Final exam: Contains both foundation and advanced components.	0%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
In-person practical, skills, or performance task or test	Knowledge Foundation - Quiz In-class invigilated quiz in weeks 7,9, & 11.	0%	Multiple weeks	30 mins	AI prohibited
Outcomes assessed:
Q&A following presentation, submission or placement	Oral review for self-learning foundation assignment Student answers questions about their submission for self-learning foundation assignment (opportunities in weeks 8, 11 and StuVac)	0%	Multiple weeks	8 minutes	AI prohibited
Outcomes assessed:
Q&A following presentation, submission or placement	Oral review for skills foundation assignment Student answers questions about their submission for skills foundation assignment (opportunities in weeks 8, 11 and StuVac)	0%	Multiple weeks	8 minutes	AI prohibited
Outcomes assessed:
Written work	Skills Foundation Assignment Group project with individual sections (3 submission opportunities in weeks 7, 10 & 13)	0%	Multiple weeks	8 weeks	AI allowed
Outcomes assessed:
Portfolio or journal	Self-learning Advanced Assignment) Learning of selected technology (2 submission opportunities in week 10 & 13)	0%	Multiple weeks	8 weeks	AI allowed
Outcomes assessed:
Portfolio or journal	Self-learning Foundation Assignment Learning of selected technology (3 submission opportunities weeks 7, 10 and 13)	0%	Multiple weeks	8 weeks	AI allowed
Outcomes assessed:
Written work	Skills Advanced Assignment Group project with individual sections, (2 submission opportunities in week 10 & 13)	0%	Multiple weeks	8 weeks	AI allowed
Outcomes assessed:
Contribution	Weeks 1-3 participation rating Assessment of participation in class activities	0%	Week 03	3 weeks	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ? = 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

The assessment in this unit is structured in an unusual way. There are three different assessment themes: Knowledge; Skills; and Self-Learning; and 2 different assessment types – assignments and tests (as well as an early feedback task that does not affect your grade), and these are split into Foundation tasks and Advanced tasks.

	Foundation Assessments	Advanced Assessments
Test	Knowledge Foundation Tests: 4 attempts (in weeks 7, 9, 11 and final exam) pass/fail only	Knowledge Advanced Test: 1 attempt (only in final exam) 30 marks
Assignment	Skills Foundation Assignment: 3 attempts (in weeks 7, 10, and 13, with oral reviews in weeks 8, 11 and 13/stuvac) pass/fail only	Skills Advanced Assignment: 2 submission slots: week 10 (if already passed Skills Foundation in week 8, for feedback only); week 13 for marking with secure "no AI" oral review in week 13/Stuvac 10 marks
Assignment	Self-Learn Foundation Assignment: 3 attempts (in weeks 7, 10, and 13, with oral reviews in weeks 8, 11 and 13/stuvac) pass/fail only	Self-Learn Advanced Assignment: 2 submission slots: week 10 (if already passed Self-Learn Foundation in week 8, for feedback only); week 13 for marking with secure "no AI" oral review in week 13/Stuvac 10 marks
General notes	These tasks are only pass/fail and are hurdle tasks. All 3 Foundation Assessments must be passed to achieve a 50P result or greater. Important to note that to achieve "Pass" for the Foundation requires reaching a satisfactory level (an expected threshold), not necessary just half (50%) the questions.	These Advanced tasks are optional but needed to achieve anything above 50P. The marks are added to 50, once satisfactory level has been achieved on all the hurdle Foundation tasks.

No simple extension is applicable in this unit given multiple submissions are built-in for knowledge tests and assignment submissions.

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.

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.

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.

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:

Late submission 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: The usual policy applies to the Advanced assignments. Since the Foundation tasks in this unit don't have marks awarded (being purely pass/fail) applying a percentage penalty doesn't make sense. Instead, you are penalised 0.5 overall unit grade marks per day for late submissions of the Foundation assignments.

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. Administration 2. Degree Structure & Majors	Lecture (1.5 hr)
Week 01	Transition	Tutorial (2 hr)
Week 02	1. The Profession 2. Ethics	Lecture (1.5 hr)
Week 02	1. Admin 2. Ice-breaking	Tutorial (2 hr)
Week 03	1. Problem Solving 2. Professional Development	Lecture (1.5 hr)
Week 03	1. The Profession 2. Ethics	Tutorial (2 hr)
Week 04	1. Group work 2. Introduce assignment	Lecture (1.5 hr)
Week 04	1. Problem Solving 2. Professional Development	Tutorial (2 hr)
Week 05	1. Communications 2. Knowledge Foundation Preparation	Lecture (1.5 hr)
Week 05	1. Group work 2. Assignment discussion	Tutorial (2 hr)
Week 07	1. Review 2. Topics connections	Lecture (1.5 hr)
Week 07	1. Communications 2. Knowledge Foundation Test Attempt 1	Tutorial (2 hr)
Week 08	Guest speaker: Computer Science	Lecture (1.5 hr)
Week 08	Assignment Foundation Attempt 1	Tutorial (2 hr)
Week 09	Guest speaker: Software Development	Lecture (1.5 hr)
Week 09	1. Group assignment activities 2. Knowledge Foundation Test Attempt 2	Tutorial (2 hr)
Week 10	Guest speaker: Cyber Security	Lecture (1.5 hr)
Week 10	1. Assignment Foundation Attempt 2 2. Assignment Advanced Attempt 1	Tutorial (2 hr)
Week 11	Guest speaker: Data science	Lecture (1.5 hr)
Week 11	1. Group assignment activities 2. Knowledge Foundation Test Attempt 3	Tutorial (2 hr)
Week 12	Guest speaker: Others....	Lecture (1.5 hr)
Week 12	1. Foundation Assignment Attempt 3 2. Advanced Assignment Attempt 2	Tutorial (2 hr)
Week 13	Review	Lecture (1.5 hr)
Week 13	Review	Tutorial (2 hr)

Attendance and class requirements

It is expected that students attend all tutorials that occur during periods when group projects are underway.

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. analyse and evaluate situations where ethical issues arise within IT professional activities, and relate the choices to ethical frameworks as well as professional standards and requirements
LO2. explain the principles behind problem-solving in a professional IT environment and apply these to example problems
LO3. explain the theoretical concepts and principles of intellectual property and its protection
LO4. explain and give examples of the principles governing participation in a professional IT context including codes of practice, professional standards and legislative and statutory requirements
LO5. track issues, and manage group work and communication for a small scale project using project methodologies and tools, as well as evaluate the limits and capabilities of the project tools
LO6. negotiate and evaluate team responsibilities and team processes with respect to the various points of view of team members
LO7. execute defined project tasks within a predefined project context in small diverse teams
LO8. carry out the processes of the small team project according to professional codes and principles and then compare the process to what one would expect to find in a large-scale professional IT environment
LO9. confidently use IT specific language and concepts in communication
LO10. formulate queries about a particular topic, research, retrieve relevant information from digital and other sources, evaluate it for reliability and synthesise it for a task, as well as adapt communication to meet the format and needs of the audience (including producing a written report and delivering an oral presentation)
LO11. explain and give examples of the scope of activities and responsibilities of professionals working in the areas of information technology and how these are described by the degree major options
LO12. reflect on, and assess, own skills and attributes to create a professional development plan.

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 structure of the assessment for 2026 has been modelled from INFO1110 for consistency.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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

Use of generative artificial intelligence (AI)

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

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

INFO1111: Computing 1A Professionalism

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Use of generative artificial intelligence (AI)

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

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links