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
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	David Lowe, david.lowe@sydney.edu.au
Lecturer(s)	David Lowe, david.lowe@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final exam (Knowledge level 3) Final exam (pre-req = knowledge level 2)	0%	Formal exam period	1 hour
Outcomes assessed:
Skills-based evaluation	Oral exam (Knowledge level 4) Oral exam (pre-req = knowledge level 3)	0%	Formal exam period	30 mins
Outcomes assessed:
Small test	Online quiz (Knowledge level 2) Online quiz (pre-req = knowledge level 1)	0%	Multiple weeks	30 mins
Outcomes assessed:
Small test	Online Quiz (Knowledge level 1) Mandatory exam to assess level 1 knowledge	0%	Multiple weeks	30 mins
Outcomes assessed:
Assignment	Project Mandatory Project	0%	Multiple weeks	13 weeks
Outcomes assessed:
Assignment	Self-learning report and demonstration Learning of selected technology (Self-Learning levels 1-4)	0%	Multiple weeks	13 weeks
Outcomes assessed:
= hurdle task ? = group assignment ? = Type B final exam ?

Assessment summary

The assessment in this unit is all structured as a sequence of tasks associated with levels (0-4) of performance in three area: knowledge; practice; and self-learning. A task at a given level can only be attempted once the student has achieved the prior level. The final mark assigned will be based on the combination of levels achieved. The specific tasks include:

Knowledge:
- Level 1: Basic quiz (mandatory): An online quiz which assesses core knowledge required by any student in order to pass the unit.
- Level 2: Advanced quiz (optional): An online quiz which assesses “credit-level” knowledge.
- Level 3: Final exam (optional): A final exam in the formal exam period that assesses distinction-level knowledge.
- Level 4: Oral exam (optional): An oral exam to assess high-distinction level knowledge.

Practice:
- Levels 1-4: This is a group project where students demonstrate practical technical skills and knowledge of the different computing majors.

Self-learning:
- Levels 1-4: A sequence of increasingly sophisticated self-learning tasks that demonstrate each students’ ability to take responsibility for their own learning.

Opportunities for each level will remain available through to the end of the semester, so that students have the opportunity to continue to demonstrate their achievement, and someone who is a slow starter will still have an opportunity to catch up,

Detailed information for each assessment can be found on Canvas.

The final mark and grade for the unit will be determined based on the levels achieved in each of the three core areas. A minimum requirement to pass the unit will be a level 1 in all three areas (knowledge, practice, self-learning). Each additional level will increase the mark by 5 (e.g. level 2 in all areas will result in a mark of 50+3(5)=65C, and a level 3 in all areas would give a mark of 50+3(10) = 80D). Full details are provided 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.

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.

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.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	1. Introduction; 2. Professionalism	Lecture and tutorial (3 hr)
Week 02	The ICT professionals and teams	Lecture and tutorial (3 hr)
Week 03	Communication and collaboration	Lecture and tutorial (3 hr)
Week 04	Information retrieval	Lecture and tutorial (3 hr)
Week 05	ICT problem solving: designing abstractions, model-building	Lecture and tutorial (3 hr)
Week 06	Intellectual property	Lecture and tutorial (3 hr)
Week 07	Ethics	Lecture and tutorial (3 hr)
Week 08	Systems	Lecture and tutorial (3 hr)
Week 09	Computer science	Lecture and tutorial (3 hr)
Week 10	Software development	Lecture and tutorial (3 hr)
Week 11	Data science	Lecture and tutorial (3 hr)
Week 12	Information systems	Lecture and tutorial (3 hr)
Week 13	Review	Lecture and tutorial (3 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 (and hence much of the learning) in INFO1111 is somewhat different from most units, and was run as a trial in semester 1, 2021. The feedback indicated that some aspects of this worked really well (for example, many students really appreciated the opportunity to focus on topics of interest to them in the self-learning) and other aspects didn't work as well (for example, many students felt that being able to submit work at various points in the semester meant that they didn't have a clear deadline). The trial was successful enough that the general approach will be retained, but a number of changes will be made, based in part on the feedback from the USS: - Improve the way in which the assessment structure is described, so that it is easier to understand. And provide better mechanisms in Canvas for students to see what is approved and what levels they have achieved etc. - Simplify the quizzes so that students get more detailed feedback. - Change the self-learning so that it has a more structured approach where students can do it more incrementally. - Provide more specific skills development associated with how to approach your own self-learning.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Media

Student links

About us

Connect

Current students

INFO1111: Computing 1A Professionalism

Semester 1, 2022 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Media

Student links

About us

Connect