Outline

Overview

This unit provides an introduction to the development of software in design and the creative industries. It teaches an understanding of the fundamentals of computational thinking, as well as skills in the design and implementation of software for creative expression. It introduces students to tools for building interactive design prototypes that express their interaction design skills through programming. It covers knowledge of programming concepts; creative coding practices; and Javascript and the p5.js library. Key concepts covered in this unit include: variables, functions, control flows, and algorithmic thinking. Students learn how to approach creative expression through the medium of code, which will allow them to incorporate programming into their own design practice as well as to collaborate effectively with software developers. This unit is a foundational core unit in the Master of Interaction Design and Electronic Arts program.

Unit details and rules

Academic unit	Design Lab
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Liam Bray, liam.bray@sydney.edu.au
Lecturer(s)	Liam Bray, liam.bray@sydney.edu.au
Tutor(s)	Alexander Elton-Pym, alex.elton-pym@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	A5: Interactive Algorithmic System Design and implement and interactive algorithmic system using p5.js.	40%	Formal exam period	14 hours
Outcomes assessed:
Assignment	A1: Creative Coding Challenge Milestone 1 Complete the “Milestone 1” challenge on the CCCs platform.	5%	Week 03	1 Hour
Outcomes assessed:
Assignment	A2: Creative Coding Challenge Milestone 2 Complete the “Milestone 2” challenge on the CCCs platform.	5%	Week 05	1 Hour
Outcomes assessed:
Online task	A3: In-Class Quiz Complete a series of creative coding challenges during class time.	30%	Week 07	2 hours
Outcomes assessed:
Assignment	A4: Designing a Creative Coding Challenge Create your own coding challenge in the CCCs format.	20%	Week 10	7 Hours
Outcomes assessed:

Assessment summary

Design as a practice allows us to push the boundaries of technology to discover new ways of interacting with the world. Designers were quick to embrace the possibilities of modern computing, and in the middle of last century began using programming as a creative tool. Early computer artists began to explore the creative behaviours exhibited by computational systems and discovered a whole new way of generating designs.

In this class you will use programming as a creative tool to design algorithmic systems which produce visual designs. Over the course of this unit, you will:

Develop a knowledge of algorithmic design methods.
Learn the fundamentals of JavaScript using the P5.js framework.
Design interactive computational systems that algorithmically produce dynamic behaviours.

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	Work of outstanding quality, demonstrating mastery of the learning outcomes assessed. The work shows significant innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or exceptional skill.
Distinction	75 - 84	Work of excellent quality, demonstrating a sound grasp of the learning outcomes assessed. The work shows innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or superior skill.
Credit	65 - 74	Work of good quality, demonstrating more than satisfactory achievement of the learning outcomes assessed, or work of excellent quality for a majority of the learning outcomes assessed.
Pass	50 - 64	Work demonstrating satisfactory achievement of the learning outcomes assessed.
Fail	0 - 49	Work that does not demonstrate satisfactory achievement of one or more of the learning outcomes assessed.

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	Programming for Designers	Lecture and tutorial (3 hr)
Week 02	Making Patterns with Code	Lecture and tutorial (3 hr)
Week 03	Simple Algorithmic Design	Lecture and tutorial (3 hr)
Week 04	Designing Reusable Code	Lecture and tutorial (3 hr)
Week 05	Transformations and Events	Lecture and tutorial (3 hr)
Week 06	Designing with Data	Lecture and tutorial (3 hr)
Week 07	In-Class Quiz	Practical (2 hr)
Week 08	Working with Media	Lecture and tutorial (3 hr)
Week 09	Object-oriented Modelling	Lecture and tutorial (3 hr)
Week 10	Designing for Emergence	Lecture and tutorial (3 hr)
Week 11	Breaking out of the p5 Canvas	Lecture and tutorial (3 hr)
Week 12	Extension Topics in Creative Code (3D, AI, & more)	Lecture and tutorial (3 hr)
Week 13	Assignment Development Session	Lecture and tutorial (3 hr)

Attendance and class requirements

Please refer to the Resolutions of the University School: http://sydney.edu.au/handbooks/architecture/rules/faculty_resolutions.shtml

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. comprehend, modify and integrate code from diverse sources
LO2. understand and communicate design decisions and the architectural constraints of computer programs
LO3. use a combination of computational and design thinking to synthesise interactive artefacts and explore programming as a creative medium
LO4. create interactive digital artefacts by authoring programs
LO5. evaluate the technical, aesthetic and human-centred qualities of interactive digital artefacts.

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 scope of the final assessment was narrowed to improve clarity for students and extra documentation was created to ensure that there was a consistency with assignment submissions.

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

IDEA9103: Design Programming

Semester 1, 2021 [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