Outline

Overview

The aim of this unit of study is the learning of key technical skills for prototyping and building interactive digital media within a creative design framework. The unit provides an introduction to the fundamentals of various software and hardware construction tools, and the technological platforms available for building sensor-based interfaces. Students will gain practical experience through a series of exercises and assignments.

Unit details and rules

Academic unit	Design Lab
Credit points	6
Prerequisites ?	IDEA9103
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Luke Hespanhol, luke.hespanhol@sydney.edu.au
Lecturer(s)	Luke Hespanhol, luke.hespanhol@sydney.edu.au
Tutor(s)	Joanne Martin, joanne.l.martin@sydney.edu.au
Tutor(s)	Yi Zhao, yi.zhao1@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Assessment 3: Weekly reflective progress reports Written assessment	20%	Multiple weeks	5-10 minutes each week
Outcomes assessed:
Assignment	Assessment 2: AI-based distributed responsive environment Practical assessment	50%	STUVAC	Approximately 50 hours
Outcomes assessed:
Assignment	Assessment 1: Remote-controlled audio-visual interaction Practical assessment	30%	Week 08	Approximately 30 hours
Outcomes assessed:
= group assignment ?

Assessment summary

Assessments are designed to evaluate students progressive and reflective learning of computing techniques for designing tangible and prototyping interactions in shared spaces.

Assessment 1: Remote-controlled audio-visual interaction.
Assessment 2: AI-based distributed responsive environment.
Assessment 3: Weekly reflective progress reports: online quizzes.

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	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	1. Welcome to the course: policies, overview, motivation, and technologies. 2. Introduction to web apps: running p5js locally.	Online class (3 hr)
Week 02	1. Object-oriented and asynchronous computing. 2. Creative coding with classes; callbacks.	Online class (3 hr)
Week 03	Remote interaction via OSC.	Online class (1 hr)
Week 03	Remote controlling a visual sketch.	Tutorial (2 hr)
Week 04	1. Building distributed environments with OSC and MQTT. 2. Distributed architectures: client/server, peer-to-peer.	Online class (3 hr)
Week 05	Programming sound: examples and basic concepts.	Online class (1 hr)
Week 05	1. Interactions with sound. 2. Assignment 1 assistance.	Tutorial (2 hr)
Week 06	1. Introduction to machine learning and ML5js. 2. Image classification with MobileNet. 3. Assignment 1 assistance.	Online class (3 hr)
Week 07	Pose and object detection in ML5js.	Online class (1 hr)
Week 07	1. Tracking presence and objects with a camera (PoseNet). 2. Assignment 1 assistance.	Tutorial (2 hr)
Week 08	1. Supervised AI training: classification vs regression. 2. Training your own neural network in ML5js; saving and loading models.	Online class (3 hr)
Week 09	Sentiment analysis.	Online class (1 hr)
Week 09	Creating a distributed mood visualiser.	Tutorial (2 hr)
Week 10	1. Classifying sounds. 2. Creating AI-based audio gestures.	Online class (3 hr)
Week 11	Introduction to generative models.	Online class (1 hr)
Week 11	1. Creating a distributed visual effect generator. 2. Assignment 2 assistance.	Tutorial (2 hr)
Week 12	Assignment 2 assistance.	Online class (3 hr)
Week 13	Assignment 2 assistance.	Online class (1 hr)
Week 13	Assignment 2 assistance.	Tutorial (2 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.

Required readings

This unit of study will build on the popular p5.js creative coding project, and its associated ML5js machine learning library. Students are advised to make themselves familiar with the web sites for these projects via their respective websites:

p5js: http://p5js.org/
ML5js: https://ml5js.org/

Also, highly recommended is The Coding Train website by Daniel Shiffman, with a wealth of tutorials, examples and resources about creative coding in p5.js:

The Coding Train: https://thecodingtrain.com
Beginners Guide to Machine Learning with JavaScript: https://youtube.com/playlist?list=PLRqwX-V7Uu6YPSwT06y_AEYTqIwbeam3y/

There are no required readings for this unit of study, but there are many good articles and videos on machine learning for those new to the topic. In addition to The Coding Train videos, the following are recommended:

Excavating AI: The Politics of Images in Machine Learning Training Sets: https://excavating.ai/
But what is a neural network? https://youtu.be/aircAruvnKk

A list of online resources, course material, announcements and assessment submission rubric will be available on the Canvas site for the unit. The site can be accessed at https://canvas.sydney.edu.au/login/canvas

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. Assess the requirements of an interaction design problem.
LO2. Plan, design and communicate the intended structure and behaviour of systems composed of interactive digital media.
LO3. Demonstrate technical competency in prototyping and building technologies for novel and creative interactive applications.
LO4. Develop and test software programs and systems that are correct, robust and well-documented.
LO5. Reflectively document their process and design solution.
LO6. Understand, discuss and apply interaction design principles and concepts.

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.

Keeping weekly status report, however adjusting some of the questions. Planning more time for code review sessions in class during tutorials, and support toward assignments.

Additional information

Additional costs

The unit will mostly make use of freely accessible software and tools. Students will be expected to pay for low-cost tools and apps adopted for specific purposes.

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Disclaimer

On this page

Useful links

IDEA9101: IDEA Laboratory 1

Semester 1, 2022 [Normal day] - Camperdown/Darlington, Sydney

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Disclaimer

On this page

Useful links