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Unit of study_

IDEA9101: IDEA Laboratory 1

Semester 1, 2023 [Normal day] - Remote

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

Unit code IDEA9101
Academic unit Design Lab
Credit points 6
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Andres Pinilla Palacios,
Tutor(s) Yi Zhao,
Andrew Le,
Fred Rodrigues,
Joel Flanagan,
Type Description Weight Due Length
Assignment group assignment Assessment 2: AI-based distributed responsive environment
Practical assessment
Due date: 31 May 2023 at 23:59
Approximately 50 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment group assignment Assessment 1: Remote-controlled audio-visual interaction
Practical assessment
40% Week 07
Due date: 10 Apr 2023 at 23:59
Approximately 30 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Online task Assessment 3: Weekly reflective progress reports
Written assessment
20% Weekly 5-10 minutes each week
Outcomes assessed: LO5 LO6
group assignment = 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


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.


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.


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.


50 - 64

Work demonstrating satisfactory achievement of the learning outcomes assessed.


0 - 49

Work that does not demonstrate satisfactory achievement of one or more of the learning outcomes assessed.

For more information see

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

WK Topic Learning activity Learning outcomes
Week 01 1. Welcome to the course: policies, overview, motivation, and technologies. 2. Introduction to web apps: running p5js locally. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 02 1. Object-oriented and asynchronous computing. 2. Creative coding with classes; callbacks. Lecture and tutorial (3 hr) LO2 LO3 LO4 LO5
Week 03 1. Remote interaction via OSC. 2. Remote controlling a visual sketch. Lecture and tutorial (3 hr) LO2 LO3 LO4
Week 04 1. Building distributed environments with OSC and MQTT. 2. Distributed architectures: client/server, peer-to-peer. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO6
Week 05 1. Programming sound: examples and basic concepts. 2. Interactions with sound. 3. Assignment 1 assistance. Lecture and tutorial (3 hr) LO1 LO3 LO4 LO6
Week 06 1. Introduction to machine learning and ML5js. 2. Image classification with MobileNet. 3. Assignment 1 assistance. Lecture and tutorial (3 hr) LO1 LO3 LO4 LO6
Week 07 1. Pose and object detection in ML5js. 2. Tracking presence and objects with a camera (PoseNet). 3. Assignment 1 assistance. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 08 1. Supervised AI training: classification vs regression. 2. Training your own neural network in ML5js; saving and loading models. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 10 1. Sentiment analysis. 2. Creating a distributed mood visualiser. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO6
Week 11 1. Classifying sounds. 2. Creating AI-based audio gestures. Lecture and tutorial (3 hr) LO2 LO3 LO4 LO6
Week 12 1. Introduction to generative models. 2. Creating a distributed visual effect generator. 3. Assignment 2 assistance. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO6
Week 13 1. Advanced generative models: ChatGPT and Stable Diffusion. 2. Assignment 2 assistance. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 14 (STUVAC) Assignment 2 assistance. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6

Attendance and class requirements

Please refer to the Resolutions of the University School:

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:

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:

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:

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


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.

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

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 costs

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


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.