Outline

Overview

This unit of study consolidates students' knowledge of advanced concepts in digital modelling, visualisation media and digital fabrication techniques available for architectural design. The unit develops conceptual understanding of generative geometric logic through a case study analysis followed by a small design project. Students will explore the practical applications of the digital geometry they create using commercial modelling and rendering packages in conjunction with the digital fabrication equipment available in DMaF. It will help students: generate sophisticated digital geometry through pre-packaged techniques and scripting processes, assign colour and texture information, generate sophisticated images for visualization purposes and fabricate prototypes. At the conclusion of this unit students should be conversant with 3D modelling, photo-rendering and digital fabrication terminology and be able to generate complex 3D models. Class preparation: 3 hours/week, assessment preparation 8 hours/semester.

Unit details and rules

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

Teaching staff

Coordinator	Anastasia Globa, anastasia.globa@sydney.edu.au
Tutor(s)	Anastasia Globa, anastasia.globa@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Presentation	3. Interactive Bio-Environmets Canvas Submission and Project Presentation	40%	Formal exam period Due date: 13 Jun 2025 at 23:59	Presentation 10 min, 20-page + Vid/Game
Outcomes assessed:
Attendance	Attendance 90% attendance is required in this UoS	0%	Multiple weeks	Duration of class
Outcomes assessed:
Presentation	1. Biological relationships Assignment 1 Presentation and Submission	30%	Week 05 Due date: 28 Mar 2025 at 23:59	Presentation 10 min, 20-page + Video
Outcomes assessed:
Presentation	2. Bio-Inspired Prototypes Presentation and Canvas submission	30%	Week 09 Due date: 02 May 2025 at 23:59	Presentation 10 min, 20-page, 3D model
Outcomes assessed:
= hurdle task ? = group assignment ? = AI allowed ? = restricted AI ?

Assessment summary

Biological relationships: Working individually, students will produce a series of 3D digital models using Grasshopper which, describe your biological typology assembly based on your reference research. Students will be required to define the parametric logic and attributes of the assembly using diagrams, line drawings, shaded renders and text.
Bio-Inspired Prototypes: Students will need to demonstrate the development of their bio typologies at the scale of the body. An understanding of how the study operates as a performative structure in relation to the body will be investigated through diagrams, line drawings, renders, basic animation, text and a physical model.
Interactive Bio-Environments: Students will work within their team to further develop their bio-form-making strategy and their performative capabilities. Students will test their proposals through a series of chosen computational design strategies. Further to this, students will output their findings to a gaming engine and test a number of spatial scenarios. Students will present their work to the class in the form of a video presentation / or game play.

Detailed information for each assessment can be found on Canvas.

Attendance: Students should be present and engaged in their learning during classes. Late arrival/early departure will be deemed as an absence. Students who do not meet the minimum 90% threshold, who have approved special consideration may be offered the opportunity to sit an alternative assessment to pass this unit.

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.

Use of generative artificial intelligence (AI) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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 penalties will apply as per the universities policy.

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.

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
Formal exam period	[Task 3.5] Assignment 3 Final Presentation.	Presentation (3 hr)
Week 01	[Task 1.1] The Biological Typology & Pattern Variation Introduction to tutors & semester brief. Introduction / Museum Visit	Tutorial (3 hr)
Week 01	Chau Chak Wing Museum visit	Field trip (2 hr)
Week 02	[Task 1.2] Spatial Arrays Intermediate Grasshopper and design workflow. Continuation of component relationships, datasets and data trees.	Tutorial (3 hr)
Week 03	[Task 1.3] Spatial Composites Expanding on the booklet structure, content, layout and design for Assessment 1 submission. Progress Review Session.	Tutorial (3 hr)
Week 04	[Task 1.4] Fields and forces – Assessment 1 (30%) Due. Introducing Assessment 2: Bio-Inspired Prototypes	Tutorial (3 hr)
Week 05	[Task 1.5] Assignment 1 Presentation. Submission due.	Presentation (3 hr)
Week 06	[Task 2.2] Bio-Prototypes. Advanced computational concepts.	Tutorial (3 hr)
Week 07	[Task 2.3] Hybrid Volumes	Tutorial (3 hr)
Week 09	[Task 2.4] Bio-Inspired Prototypes - Assignment 2 Submission due. Presentation	Presentation (3 hr)
Week 10	[Task 3.1] Hybrid or Chimera of bio typologies applied on a different scale – as the responsive spatial environment.	Tutorial (3 hr)
Week 11	[Task 3.2] Animation and Interactions. Interactive environments (Unreal 1).	Tutorial (3 hr)
Week 12	[Task 3.3] Immersive Environments (Unreal 2)	Tutorial (3 hr)
Week 13	[Task 3.4] Preparation for Submission of Assignment 3. Submission due.	Tutorial (3 hr)

Attendance and class requirements

Attendance is compulsory.

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.

At the completion of this unit, you should be able to:

LO1. exercise critical thinking to empirically and creatively research and replicate a pre-existing system/precedent
LO2. demonstrate competency in computational design, 3D modelling, digital fabrication, image-generative AI and associated software literacy
LO3. utilise an understanding of the computational process in order to produce a wide range of explored solutions to a design problem
LO4. utilise rendering and simulation software as well as post-production techniques to produce carefully crafted visualisations and dynamic simulations
LO5. integrate knowledge gained on computational strategies into the design process to enable creative and innovative interactive solutions at various scales

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

Alignment with Competency standards

Outcomes	Competency standards
	National Standard of Competency for Architects - 3.1. Design response integrates the objectives of brief, user intent and built purpose. 3.2. Application of creative imagination, aesthetic judgement and critical evaluation in formulating design options.
	National Standard of Competency for Architects - 3.8. Application of manual and digital graphic techniques and modelling to describe three-dimensional form and spatial relationships.
	National Standard of Competency for Architects - 3.5. Exploration and application of ordering, sequencing and modelling of three-dimensional form and spatial content.
	National Standard of Competency for Architects - 3.5. Exploration and application of ordering, sequencing and modelling of three-dimensional form and spatial content. 3.8. Application of manual and digital graphic techniques and modelling to describe three-dimensional form and spatial relationships.
	National Standard of Competency for Architects - 3.2. Application of creative imagination, aesthetic judgement and critical evaluation in formulating design options. 3.5. Exploration and application of ordering, sequencing and modelling of three-dimensional form and spatial content.

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Increased the proportion of individual assessments and work vs group work. Reduced deliverables.

Additional information

Additional costs

Additional costs will be incurred for physical outputs using the digital fabrication equipment in DMAF (3D printing).

Work, health and safety

Students will be required to use 3D printing for their assignments.

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

Use of generative artificial intelligence (AI) and automated writing tools

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

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Work, health and safety

Disclaimer

On this page

Useful links

MARC6102: 3D Computer Design Modelling

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI) and automated writing tools

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

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Work, health and safety

Disclaimer

On this page

Useful links