CSYS5061: Semester 1, 2020

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Units CSYS5061 Semester 1 2020 [Supervision]

Unit outline_

CSYS5061: Complex Systems Research Project B

Semester 1, 2020 [Supervision] - Camperdown/Darlington, Sydney

Overview

The research pathway project aims to provide: (a) analytical and computational skills for modelling systems characterised by many interacting heterogeneous variables, (b) adequate programming skills for simulating complex systems. It is aimed at developing a pathway to a research career. The student will work individually on an assigned open-ended research project, focussed on modelling a complex problem or delivering a novel solution. The concepts covered depend on the nature of the project. The project could be directly tied to student's area of specialisation (major), or to their vocational objectives or interests. Students with expertise in a specific industry sector may be invited to partner with relevant team projects. The project outcomes will be presented in a thesis that is clear, coherent and logically structured. The project will be judged on the extent and quality of the student's original work and particularly how innovative, perceptive and constructive they have been in developing and applying cross-disciplinary complex systems concepts. As the result, the student will develop capability for modelling complex systems, from the identification of the relevant variables and interactions to the analysis and simulations of the predictions, having learnt the conceptual and methodological tools (techniques and algorithms) for the analysis and inference of complex models.

Unit details and rules

Academic unit	Civil Engineering
Credit points	6
Prerequisites ?	CSYS5010
Corequisites ?	CSYS5060. Research Project A is meant to be done before or in parallel with Research Project B
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Mikhail Prokopenko, mikhail.prokopenko@sydney.edu.au

Type	Description	Weight	Due	Length
Presentation	Presentation	50%	Week 13	n/a
Outcomes assessed:
Assignment	Report	50%	Week 13	n/a
Outcomes assessed:

Assessment summary

Professionally written technical report: The report should outline the project outcomes, outputs and process, including methodologies selected, requirement analysis, design decisions, implementation details, observations, integration and testing details, results, output, and recommendations for future work.
Oral presentation:
A presentation submitted online (e.g., powerpoint). Opportunities for peer evaluation will be provided.

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
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
Multiple weeks	Progress meetings	One-to-one tuition (1 hr)
Week 01	Introduction to Capstone and planning session (note: can use Skype for non-local students)	Workshop (3 hr)
Week 13	Presentation and feedback session	Seminar (2 hr)
Weekly	Planning, research and implementation	Independent study (6 hr)

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. effectively work as a project team and communicate complex concepts requited to complex system analysis, design and implementation to other project members, academics, industry stakeholders
LO2. write technical reports, papers and other technical/academic documents, which convey the results of the project, and to prepare presentation and media which conveys the same
LO3. run a medium sized project effectively by demonstrating appropriate division of labour, regular meetings, innovation, analysis of complex ideas, comparison of design ideas, effective and timely implementation, appropriate testing and integration, effective communication and presentation
LO4. demonstrate professional conduct during the course of the project in order to complete it within time and by producing high quality outcomes
LO5. self-teach (individually and within a group setting) additional contextual knowledge about the project
LO6. compare alternative designs and modify design of the complex system during the course of the project
LO7. use advanced programming skills to analyse, understand and simulate the dynamics of complex systems
LO8. use literature review to understand the current state-of-the-art in complex systems analysis and apply such knowledge effectively in the project
LO9. self-teach (individually and within a group setting) additional programming techniques and software which are relevant to the project and not covered in MCXS previously
LO10. design complex systems in a given context that satisfy the requirements of the project.

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.

This is the first time I'm coordinating this unit.

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

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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