Outline

Overview

This unit will equip you with vital skills in researching, writing, communicating and thinking in the Mathematical Sciences. These are skills that are used every day by professionals in mathematics, statistics, data science and other branches of mathematical science and in areas where mathematics and statistics is applied, such as science, computation and finance. The skill that you will master include logical reasoning, collaborative group work, skills in reading and writing mathematics, giving feedback, typesetting mathematics, and giving formal and informal presentations. You will also work together with other students on a small research project, as you learn about research processes in the mathematical sciences. As part of this, you will learn about open research problems in the mathematical sciences, to acquire the skills to conduct a review of current knowledge on a topic, and you will practice posing and tackling original research questions. This unit will also give you the opportunity to meet and work with other students in your Bachelor of Mathematical Sciences cohort.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Stephan Tillmann, stephan.tillmann@sydney.edu.au
Lecturer(s)	Oded Yacobi, oded.yacobi@sydney.edu.au
Lecturer(s)	Stephan Tillmann, stephan.tillmann@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written work	Essay Essay on a mathematical topic	35%	Formal exam period	Approximately 2000-3000 words	AI allowed
Outcomes assessed:
Out-of-class quiz	Reading and Writing Mathematics Four online modules and quizzes with automated feedback, estimated as a total of 10 hours of student effort over 2 weeks	0%	Multiple weeks	10 hours	AI allowed
Outcomes assessed:
Out-of-class quiz	Mathematical Writing Mechanisms Three online modules and quizzes with automated feedback estimated as a total of 10 hours of student effort over 2 weeks	0%	Multiple weeks	10 hours	AI allowed
Outcomes assessed:
Written work	Draft essay Written outline or draft for an essay on a mathematical topic	5%	Week 08	2 pages	AI allowed
Outcomes assessed:
Presentation	Project slides Slides for the group research project presentation	15%	Week 11	Approximately 12 slides	AI allowed
Outcomes assessed:
Interactive oral	Project presentation Students present findings and delegate for Q&A	10%	Week 12	10 minutes plus Q&A	AI prohibited
Outcomes assessed:
Interactive oral	Project interview Students give a short presentation and answer 2-3 questions about their contributions to the group research project	15%	Week 13	5-7 minutes	AI prohibited
Outcomes assessed:
Evaluation	Active Participation in Seminars Writing exercises, peer feedback, presentations, participation	20%	Weekly	Ongoing - see Canvas for details	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Active Participation in Seminars: Contribute to weekly seminars. Writing exercises, peer feedback, presentations, participation. See Canvas for further details.

Online Modules

Reading and Writing: 4 modules to complete. Students are required to pass the online quizzes in order to pass this unit.
Writing Mechanisms: 3 modules to complete. Students are required to pass the online quizzes in order to pass this unit.

Individual Essay

Essay: Essay on a mathematical topic, approx. 2000-3000 words.
Draft Essay: 2 pages of essay outline or draft, submitted week 8 for feedback. No simple extension allowed.

Group Research Project

Project Slides: Slides for the group research project presentation, submitted to Canvas. No simple extension allowed.
Project Presentation: Students present findings and delegate for Q&A, 10 minutes presentation plus Q&A. No simple extension allowed.
Project Interview: Students give a short presentation and answer 2-3 questions about their contributions to the group research project

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	Representing complete or close to complete mastery of the material.
Distinction	75 - 84	Representing excellence, but substantially less than complete mastery.
Credit	65 - 74	Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.
Pass	50 - 64	Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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 of Draft Essay, Project Slides and Project Presentation is prohibited. The usual penalties apply to all other assessment items.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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
Week 01	Ice breaker activities; introduction to research projects	Seminar (2 hr)
Week 01	Introduction to the unit	Workshop (1 hr)
Week 02	Discussion of writing techniques, analyse essays in groups	Seminar (2 hr)
Week 02	Research group meeting	Workshop (1 hr)
Week 03	Interrogating and researching objects at the Chau Chak Wing Museum	Seminar (2 hr)
Week 03	Research group meeting	Workshop (1 hr)
Week 04	Peer feedback on writing; online editors	Seminar (2 hr)
Week 04	Research group meeting	Workshop (1 hr)
Week 05	Databases, MathSciNet, researching a topic, AI	Seminar (2 hr)
Week 05	Research group meeting	Workshop (1 hr)
Week 06	Individual essay topics: presentation, ideas, feedback	Seminar (2 hr)
Week 06	Research group meeting	Workshop (1 hr)
Week 07	Current research in the mathematical sciences	Seminar (2 hr)
Week 07	Research group meeting	Workshop (1 hr)
Week 08	Current research in the mathematical sciences	Seminar (2 hr)
Week 08	Research group meeting	Workshop (1 hr)
Week 09	Peer feedback session on essay drafts	Seminar (2 hr)
Week 09	Research group meeting	Workshop (1 hr)
Week 10	Student presentations on essay topics	Seminar (2 hr)
Week 10	Research group meeting	Workshop (1 hr)
Week 11	Student presentations on essay topics	Seminar (2 hr)
Week 11	Research group meeting	Workshop (1 hr)
Week 12	Problem solving activities	Seminar (2 hr)
	Research group meeting	Workshop (1 hr)
	Project presentation event	Seminar (3 hr)
Week 13	Individual interviews	Seminar (2 hr)

Attendance and class requirements

Attendance at Seminars and Workshops is required. The activities are NOT available for online participation.

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

See the Canvas site for reference material.

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. List and compare common logical fallacies
LO2. Explain and utilise the effectiveness of proper definitions, and the interaction between narrative and logic
LO3. Analyse and apply the principles of written and oral mathematical communication
LO4. Evaluate and synthesise the current state of knowledge about a mathematical problem by obtaining information from a range of scientific resources (books, journals, online, meetings with experts)
LO5. Solve a mathematical problem independently and in collaboration with a team of researchers
LO6. Effectively communicate and collaborate in a group
LO7. Formulate innovative and creative ideas

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 this unit has been offered.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

MATH1080: Research and Communication in Math. Sci.

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links