Outline

Overview

This unit will provide an overview of the different research methods that are used in Computer Science. Students will learn to find and evaluate research on their topic and to present their own research plan or results for evaluation by others, write a literature review, learn about research quality metrics and ethics. This unit of study is required for students in Computer Science who are enrolled in a research project as part of their Honours or MIT/MITM degree, and for students starting their research degree (PhD, MPhil) in the School of Computer Science.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	INFO4990
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Sasha Rubin, sasha.rubin@sydney.edu.au
Lecturer(s)	Sasha Rubin, sasha.rubin@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Presentation	A3.b Oral presentation Oral presentation (given in person)	25%	Multiple weeks Due date: 29 May 2025 at 23:59 Closing date: 29 May 2025	Multiple weeks
Outcomes assessed:
Small continuous assessment	A3.c Peer reviews Peer reviews of other students' presentations, to be submitted online	15%	Multiple weeks Due date: 30 May 2025 at 23:59 Closing date: 01 Jun 2025	Multiple weeks
Outcomes assessed:
Assignment	A1. Annotated bibliography and database search result, Identifying top research community/venues, identifying valid Research Problems In DOC or PDF format, through Canvas/Turnitin, and identical copy to main supervisor in the School of Computer Science	15%	Week 04 Due date: 19 Mar 2025 at 23:59 Closing date: 21 Mar 2025	multiple weeks
Outcomes assessed:
Assignment	A2. Literature review and Outline of research proposal In DOC or PDF format, through Canvas/Turnitin, and identical copy to main supervisor in the School of Computer Science	35%	Week 08 Due date: 02 May 2025 at 23:59 Closing date: 02 May 2025	Multiple weeks
Outcomes assessed:
Assignment	A3.a Slides In PDF format, through Canvas/Turnitin.	10%	Week 10 Due date: 12 May 2025 at 23:59 Closing date: 14 May 2025	multiple weeks
Outcomes assessed:
= hurdle task ? = AI allowed ?

Assessment summary

Assignment 1 [15%] - Setting Research Context: List of top conferences and journals, main research groups, two exemplary papers, two or three open research problems in the field, annotated bibliography of core relevant articles.

Assignment 2 [35%]: Literature review and outline of research proposal.

Assignment 3 [50%]:

3a: Slides (10%)

3b: Oral presentation (25%)

3b: Peer reviews (15%)

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 sydney.edu.au/students/guide-to-grades.

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:

Assignments have a non-standard late submission and penalty. Written assignments A1 and A2 can be submitted a maximum of 5 days late without penalty; written assignments A3.a and A3.c can be submitted a maximum of 2 days late without penalty. No submissions will be accepted after that (and will receive a grade of 0) without approved SC. There will be no rescheduling of presentations without approved SC. For presentations, students must abide to the day and time indicated on the presentation schedule or will receive 0.

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
Week 01	Introduction to CS Research Methods; Identifying Research Problems	Lecture (2 hr)
Week 02	Information gathering (Librarian); Build an annotated bibliography (Learning Hub)	Lecture (2 hr)
Week 03	Clear writing workshop (Learning Hub)	Lecture (2 hr)
Week 04	How to Write a Literature Review and Research Proposal (Learning Hub)	Lecture (2 hr)
Week 05	How to read a computer science paper; How to write a literature review in computer science	Lecture (2 hr)
Week 06	Research Ethics	Lecture (2 hr)
Week 07	Peer Review Workshop on Literature Review assignment drafts (Learning Hub)	Lecture (2 hr)
Week 08	Mastering oral presentations workshop (Learning Hub)	Lecture (2 hr)
Week 09	Critique slides in class; How to give peer reviews for oral presentations	Lecture (2 hr)
Week 10	Student presentations and peer feedback	Lecture (2 hr)
Week 11	Student presentations and peer feedback	Lecture (2 hr)
Week 12	Student presentations and peer feedback	Lecture (2 hr)
Week 13	Student presentations and peer feedback	Lecture (2 hr)

Attendance and class requirements

Students are expected to attend all classes.

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

These will be posted in Canvas and/or Ed as the semester progresses.

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. demonstrate knowledge of major approaches used in CS research and ability to apply one or more to own project
LO2. plan a program of research in Computer Science
LO3. perform a critical evaluation of research work
LO4. retrieve academic literature on a specific topic and identify top journals, conferences, research groups on this topic
LO5. produce a literature survey for a field of CS research
LO6. write and present a research proposal
LO7. understand ethical practices and copyrights
LO8. read research literature and understand how the described work fits into one or more research approaches
LO9. understand the nature of CS research and how research is evaluated.
LO10. understand scientific peer reviewing and how to give constructive feedback
LO11. communicate research ideas and proposal in a clear, effective way in oral and written form

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.

Added workshops on "Clear writing", and lectures on "Slides critique" and "How to read an academic paper".

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

INFO5993: Computer Science Research Methods

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links