SURG5007: Semester 2, 2021

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Units SURG5007 Semester 2 2021 [Supervision]

Unit outline_

SURG5007: Dissertation A

Semester 2, 2021 [Supervision] - Remote

Overview

The dissertation is a formal piece of writing relevant to the subject area of the Masters' degree. Candidates will work on a specified research project under appropriate supervision. At least one of the project supervisors must be an academic staff member of the University. The dissertation is in Parts A and B, both of which will be completed in a minimum of one year of full-time study or two years of part time study.

Unit details and rules

Academic unit	Surgery
Credit points	9
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Peter Moritz, peter.moritz@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Literature Review Literature Review	0%	Formal exam period	up to 2000 words
Outcomes assessed:
Online task	Module Discussion Boards Discussion post	0%	Ongoing	400 words or less
Outcomes assessed:

Assessment summary

Module Discussion Board: Fortnightly discussion board which aims to assist with tracking of your project progress throughout semester.

Literature Review: Up to 2,000 word written literature review identifying gaps and developing an appropriate research question will need to be submitted to your research supervisor for written approval.

Detailed information 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
Week 01	Module 1: The Research Question	Forum (10 hr)
Week 03	Module 2: The Search Strategy	Forum (10 hr)
Week 05	Module 3: Data Plan and Ethics	Forum (10 hr)
Week 07	Module 4: Methods	Forum (10 hr)
Week 09	Module 5: Progress	Forum (10 hr)
Week 11	Module 6: Timelines	Forum (10 hr)

Attendance and class requirements

Active supervisor relationship in area of interest and an introductory understanding of research, ethics and statistics.

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 9 credit point unit, this equates to roughly 180-225 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. Demonstrate how to formulate a research question to address a gap in the current literature
LO2. Discuss how to perform a literature review and how to conduct a systematic review
LO3. Consider data required for your project and ethics requirements
LO4. Outline and present your research plan within a time frame achievable with the Surgical Dissertation program

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.

We value your feedback about any aspect of the unit of study and your experience as a student of Sydney Medical School. To help ensure our courses meet your needs and maintain a high standard, we welcome your feedback at any time and we ask you to complete the Mid-Semester Evaluation Survey and the unit of study Evaluation Survey at the end of the semester. You can also rate any component of the unit using our star rating system found at the bottom of many pages as you progress through the unit. Your ratings and comments are anonymous and specifying what you liked and didn’t like about any of the learning materials, assessment items, discussion forums, feedback etc will help us to target our improvement efforts. Please note that your participation in this unit of study permits de-identified information about your learning experience and interaction with learning resources to be used for the purpose of improving the student learning experience.

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

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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