PMGT5861: Semester 1, 2022

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Units PMGT5861 Semester 1 2022 [Supervision]

Unit outline_

PMGT5861: Project Leadership Thesis B

Semester 1, 2022 [Supervision] - Remote

Overview

Project Leadership Thesis A and B provide an opportunity for students to undertake a major project in a specialised area relevant to Project Leadership. Students will work individually to plan and write reports. Project Leadership Thesis can be spread over a whole year, in two successive Units of Study of 6 credits points each, Project Leadership Thesis A (PMGT5860) and Project Leadership Thesis B (PMGT5861). This particular unit of study, which must be preceded by or be conducted concurrently with PMGT5860 Project Leadership Thesis A, should cover the first half of the work required for a complete thesis project. In particular, it should include completion of all components planned but not undertaken or completed in PMGT5860 Project Leadership Thesis A. Project Management Leadership Thesis A and B is aimed at providing candidates with an opportunity to develop deeper understanding of issues related to different aspects of leadership in managing projects in complex setting. This is an independent study guided by regular academic supervision as well as feedback from industry experts or advisors who would be serving on thesis panel. Candidates would be required to bring real world problem from their current work environment and develop rigorous analysis of the problem by summarising the existing literature and practices. Most of the work would comprise using case study, interpretative study and or action research as a methodological approach to investigate and analyse the phenomenon under investigation.

Unit details and rules

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

Teaching staff

Coordinator	Lynn Crawford, lynn.crawford@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Thesis Refer to Masters by Coursework Thesis Manual for details.	90%	Formal exam period	15000-20000 words
Outcomes assessed:
Presentation	Seminar Presentation Presentation of key points of thesis. Refer to Thesis Manual for details.	10%	Week 12	10 minutes
Outcomes assessed:

Assessment summary

Assessment for this Unit is based on

Seminar presentation of key findings
Submission of completed thesis

Refer to Masters by Coursework Thesis Manual available from the Project Research Studies site in Canvas for details. Final thesis must also be submitted, using Turnitin, on the Project Research Studies site.

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	Learning outcomes
Multiple weeks	Meetings with Supervisor	One-to-one tuition (10 hr)
Ongoing	Conduct of research, analysis, discussion, documented in academic thesis	Independent study (140 hr)

Attendance and class requirements

This Unit primarily involves independent study. You should plan to meet regularly with your supervisor throughout the Semester. Plan your thesis as you would a project.

Additional information is available Canvas on the Project Research Studies site. All submitted work should be submitted via the links provided on the Project Research Studies site: https://canvas.sydney.edu.au/courses/11377

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

The Library: Visit the University Library to familiarise yourself with available resources: https://www.library.sydney.edu.au/research/

Reference Manager: Start using a reference manager to keep track of your references and make it much easier to cite them when you write up your thesis. The University provides Endnote but other excellent free reference managers are available eg www.zotero.org.

Read about research design and methods:

Suggested resources:

The following are available online:

Beverly Pasian. (2016). Designs, Methods and Practices for Research of Project Management. Taylor and Francis. https://doi.org/10.4324/9781315270197

Cassell, C., Cunliffe, A. L., & Grandy, G. (2017). The SAGE Handbook of Qualitative Business and Management Research Methods: History and Traditions. SAGE Publications. https://doi.org/10.4135/9781526430212

Hair, J. F., Page, M., & Brunsveld, N. (2019). Essentials of Business Research Methods. Routledge.

The following are excellent but not available in online versions:

Bryman, Alan., & Bell, E. (2015). Business research methods (Fourth edition.). Oxford University Press.

Veal, A. James. (2005). Business research methods: A managerial approach (2nd ed.). Pearson Addison Wesley.

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. Drive a research project and take ownership in its articulation, design, planning, execution and documentation.
LO2. Conduct a critical review of literature relevant to a chosen topic with focus on identifying key themes, contributions, research gaps and issues.
LO3. Articulate and justify a researchable question or questions suitable for investigation.
LO4. Identify, select and justify relevant theory, research philosophy and research methods suitable for addressing a research question or problem in an academic manner.
LO5. Apply selected research method(s) with consideration of reliability and validity of results.
LO6. Draw conclusions based on raw data analysis and refine those conclusions in context based on consideration of methods and assumptions involved.
LO7. Demonstrate originality, ingenuity and initiative in dealing with critical research issues.
LO8. Document and report research work that demonstrates a clear line of argument and follows a format appropriate for academic literature.
LO9. Demonstrate ability to disseminate research work accurately, informatively and constructively to both academic and non-academic audiences in a professional manner.

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.

No changes have been made since this unit was last offered

Additional information

Late Penalty:

For every calendar day up to and including ten calendar days after the due date, a penalty of 5% of the maximum awardable marks will be applied to late work. The penalty will be calculated by first marking the work, and then subtracting 5% of the maximum awardable mark for each calendar day after the due date. Example: Consider an assignment's maximum awardable mark is 10; the assignment is submitted 2 days late; and the assignment is marked as 7/10. After applying the penalty, marks will be: 7 - (0.5 x 2) = 6/10. For work submitted more than ten calendar days after the due date a mark of zero will be awarded. The marker may elect to, but is not required to, provide feedback on such work. Refer to section 7A of Assessment procedures policy available at: http://sydney.edu.au/policies/showdoc.aspx?recnum=PDOC2012/267&RendNum=0

Study Commitment:

Text-matching software for Assignment Submission:

As part of the assessment process, text matching software such as Turnitin will be used to identify plagiarism and/or be used for providing feedback.

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

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