Outline

Overview

The Job Smart Edge unit is designed to provide students with the employability skills and tools to work effectively in an organisation. The unit features a client project bootcamp jointly delivered by careers and employability practitioners and educators. The focus is on preparing students to successfully undertake industry-focused problem-solving work in diverse teams. The learning journey includes an intensive 2-week virtual Digital Industry Project with a real client, with pre- and post-project employability workshops to provide students with the skills, models and tools to allow them to navigate and secure further employability opportunities. Workshops cover learning how to plan a project, launch a project, ensure project engagement, wrap up a project and communicate about a project. The assessments are designed to assist students with setting personal, team and project goals, with working on a project challenge and preparing a report for a client, and with developing and reflecting on individual employability capabilities developed through the program (including innovative problem solving and interpersonal leadership skills). By the end of the unit it is expected that students will be more equipped and prepared to face the world of employment.

Unit details and rules

Academic unit	Work Integrated Learning Hub
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	BUSS6102
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Gaia Grant, gaia.grant@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Assessment 1: Project report Preparing a draft report for the Digital Industry Project	35%	Week 03 Due date: 09 Oct 2022 at 23:59 Closing date: 11 Oct 2022	2000 words
Outcomes assessed:
Presentation	Assessment 2: E-portfolio Compiling & presenting evidence of employability skills development	35%	Week 05 Due date: 23 Oct 2022 at 23:59 Closing date: 02 Nov 2022	1000 Words / 5 web pages
Outcomes assessed:
Assignment	Assessment 3: Process report Reflection & evidence of attendance & participation in Seminars & Workshops	30%	Week 06 Due date: 30 Oct 2022 at 23:59 Closing date: 09 Nov 2022	1500 words
Outcomes assessed:
= group assignment ?

Assessment summary

Assessment 1: Project report This assignment involves the preparation of an authentic group project report for a client as part of the Job Smart Edge Digital Industry Project. It requires the effective practical application of the business models, skills and tools demonstrated and trialled in the seminar and workshop sessions.

Assessment 2: E-portfolio The e-portfolio assignment is designed to provide evidence of employability skills development throughout the unit. In this assignment students compile a personal employability e-portfolio from relevant notes in the process diary, and each student is then required to present their portfolio to the unit mentors and peers and respond to questions as part of an employment presentation simulation.

Assessment 3: Process report In this assessment, students are required to provide evidence of attendance and participation in the seminars and workshops. The evidence includes goal setting and process diary reflection entries to demonstrate seminar and workshop participation and skills development, based on the practical project management experiences from the Digital Industry Project and readings from the literature.

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	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
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.

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:

As per Business School Policy, any assessment submitted after the due time and date will incur a late penalty of 5% of the total marks per 24-hour period, or part thereof, late (note that this is applied to the mark gained after the submitted work is marked). Since submission is electronic, weekends and public holidays count as days in the same way as working days.

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	1. PLANNING A PROJECT: Introduction to the course, the experiential & reflective project-based approach, the assessments, the project team, and to project planning skills. Models, skills & tools for framing a challenge and planning accordingly. NOTE: Each week includes - Seminar (1hr, online) + Workshop (2hrs - online and face-to-face options available)	Block teaching (3 hr)
Week 02	2. LAUNCHING A PROJECT: Models, skills and tools for identifying original, well-structured & strategic approaches for project work.	Block teaching (3 hr)
Week 03	3. PROJECT ENGAGEMENT: Models, skills and tools for involving individuals and teams in collaborative and objectively effective processes. Digital Industry Project (WK1)	Block teaching (14 hr)
Week 04	4. WRAPPING UP A PROJECT: Models, skills and tools for highlighting and testing unique and practical solutions for implementation, and evaluating success. Digital Industry Project (WK2)	Block teaching (14 hr)
Week 05	5. COMMUNICATING EFFECTIVELY: Developing skills for presenting your employability capabilities and your ideas.	Block teaching (3 hr)
Week 06	6. FINAL PRESENTATIONS	Block teaching (3 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. Identify and develop innovative problem-solving capabilities and skills that will enhance employability.
LO2. Cultivate the ability to work effectively with others from diverse cultural and disciplinary backgrounds.
LO3. Develop oral and written communication skills through interactions with industry stakeholders, using a range of communication and presentation modes.
LO4. Evaluate issues related to employability and professional development by reflecting on experiential learning opportunities in the light of research.

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.

Adding ULOs and assessments as endorsed by the UoS Subcommittee meeting (30/03/2021).

Additional information

Business School Policies

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

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

BWIL6573: Job Smart Edge

Intensive September - October, 2022 [Block mode] - Camperdown/Darlington, Sydney

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

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect