Outline

Overview

This unit introduces students to evidence-based HR and some of the different ways in which data can be used to make insightful, informed, and effective evidence-based business decisions. The unit begins with an introduction to some of the basics of data analysis before looking at examples of how data can be used to provide meaningful insights in a range of different HR functions. The weekly workshops and class assignments provide students with the opportunity to develop their analytic and communication skills to turn data into insights, which is crucial for evidence-based HR.

Unit details and rules

Unit code	WORK6010
Academic unit	Work and Organisational Studies
Credit points	6
Prohibitions ?	None
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Vanessa Loh, vanessa.loh@sydney.edu.au

Type	Description	Weight	Due	Length
Supervised exam ?	Final exam Extended answer/short essay questions	30%	Formal exam period	2 hours
Outcomes assessed:
Presentation	Group presentation 10-minute presentation + 15-minute facilitated discussion	20%	Multiple weeks	25 minutes
Outcomes assessed:
Participation	Participation Workshop participation	10%	Ongoing	Ongoing
Outcomes assessed:
Small test	Tutorial quiz + oral test 25min quiz (multiple choice + short answer questions) + 5min oral test	10%	Week 05	25 minutes + 5 minutes
Outcomes assessed:
Assignment	Group report Written report	30%	Week 12 Due date: 15 May 2023 at 23:59 Closing date: 29 May 2023	2500 words
Outcomes assessed:
= group assignment ?

Assessment summary

Participation: Students will be assessed based on a combination of attendance, preparation, and participation in class discussions and activities. Students MUST attend at least 75% of classes (i.e., 9 out of 12 workshops) in order to be eligible for this mark. A participation grade of zero (0) will be allocated to students who attend less than 75% of classes. Students are expected to take responsibility to ensure that their attendance is recorded in class each week.
Tutorial quiz + oral Q&A: A 25-minute tutorial quiz and 5-minute oral Q&A will be held in week 5 to test student knowledge and learning from the first 4 weeks of the unit. The tutorial quiz will be held in your week 5 workshop, while the oral Q&A will be held outside of class time.
Group presentation: During the week 8, 9, or 10 workshop, groups will present their evidence-based hypotheses, planned analyses, expected results and potential business insights (10-minute presentation) and facilitate a 15-minute class discussion related to their presentation. Students will be assessed both individually (10%) and as a team (10%).
Group report: Groups of 3-5 students in the same workshop (tutorial) will work together to create a HR data insights report that would be appropriate for a general business audience, academic researchers, as well as senior leadership executives. The group report should include an executive summary, background and introduction, an analysis plan, the actual results and data-driven business insights, based on an analysis of a hypothetical human-resource dataset that will be provided in class.
Final exam: The final exam will require students to critically analyse, reflect on, synthesise, and apply their knowledge, learning and understanding of unit content from the lectures, required readings, and earlier in-semester assignments to new situations.

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

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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	Introduction to evidence-based HR	Lecture (1 hr)
Week 01	Introduction to evidence-based HR	Workshop (2 hr)
Week 02	Data collection, sampling and measurement	Lecture (1 hr)
Week 02	Data collection, sampling and measurement	Workshop (2 hr)
Week 03	Hypothesis testing, descriptives and t-tests	Lecture (1 hr)
Week 03	Hypothesis testing, descriptives and t-tests	Workshop (2 hr)
Week 04	ANOVA, regression and reporting your results	Lecture (1 hr)
Week 04	ANOVA, regression and reporting your results	Workshop (2 hr)
Week 05	Staffing, selection and recruitment	Lecture (1 hr)
Week 05	Staffing, selection and recruitment	Workshop (2 hr)
Week 06	Training and development	Lecture (1 hr)
Week 06	Training and development	Workshop (2 hr)
Week 07	Job performance, reward and remuneration	Lecture (1 hr)
Week 07	Job performance, reward and remuneration	Workshop (2 hr)
Week 08	Employee engagement	Lecture (1 hr)
Week 08	Employee engagement	Workshop (2 hr)
Week 09	Employee health and wellbeing	Lecture (1 hr)
Week 09	Employee health and wellbeing	Workshop (2 hr)
Week 10	Work teams and collaboration	Lecture (1 hr)
Week 10	Work teams and collaboration	Workshop (2 hr)
Week 11	Absenteeism and turnover	Lecture (1 hr)
Week 11	Absenteeism and turnover	Workshop (2 hr)
Week 12	Key challenges for HR data analytics	Lecture (1 hr)
Week 12	Key challenges for HR data analytics	Workshop (2 hr)
Week 13	Review and exam preparation	Lecture (1 hr)

Attendance and class requirements

Lecture recordings: Weekly lectures will be pre-recorded and made available on Canvas for student use. Live lectures and workshops (tutorials) will NOT be recorded so students should ensure that they attend and participate in all their designated workshops and attend the live lectures as required.

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

All readings for this unit can be accessed through the unit’s Reading List, available on Canvas.

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. explain the importance of taking an evidence-based and analytical approach to human resource management
LO2. understand the principles of evidence-based practice and how to apply them to solve HR and business problems
LO3. analyse critically the potential for human resource management to solve business problems, enhance competitive advantage, and boost individual and collective performance through the use of data-driven analytics and evidence-based practice
LO4. analyse complex datasets using appropriate analytical tools and techniques to provide meaningful insights for more effective and evidence-based business decisions
LO5. present and communicate information about data, analysis, and insights from the data, clearly, succinctly, and persuasively, in both written and oral form, in ways that are appropriate for different target audiences
LO6. work collaboratively with people from diverse backgrounds to address complex and unfamiliar problems using data-driven analytics and evidence-based practice.

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.

In response to student feedback, there is now an oral Q&A assessment in Week 5 to give students an opportunity to improve their ability to communicate clearly prior to the group presentation.

Additional information

More information can be found on Canvas.

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

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

On this page

Useful links

Media

Student links

About us

Connect

Current students

WORK6010: HR Data Insights

Semester 1, 2023 [Normal evening] - 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

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

On this page

Useful links

Media

Student links

About us

Connect