Course coordinator
Dr Caroline Knight
Course overview
- Study period
- Semester 1, 2026 (23/02/2026 - 20/06/2026)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
People analytics transforms HR through data-driven decision making. Apply HR theory in practice by exploring cutting-edge people analytics techniques that integrate statistical analysis with digital technologies to address real world HR challenges. Develop skills to translate complex findings into meaningful insights for stakeholders while navigating the ethical challenges in this rapidly growing field that is now an essential capability for modern HR professionals.
This course will help students understand some of the latest developments in the field of Human Resources, in particular the use of digital and analytic technologies in the management of people in organisations. The course will begin with contextualising HR analytics and its role in contributing to the HR function's offering to organisations. It will help explain and explore the use of people data in HR and how organisations are beginning to engage digital technologies and sophisticated analytic techniques to help make key HR related investment decisions. A key aim of the course is to ensure that students develop their analytic competency, both in understanding how and why organisations can apply analytics to answer HR related business problems but also to understand some of the techniques used in HR analytics. With a focus on some of the key pillars of HR analytics: Employee Engagement, Employee Performance and Turnover the students will begin to explore analytic modelling potential. Importantly the course will enable students to consider some of the logistical, ethical and methodological challenges faced by HR analytics teams.
MGTS3609 is one of the last courses for students to take with the Human Resources major in the Bachelor of Business (Management) degree. The course will help students be aware of the latest cutting-edge developments in the field of Human Resources. Digital developments in HR are fast moving and this course will go some way to prepare students for the potential challenges that they may face (with regards to the analyses of people data) if they decide to follow a career in HR.
Course Changes in Response to Previous Student Feedback
This course has undergone a major overhaul since Semester 2, 2025, to respond to student feedback, improve alignment with industry standards in statistical software, and build AI competencies. For the first time, in Semester 1, 2026, we will be using the open source software, R, to conduct HR People Analytic Analyses. Students will also engage with AI in HR People Analytic Activities. In response to student feedback, students will also be provided with the dataset for Assessment 2 (Group Presentation) and will be able to conduct their own analyses should they choose to.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
MGTS2604 + 4 units of Human Resources major courses
Course contact
Course staff
Lecturer
Dr Caroline Knight
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Please note: Teaching staff do not have access to the timetabling system to help with class allocation. Therefore, should you need help with your timetable and/or allocation of classes, please ensure you email business.mytimetable@uq.edu.au from your UQ student email account with the following details:
- Full Name
- Student ID
- Course Code
Aims and outcomes
A main aim of the course in the HR major, is to help students obtain HR analytic competency and insight. By the end of the module the students will not only have an understanding of HR Analytics as a specialism, but they will also be able to conduct statistical analyses and interpret statistical output to help answer a theoretically informed business problem. They will have developed an understanding various models of the role that HR Analytics can play in organisations and have gained a critical understanding of HR analytics as an HR activity.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Evaluate the strategic function, scope, methodologies, and organisational impact of HR People Analytics as a critical function within organisations.
LO2.
Critique theoretical and methodological approaches in HR People Analytics, identifying limitations and implications for evidence-based practice.
LO3.
Conduct and interpret statistical analyses on HR-related data and communicate the importance of the findings both as an individual and in a team.
LO4.
Evaluate ethical implications of HR People Analytics as applied in practice and propose appropriate mitigation strategies.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Quiz |
Series of 4 Quizzes
|
15% |
Quiz 1 Week 4 Wed - Week 4 Thu Quiz 2 Week 6 Wed - Week 6 Thu Quiz 3 Week 8 Wed - Week 8 Thu Quiz 4 Week 10 Wed - Week 10 Thu |
| Presentation |
Group Presentation
|
35% |
Week 11 - Week 12
All presentations and additional materials will need to be submitted via BB by 1pm Friday 8 May. Buddycheck peer evaluation due by 1pm Friday 22 May. |
| Examination |
Final Exam
|
50% |
End of Semester Exam Period 6/06/2026 - 20/06/2026 |
Assessment details
Series of 4 Quizzes
- Online
- Mode
- Written
- Category
- Quiz
- Weight
- 15%
- Due date
Quiz 1 Week 4 Wed - Week 4 Thu
Quiz 2 Week 6 Wed - Week 6 Thu
Quiz 3 Week 8 Wed - Week 8 Thu
Quiz 4 Week 10 Wed - Week 10 Thu
- Other conditions
- Time limited.
Task description
Students will be given a series of four quizzes (5% each) that test students' ongoing understanding of material from the course.
Students will be quizzed on various features of the course, including tutorial activities, lecture material and video material (presented in the lecture and/or posted on blackboard).
The quizzes will draw questions randomly from a pool of questions and be open for 24 hours.
AI Statement:
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Quizzes will be deployed on Blackboard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
Late submission
Exams submitted after the end of the submission time will incur a late penalty.
Group Presentation
- Identity Verified
- Team or group-based
- In-person
- Mode
- Oral
- Category
- Presentation
- Weight
- 35%
- Due date
Week 11 - Week 12
All presentations and additional materials will need to be submitted via BB by 1pm Friday 8 May.
Buddycheck peer evaluation due by 1pm Friday 22 May.
- Other conditions
- Time limited, Peer assessment factor, Secure.
Task description
Students will self-select into groups and create and sign a Group Contract.
In their groups, students will be given an HR analytics case study, R code for conducting analyses, and the associated dataset.
The case study will be provided on blackboard.
The groups will need to consider the case study, conduct and interpret the analyses, and present the importance of the findings to a hypothetical board of directors.
The presentation will need to outline the findings, what implications the analyses has for the organisation and set out recommendations in terms of what the organisation should be considering (regarding investments in HR) going forward.
The presentations will be recorded live in tutorials in Weeks 11 and 12 and all group members will need to take part in the presentation.
Each group member will also need to complete an individual peer evaluation of their group members' contributions.
Please note that you MUST be a member of a group in order to complete this group presentation task. Preliminary allocation to groups will take place in Week 3 tutorials. Students who do not attend tutorials in Week 3 and do not notify course staff by the end of Week 5 with an adequate reason for their absence with appropriate documentation will not be allocated to a group and may receive zero for this assessment task.
AI Statement:
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Submission will be via BB.
The presentations will be scheduled during tutorials in Weeks 11 and 12.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Final Exam
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 50%
- Due date
End of Semester Exam Period
6/06/2026 - 20/06/2026
- Other conditions
- Time limited, Secure.
Task description
This exam will present a case description of an analytics project with some analytics output. The students will be expected to interpret the output, report and summarise the findings along with an explanation of the implications that the findings have for HR decision making going forward. The students will also need to reflect on any ethical challenges that may arise from the project.
Students will be given 120 minutes for the exam, plus 10 minutes reading time, under timed conditions.
A mock exam will be provided to students before the revision session that takes place in the last week of semester.
More details will be provided on Blackboard.
AI Statement:
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | No calculators permitted |
| Open/closed book | Closed book examination - specified written materials permitted |
| Materials | One A4 sheet of handwritten or typed notes, single sided, is permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
This is an in-person exam. Exam papers will be collected at the end of the exam.
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 29 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Grades will be allocated according to University-wide standards of criterion-based assessment.
Supplementary assessment
Supplementary assessment is available for this course.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Own copy required
You'll need to have your own copy of the following reading resources. We've indicated below if you need a personal copy of the reading materials or your own item.
| Item | Description |
|---|---|
| Book |
Predictive HR Analytics: Mastering the HR Metric
by Edwards; Martin R.; Edwards; Kirsten; Jang; Daisung - 2024 Edition: 3rd edition Publisher: Kogan Page ISBN: 9781398615892; 9781398615656; 9781398615908 |
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 |
General contact hours |
Online Self Study Self-directed study - No Tutorial this week. |
Lecture |
Intro To People Analytics: HR in context How does HR Analytics as an offering fit into the broader sweep of HR activities and what does it bring to the field? |
|
Week 2 |
Lecture |
HR Function, Analytics Teams & Analytics Competency How do organisations arrange their HR Analytics teams and what kind of projects do they undertake? |
Tutorial |
HR Analytics Teams: Getting to know each other |
|
Week 3 |
Lecture |
Analytics HR and Technology What are the technological developments that we are seeing in HR that enable the full utilisation of HR Analytics? |
Tutorial |
HR Analytics Teams: Working in groups |
|
Week 4 |
Lecture |
Practitioner Talk Talk from an HR analytics expert about different projects HR data can be used for. This will give you real insight into the role of HR Analytics in organisations. It is essential that you attend. |
Tutorial |
Introduction to RStudio for People Analytics Learning outcomes: L03 |
|
Week 5 |
Lecture |
Engagement HR Analytics Why is employee engagement such an important focus of HR analytics teams? What is it? What analytic opportunities does engagement data enable? |
Tutorial |
Working with Rstudio for People Analytics Learning outcomes: L03 |
|
Week 6 |
Lecture |
Turnover HR Analytics Why is turnover such an important part if HR analytics activities? Can we predict turnover and cost it? What opportunities does this provide for HR? |
Tutorial |
Employee Engagement Analyses - Descriptives and Correlations Learning outcomes: L03 |
|
Mid-sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 7 |
Lecture |
Performance HR Analytics What do we mean by employee performance and what data might be useful as measures of performance? What are the limits to performance data and analytics? |
Tutorial |
Performance Analyses - Regression Learning outcomes: L03 |
|
Week 8 |
Lecture |
Diversity HR Analytics Why is HR analytics so important for the management of Diversity and inclusion in organisations? What kind of analytics might an organisation utilise with Diversity and Inclusion? |
Tutorial |
Hands on with the A2 dataset and Rubric Learning outcomes: L03, L04 |
|
Week 9 |
Lecture |
People Analytics - Stakeholder reporting It is one thing to be able to analyse and interpret HR data - but "so what"? What does it tell an organisation and how can you communicate the importance of the analyses? Reporting and convincing stakeholders of the importance of analytics is key. |
Tutorial |
AI Jam - Crafting Visuals, Reports and More Learning outcomes: L03 |
|
Week 10 |
Lecture |
Workforce Planning What is workforce planning and how does HR analytics help? |
Tutorial |
Automated Hiring |
|
Week 11 |
Lecture |
Practitioner Talk Talk from an HR analytics expert in a multinational organisation about the value add of HR analytics and a discussion of the use of machine learning in analytics. This will give you real insight into how HR Analytics is being used in this organisation. It is essential that you attend. |
Tutorial |
A2 Presentations A2 Presentations will occur live in your tutorials this week. |
|
Week 12 |
Lecture |
Ethical Considerations and Debates Just because you can analyse people data in the work place doesn't mean you should. What are some of the ethical challenges and opportunities of HR analytics? |
Tutorial |
A2 Presentations A2 presentations will occur live in your tutorials this week. |
|
Week 13 |
Lecture |
HR People Analytics - Revision |
Tutorial |
Exam Revision |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments for Students Policy and Procedure
- AI for Assessment Guide
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.