Course coordinator
Professor Andrew Cresswell
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Human Movement & Nutrition Sci
Recent developments in sports technology have increased our ability to record and store large volumes of data. This course is designed to integrate contemporary methods of data collection around sport and athletic performance, with analytical techniques used to evaluate, visualise and present large data sets. While theoretical lectures form a significant part of the course, a large component will be project based where students will collect, analyse and present their own large data set. Students will be exposed to advanced technology and measurement methods as well as analytical software that has become the standard used by sporting organisations, clubs and teams. A capstone experience will be the development of an 'entrepreneurial pitch' focused towards the sports technology industry.
Recent developments in sports technology have increased our ability to record and store large volumes of data. This course is designed to integrate contemporary methods of data collection around sport and athletic performance, with analytical techniques used to evaluate, visualise and present large data sets. While theoretical lectures form a significant part of the course, a large component will be project-based where students will collect, analyse and present a large data set. Students will be exposed to advanced technology and measurement methods as well as analytical software that are often used by sporting organisations, clubs and teams.ﾠ
Course requirements
Assumed background
Assumed background: Courses successfuly completed in the exercise and sports sciences - eg biomechanics, exercise physiology, strength and conditioning, motor control etc..
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
BIOL2630 or BIOL1630 and PHYL2730
Course contact
Course staff
Lecturer
Professor Andrew Cresswell
Mr Max Andrews
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
The aim of the course are to provide students with: 1) anﾠunderstanding of contemporaryﾠdata collection methods used in sport and exercise science 2) an understanding of signal processing and filtering of data 3)ﾠthe understanding of data bases and how they are developed and utilised in sports context 4) the ability to collect and visualise data using a commercial database system 5) the ability to understand how new and innovativeﾠtechnologies can be developed and potentially brought to market.ﾠ
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Develop an understanding of contemporarydata collection methods used in sport and exercise science
LO2.
Develop an understanding of signal processing and filtering of data
LO3.
Develop an understanding of data bases and how they are developed and utilised in sports context
LO4.
Develop an the ability to collect and visualise data using a commercial database system
LO5.
Develop an understanding of how new and innovativetechnologies can be developed and potentially brought to market.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation, Participation/ Student contribution |
Workshop skill development
|
30% |
Assessed during each timetabled workshop activity. |
| Quiz |
In-class quiz
|
35% |
31/03/2025
During timetabled lecture class. |
| Presentation | PowerBi Analysis and Dashboard | 35% |
30/05/2025 5:00 pm |
Assessment details
Workshop skill development
- In-person
- Mode
- Activity/ Performance
- Category
- Paper/ Report/ Annotation, Participation/ Student contribution
- Weight
- 30%
- Due date
Assessed during each timetabled workshop activity.
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
There are 10 scheduled workshops in which students will progressively develop skills across the parameters of the course content.
Use of generative Artificial Intelligence (AI) or Machine Translation (MT).
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Submission guidelines
Workshop skills to be assessed and checked by the workshop facilitator.
Written/produced work completed during workshops must be uploaded into Blackboard by 5pm the same day.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
Students are permitted to miss zero workshops for HMST3000 without receiving approval.
To be eligible to submit and be graded on the workshop assessment pieces, students must attend and participate in the relevant workshop according to the day and time of their timetabled workshop activity.
If the student does not attend their timetabled workshop then a grade of 0 will be awarded to that weeks assessment piece. However, if the student has a legitimate reason for not attending with supporting evidence then an alternative assessment arrangement will be organised for the student.
Please follow the process for absences (below) as soon as you possibly know that you will be absent from a workshop. The earlier we are made aware, the easier it is to make alternative arrangements.
If you are unable to attend for medical or extenuating circumstances, you are required to complete an absentee form and email the form as soon as possible to your course coordinator (a.cresswell@uq.edu.au) no later than two (2) calendar days after the date of the original class was held.
Failure to attend compulsory workshop activities without an approved absence will result in a grade of zero for the Workshop skill development assessment piece.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
In-class quiz
- In-person
- Mode
- Written
- Category
- Quiz
- Weight
- 35%
- Due date
31/03/2025
During timetabled lecture class.
- Learning outcomes
- L01, L02
Task description
There will be one in-class face-to-face quiz - which will be held during a scheduled lecture. The allotted time for the quiz will be 1.5 hours and will occur in Week 6. There will be a combination of multiple choice, short answer and possible calculation questions.
Use of generative Artificial Intelligence (AI) or Machine Translation (MT).
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.
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Your deferred quiz date and time will be determined by the course coordinator and communicated to you via your UQ student email account.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
PowerBi Analysis and Dashboard
- Mode
- Activity/ Performance
- Category
- Presentation
- Weight
- 35%
- Due date
30/05/2025 5:00 pm
- Learning outcomes
- L02, L03, L04, L05
Task description
This assessment piece will be a Powerpoint presentation about the developed PowerBI Dashboard, relevant to the chosen target group.
Use of generative Artificial Intelligence (AI) or Machine Translation (MT).
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Submission guidelines
To be submitted via the relevant Turnitin submission portal on the course Blackboard site.
Deferral or extension
You may be able to apply for an extension.
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.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 24 |
Absence of evidence of achievement of course learning outcomes. Course grade description: This grade is assigned through both formative and summative assessment tasks throughout the course Cut Off % range 0-24% of total marks. |
| 2 (Fail) | 25 - 44 |
Minimal evidence of achievement of course learning outcomes. Course grade description: This grade is assigned through both formative and summative assessment tasks throughout the course Cut Off % range 25-44% of total marks. |
| 3 (Marginal Fail) | 45 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: This grade is assigned through both formative and summative assessment tasks throughout the course Cut Off % range 45-49% of total marks. |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: This grade is assigned through both formative and summative assessment tasks throughout the course Cut Off % range 50-64% of total marks. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: This grade is assigned through both formative and summative assessment tasks throughout the course Cut Off % range 65-74% of total marks. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: This grade is assigned through both formative and summative assessment tasks throughout the course Cut Off % range 75-84% of total marks. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: This grade is assigned through both formative and summative assessment tasks throughout the course range 85-100% of total marks.ﾠ |
Additional course grading information
A final percentage mark will be rounded to the nearest whole number (e.g. 64.50 and above will be rounded to 65 and 64.49 and below will be rounded down to 64.)
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
Library resources are available on the UQ Library website.
Other course materials
If we've listed something under further requirement, you'll need to provide your own.
Required
| Item | Description | Further Requirement |
|---|---|---|
| Laptop or similar | For completing PowerBi component of the course | own item needed |
Additional learning resources information
Any ideas, difficulties or concerns can and should be discussed in the Blackboard discussion forum. Lecturers ﾠwill moderate the discussion threads to facilitate learning.
Learning materials will be posted at regular intervals on the course "Blackboard" site so students should regularly check the site for new and updated information
Where appropriate, abbreviated lecture notes will be made available after the lectures on Blackboard - so students are encouraged to write their own "complete" lecture notes during each lecture.
All lectures may ﾠNOT always be recorded due to the room being used and possible technical errors and therefore NOT available for review purposes - you are encouraged to attend ALL scheduled lectures and workshops.
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 (24 Feb - 02 Mar) |
Lecture |
Lecture Lecture : Introduction and Sports Technology. Types of data, analogue to digital conversion, sampling data. Learning Objectives: 1, 2 Learning outcomes: L01, L02 |
Workshop |
Workshop Intro. Research a piece of sports technology. Prepare 2-3 slides to present what you found (2-3 min) Workshop attendance 3% Learning outcomes: L01, L02 |
|
Week 2 (03 Mar - 09 Mar) |
Lecture |
Lecture Sports Technology Sampling data, frequency analysis, smoothing, filtering Learning outcomes: L01, L02 |
Workshop |
Workshop Signal processing Processing data in Spike2, power spectrum analysis, designing a filter (IIR and FIR) and filtering data. Workshop attendance 3% Learning outcomes: L01, L02 |
|
Week 3 (10 Mar - 16 Mar) |
Lecture |
Lecture Technologies in cycling as an example; sensors, GPS, computers etc Learning outcomes: L01, L02 |
Workshop |
Workshop Data processing - working with spreadsheets: Vlookup, Pivot tables etc Workshop attendance 3% Learning outcomes: L01, L02, L03 |
|
Week 4 (17 Mar - 23 Mar) |
Lecture |
Lecture Technologies in cycling as an example; sensors, gps, computers etc Learning outcomes: L01, L02, L03 |
Workshop |
Workshop GPS collection and display of data. Workshop attendance 3% Learning outcomes: L01, L02, L03, L04 |
|
Week 5 (24 Mar - 30 Mar) |
Lecture |
Lecture Movement analysis from film to marker-less Learning outcomes: L01, L02, L03, L04 |
Workshop |
Workshop Demonstration of markerless system Workshop attendance 3% Learning outcomes: L01, L02, L03, L04 |
|
Week 6 (31 Mar - 06 Apr) |
Lecture |
In-class quiz In-Class quiz 30% Learning outcomes: L01, L02, L03, L04 |
Week 7 (07 Apr - 13 Apr) |
Lecture |
Lecture Data Visualisation and Reporting Types of data visualizations, data-driven storytelling, sport-specific contextual factors, examples of sports data visualizations, and storyboarding for an Athlete Management System (AMS) Learning outcomes: L03, L04 |
Workshop |
Workshop Storyboarding for PowerBI Upload of storyboard to Bb 5% Learning outcomes: L03, L04 |
|
Week 8 (14 Apr - 20 Apr) |
Lecture |
Lecture Force Platforms Profiling and benchmarking, load-response tailoring, rehabilitation and return to play, considerations in force platform analysis Learning outcomes: L03, L04 |
Workshop |
Workshop Analytics: Forcedeck prac + dashboard for force plate metrics Workshop attendance 3% Learning outcomes: L02, L03, L04 |
|
Week 9 (28 Apr - 04 May) |
Lecture |
Lecture Subjective Monitoring Internal load via RPE, variations in subjective measures, assessing training and competition response, collecting sRPE in applied settings, limitations of sRPE, and developing a subjective monitoring system. Learning outcomes: L03, L04 |
Workshop |
Workshop |
|
Week 10 (05 May - 11 May) |
Lecture |
lecture Analytics: Strength Tracking Force-velocity power profiling, velocity-based training technology Learning outcomes: L03, L04 |
Workshop |
Workshop Analytics: Dashboard for strength tracking Workshop attendance Show appropriate templates, calculations and athletes in PowerBI Workshop attendance 3% Learning outcomes: L03, L04 |
|
Week 11 (12 May - 18 May) |
Lecture |
Lecture Theory, data collection, analysis, and decision-making in injury risk assessment. Learning outcomes: L03, L04 |
Workshop |
Workshop Nordbord prac plus dashboard with forceframe/nordbord/medical data Workshop attendance 3% Learning outcomes: L02, L03, L04 |
|
Week 12 (19 May - 25 May) |
Case-based learning |
Self directed learning Free week to develop dashboard and PPT presentation Learning outcomes: L04 |
Week 13 (26 May - 01 Jun) |
Lecture |
Lecture Entrepreneurship Learning outcomes: L04, L05 |
Case-based learning |
Independent study Preparation of presentation for submission PPT Submission to Bb 35% Learning outcomes: L03 |
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 - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course: