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
Students taking this course will engage critically and practically with the potential of digital technology and 'big' and 'small' data to enhance assessment processes in HPE.
EDUC3013 enhances your capacity to systematically collect and analyse a range of student data to inform and modify your teaching practices so that you can positively impact on student learning.
As a result of engagement in this course, you will:
a. Understand the importance of why and how we use data in our classrooms including to:
ﾠ ﾠ - Evaluate the effectiveness of current teaching practices to inform subsequent strategies;
ﾠ ﾠ - Create differentiated learning opportunities for students; and
ﾠ ﾠ - Provide effective feedback for learning to students and their parents/carers.
b. Acquire fundamental data literacy knowledge and skills used by teachers in their everyday work.
c. Develop a critical understanding of data and its uses including ethical and privacy implications.
d. Learn to use of a range of ICTs for collecting data in informal and formal diagnostic, formative and summative assessments.
e. Improve your ICT and communication skills for data visualisation and data storytelling.
f. Develop a positive disposition towards the use of data in schools.
Course requirements
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
EDUC2012
Recommended companion or co-requisite courses
We recommend completing the following courses at the same time:
EDUC3012
Incompatible
You can't enrol in this course if you've already completed the following:
EDUC3010
Restrictions
BHSPE; BHSPE(Hon) students; or with permission by HMNS Head of School
Course contact
Course staff
Lecturer
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
EDUC3013 enhances your capacity to systematically collect and analyse a range of student data to inform and modify your teaching practices so that you can positively impact on student learning.
As a result of engagement in this course, you will:
a. Understand the importance of why and how we use data in our classrooms including to:
ﾠ ﾠ - Evaluate the effectiveness of current teaching practices to inform subsequent strategies;
ﾠ ﾠ - Create differentiated learning opportunities for students; and
ﾠ ﾠ - Provide effective feedback for learning to students and their parents/carers.
b. Acquire fundamental data literacy knowledge and skills used by teachers in their everyday work.
c. Develop a critical understanding of data and its uses including ethical and privacy implications.
d. Learn to use of a range of ICTs for collecting data in informal and formal diagnostic, formative and summative assessments.
e. Improve your ICT and communication skills for data visualisation and data storytelling.
f. Develop a positive disposition towards the use of data in schools.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Professional Standard 1.1 Demonstrate knowledge and understanding of physical, social and intellectual development and characteristics of students and how these may affect learning.
LO2.
Professional Standard 1.2 Demonstrate knowledge and understanding of research into how students learn and the implications for teaching.
LO3.
Professional Standard 1.5 Demonstrate knowledge and understanding of strategies for differentiating teaching to meet the specific learning needs of students across the full range of abilities.
LO4.
Professional Standard 2.5 Know and understand literacy and numeracy teaching strategies and their application in teaching areas.
LO5.
Professional Standard 2.6 Implement teaching strategies for using ICT to expand curriculum learning opportunities for students.
LO6.
Professional Standard 3.4 Demonstrate knowledge of a range of resources, including ICT, that engage students in their learning.
LO7.
Professional Standard 3.5 Demonstrate a range of verbal and non-verbal communication strategies to support student engagement.
LO8.
Professional Standard 3.6 Demonstrate broad knowledge of strategies that can be used to evaluate teaching programs to improve student learning.
LO9.
Professional Standard 4.1 Identify strategies to support inclusive student participation and engagement in classroom activities.
LO10.
Professional Standard 4.5 Demonstrate an understanding of the relevant issues and the strategies available to support the safe, responsible and ethical use of ICT in learning and teaching.
LO11.
Professional Standard 5.1 Demonstrate understanding of assessment strategies, including informal and formal, diagnostic, formative and summative approaches to assess student learning.
LO12.
Professional Standard 5.4 Demonstrate the capacity to interpret student assessment data to evaluate student learning and modify teaching practice.
LO13.
Professional Standard 5.5 Demonstrate understanding of a range of strategies for reporting to students and parents/carers and the purpose of keeping accurate and reliable records of student achievement.
LO14.
Professional Standard 7.1 Understand and apply the key principles described in codes of ethics and conduct for the teaching profession.
LO15.
Professional Standard 7.2 Understand the relevant legislative, administrative and organisational policies and processes required for teachers according to school stage.
LO16.
Demonstrate the ability to effectively collaborate with others to complete group tasks.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation | Your Learning Data | 35% |
27/03/2025 5:00 pm |
| Presentation |
Students' Learning Data
|
30% |
17/04/2025 5:00 pm |
| Quiz |
eSafety Quiz
|
Pass/Fail |
1/05/2025
Completed in-class during timetabled workshop. |
| Presentation | Data storytelling | 35% |
29/05/2025 5:00 pm |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Your Learning Data
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 35%
- Due date
27/03/2025 5:00 pm
- Learning outcomes
- L01, L02, L03, L04, L06, L08
Task description
In this task, you will begin to develop your capacity to effectively select, analyse, report and utilise data for learning. You will explore your personal data to identify your learning strengths and challenges, and provide recommendations to positively impact upon your own learning.
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
Submission via Blackboard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 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.
Students' Learning Data
- Team or group-based
- Mode
- Product/ Artefact/ Multimedia
- Category
- Presentation
- Weight
- 30%
- Due date
17/04/2025 5:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L08, L09, L11, L12, L13, L16
Task description
This task extends the 'Your Learning Data' task and assists you to develop your capacity to effectively select, analyse, report and utilise data for learning.
This time you will work in a small group to analyse school students' data. You will review students' strengths and challenges, and present ideas for planning differentiated learning and teaching in your classroom.
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
Submission via Blackboard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Extensions for group work assessment may be available and will require a single request submitted with agreement from at least 50% of the members of the group, and recognition of potential impacts on the other group members. Download and complete the form below, and attach this form to your extension request: Extension of Group Assessment - Group Member Acknowledgement (PDF, 144.9 KB)
Student Access Plans for an individual student do not guarantee an extension for the assessment item. Extension Verification Letters cannot be used for group-based assessments and activities.
Dysfunctional group dynamics, poor performance by individual group members, or illness or other issues of a group member are generally not considered sufficient grounds for an extension on submission of a group assessment item. These issues should be actively managed by the group and the Course Coordinator as appropriate, during semester.
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.
eSafety Quiz
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Quiz
- Weight
- Pass/Fail
- Due date
1/05/2025
Completed in-class during timetabled workshop.
- Learning outcomes
- L10, L14, L15
Task description
A short in-class quiz about safe, ethical and responsible use of ICTs in teaching and learning, and your knowledge of students’ safety and wellbeing, and teachers’ ICTs and social media use, using codes of conduct, policies and processes.
This is an individual task.
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.
Hurdle requirements
To pass this hurdle, students must score at least 80% on this quiz. A resit option is available for this assessment task. In the event that a student is unsuccessful in achieving a passing grade, they will be permitted a second opportunity to sit the assessment.Submission guidelines
Completed in-person.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
As this task is Pass / Fail, students who submit beyond the specified due date/time without an approved extension or do not complete the quiz, will fail this task and subsequently the course.
Data storytelling
- Mode
- Product/ Artefact/ Multimedia
- Category
- Presentation
- Weight
- 35%
- Due date
29/05/2025 5:00 pm
- Learning outcomes
- L06, L07, L12, L13
Task description
In this task, you must select, analyse and communicate with data clearly and simply to make meaning for your chosen audience. Although this sounds simple, it is a complex task involving strong data literacy and visualisation skills, and importantly, high order thinking.
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
Submission via Blackboard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 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.
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: 0-24% of total marks. |
| 2 (Fail) | 25 - 44 |
Minimal evidence of achievement of course learning outcomes. Course grade description: 25-44% of total marks. |
| 3 (Marginal Fail) | 45 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: 45%-49% of total marks. A grade of 3 will be awarded to a student who achieves a final mark of 50% or higher but who does not pass the 'must pass' assessment items noted below. |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: 50%-64% of total marks. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: 65%-74% of total marks. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: 75%-84% of total marks. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: 85%-100% of total marks. |
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.)
This course contains a ‘must pass’ assessment item, specifically the eSafety Quiz assessment item. You must pass this item to pass this course overall, i.e. even if your final percentage mark is 50% or higher, the individual ‘must pass’ assessment item must have been passed. If you do not pass the eSafety Quiz, you will be allowed 1 resit attempt. If you do not pass the resit attempt, the highest grade you can receive for this course is a 3, and you will be required to apply for a supplementary practical exam, if eligible (i.e. must achieve a grade of 3).
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
****Alternative assessment in the event of disruption
In the event of a disruption during the semester that prevents the scheduled assessment from occurring as planned, the assessment will be changed to an alternative form of assessment.ﾠThe timing of the assessment may also be impacted.
What is Turnitin
Turnitin is an electronic assignment submission tool. The tool provides your Course Coordinator with:
· a record of the exact submission time of an assignment
· an originality report indicating the percentage of your work that is an exact match of existing materials within the Turnitin database.
Instructions on how to submit an assignment using Turnitin are located on the UQ Library website
Submit your Turnitin assignment - Library Guide
Note:
When submitting, to check that you have chosen the correct file on the Preview Submission page and click on the Submit to Turnitin button. ﾠ
Remember to download your digital receipt in your Assignment inbox to confirm successful submission.
If a submission cannot be successfully completed, email a copy of the assessment task to the Course Administrator. For contact details refer to Course Contributors section of the Course Profile.
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.
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 |
|---|---|---|
Multiple weeks From Week 1 To Week 3 |
Workshop |
Use of data in schools (3 weeks) In this series of workshops, we will explore the evolution of data in schools and big data in education including NAPLAN. We will come to understand what and how data is used to promote student learning and student wellbeing. We will also critique the use of data in schools including reporting, storage and ethical considerations. Learning outcomes: L01, L02, L10, L11, L12, L13 |
Multiple weeks From Week 5 To Week 7 |
Workshop |
Data for assessment, feedback & differentiation (3 weeks) This series of workshops, we will explore diagnostic and formative assessment, data-informed strategies for learning, data-informed feedback and Tomlinson’s differentiation model. Learning outcomes: L01, L02, L03, L04, L05, L06, L08, L11, L12, L13 |
Week 9 (28 Apr - 04 May) |
Workshop |
eSafety for teachers and students In this workshop, we will explore eSafety issues in schools including the safe, responsible and ethical use of ICTs. This will include topics such as policy, processes and code of conduct for ICTs and social media, how to responsd to cyberbullying, and eSafety resources for teachers including the eSafety Commissioner. This workshop includes in-class quiz (hurdle). Learning outcomes: L10, L14, L15 |
Multiple weeks From Week 10 To Week 12 |
Workshop |
Storytelling with data (3 weeks) In this series of workshops, we will explore concepts of data literacy, data visualisation including effective graphing and data storytelling. Learning outcomes: L01, L02, L04, L05, L06, L07, L08, L09 |
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: