EUN696 Researching in Education with Big Data
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | EUN696 |
|---|---|
| Prerequisite(s): | EUN691. EUN691 can be enrolled in the same teaching period as EUN696. |
| Equivalent(s): | EUZ696 |
| Assumed Knowledge: | It is assumed that students understand educational practices in education settings and have practitioner experiences within a related education field or support profession. |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $592 |
| Domestic tuition unit fee | $3,468 |
| International unit fee | $4,740 |
Unit Outline: Semester 1 2026, Kelvin Grove, Internal
| Unit code: | EUN696 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | EUN691. EUN691 can be enrolled in the same teaching period as EUN696. |
| Equivalent: | EUZ696 |
| Assumed Knowledge: | It is assumed that students understand educational practices in education settings and have practitioner experiences within a related education field or support profession. |
| Coordinator: | Dann Mallet | dg.mallet@qut.edu.au |
Overview
This unit engages with data science for education research. You will learn about the necessary computational and statistical foundations for further experimentation with data science in education research. You will learn how to apply data science approaches to education research including identifying data, analytic approaches and the use of visualisations, as well as their strengths and weaknesses for different applications. The knowledge and skills learnt in this unit equip you with the knowledge to make better educational decisions informed by data.
Learning Outcomes
On successful completion of this unit you will be able to:
- Design and justify responses to authentic problems of educational practice through the application of research skills related to analysis of big data.
- Discuss and integrate advanced knowledge related to big data, professional educational practice and research.
- Critically analyse issues of computation, error, reliability and validity in relation to big data in education scenarios.
- Reflect on big data and its analysis in education research and on peer discussions to improve professional knowledge and practice.
- Apply methods of data science to interpret big data and analytics related to learning, in relation to educational contexts and research problems.
Content
The unit will cover the following:
- Definitions and characteristics of big data, deep learning, learning analytics
- The basics of how big data technology works
- Identification of data
- Data analysis
- Issues of reliability and validity of LSAs and in big data modelling
- Ethical issues including privacy and ownership, and generative artificial intelligence.
Learning Approaches
In this unit you will learn through engaging in the following:
- pre-recorded online videos
- tutorials (online and on-campus)
- online learning activities
- readings
- authentic assessment tasks in which you will apply scholarly knowledge to real world scenarios and in your own context.
This unit employs interactive and discussion-based learning and critical reflection on the intersection of theory and experience.
Feedback on Learning and Assessment
Feedback in this unit is provided to you in the following ways:
- a range of formative exercises discussed and undertaken throughout the unit
- feedback from peers as you investigate and discuss issues raised throughout the unit
- comments about summative assessment work included with your grade.
Assessment
Overview
There are two assessment tasks in this unit:
1. Reflection on big data and analysis in education research - this task draws on principles, theories and scholarly literature about analysis of big data in education to inform a peer discussion meeting about a given scenario and develop a response plan. You will reflect on the peer discussion meeting and future directions.
2. Big Data and Learning Analytics - this task involves undertaking a project analysing education data in given scenarios.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Reflection
You will be given a selection of scenarios relating to big data and analysis in education. You will analyse a scenario using scholarly literature to develop an informed opinion about the scenario. You will record yourself meeting with peers to discuss the scenario, ethical research, the supporting literature and your opinions to create a negotiated plan for how your group would respond.
You will record yourself explaining your initial analysis and reflecting on the peer discussion to identify and analyse key ideas, explain the group's response and discuss implications for future directions.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Big Data and Learning Analytics
You will analyse cases of educational research involving the use of big data and/or learning analytics. You will report on issues of computation, reliability, validity and ethical professional decision making.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
There are no resources that are required to be purchased for this unit. Resources will be available in the unit's Canvas site.
Risk Assessment Statement
There are no out-of-the-ordinary risks associated with the general conduct of this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.EU74 Master of Education
- Discuss, evaluate, and integrate advanced discipline-specific knowledge related to professional educational practice and research.
Relates to: ULO2, Reflection, Big Data and Learning Analytics - Reflect on and reflexively analyse own practice, integrating theoretical frameworks to improve professional knowledge and practice.
Relates to: ULO4, Reflection - Critically analyse and evaluate complex activities, contexts and phenomena related to professional practice and scholarship in the discipline of education.
Relates to: ULO3, Reflection, Big Data and Learning Analytics - Design and justify innovative solutions to authentic problems of educational practice, drawing on discipline-specific knowledge and skills to lead and influence positive change.
Relates to: ULO1, Reflection, Big Data and Learning Analytics - Apply theories and research methods to ethically explore educational issues/phenomena and practices.
Relates to: ULO5, Big Data and Learning Analytics
Unit Outline: Semester 1 2026, Online
| Unit code: | EUN696 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | EUN691. EUN691 can be enrolled in the same teaching period as EUN696. |
| Equivalent: | EUZ696 |
| Assumed Knowledge: | It is assumed that students understand educational practices in education settings and have practitioner experiences within a related education field or support profession. |
Overview
This unit engages with data science for education research. You will learn about the necessary computational and statistical foundations for further experimentation with data science in education research. You will learn how to apply data science approaches to education research including identifying data, analytic approaches and the use of visualisations, as well as their strengths and weaknesses for different applications. The knowledge and skills learnt in this unit equip you with the knowledge to make better educational decisions informed by data.
Learning Outcomes
On successful completion of this unit you will be able to:
- Design and justify responses to authentic problems of educational practice through the application of research skills related to analysis of big data.
- Discuss and integrate advanced knowledge related to big data, professional educational practice and research.
- Critically analyse issues of computation, error, reliability and validity in relation to big data in education scenarios.
- Reflect on big data and its analysis in education research and on peer discussions to improve professional knowledge and practice.
- Apply methods of data science to interpret big data and analytics related to learning, in relation to educational contexts and research problems.
Content
The unit will cover the following:
- Definitions and characteristics of big data, deep learning, learning analytics
- The basics of how big data technology works
- Identification of data
- Data analysis
- Issues of reliability and validity of LSAs and in big data modelling
- Ethical issues including privacy and ownership, and generative artificial intelligence.
Learning Approaches
In this unit you will learn through engaging in the following:
- pre-recorded online videos
- tutorials (online and on-campus)
- online learning activities
- readings
- authentic assessment tasks in which you will apply scholarly knowledge to real world scenarios and in your own context.
This unit employs interactive and discussion-based learning and critical reflection on the intersection of theory and experience.
Feedback on Learning and Assessment
Feedback in this unit is provided to you in the following ways:
- a range of formative exercises discussed and undertaken throughout the unit
- feedback from peers as you investigate and discuss issues raised throughout the unit
- comments about summative assessment work included with your grade.
Assessment
Overview
There are two assessment tasks in this unit:
1. Reflection on big data and analysis in education research - this task draws on principles, theories and scholarly literature about analysis of big data in education to inform a peer discussion meeting about a given scenario and develop a response plan. You will reflect on the peer discussion meeting and future directions.
2. Big Data and Learning Analytics - this task involves undertaking a project analysing education data in given scenarios.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Reflection
You will be given a selection of scenarios relating to big data and analysis in education. You will analyse a scenario using scholarly literature to develop an informed opinion about the scenario. You will record yourself meeting with peers to discuss the scenario, ethical research, the supporting literature and your opinions to create a negotiated plan for how your group would respond.
You will record yourself explaining your initial analysis and reflecting on the peer discussion to identify and analyse key ideas, explain the group's response and discuss implications for future directions.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Big Data and Learning Analytics
You will analyse cases of educational research involving the use of big data and/or learning analytics. You will report on issues of computation, reliability, validity and ethical professional decision making.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
There are no resources that are required to be purchased for this unit. Resources will be available in the unit's Canvas site.
Risk Assessment Statement
There are no out-of-the-ordinary risks associated with the general conduct of this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.EU74 Master of Education
- Discuss, evaluate, and integrate advanced discipline-specific knowledge related to professional educational practice and research.
Relates to: ULO2, Reflection, Big Data and Learning Analytics - Reflect on and reflexively analyse own practice, integrating theoretical frameworks to improve professional knowledge and practice.
Relates to: ULO4, Reflection - Critically analyse and evaluate complex activities, contexts and phenomena related to professional practice and scholarship in the discipline of education.
Relates to: ULO3, Reflection, Big Data and Learning Analytics - Design and justify innovative solutions to authentic problems of educational practice, drawing on discipline-specific knowledge and skills to lead and influence positive change.
Relates to: ULO1, Reflection, Big Data and Learning Analytics - Apply theories and research methods to ethically explore educational issues/phenomena and practices.
Relates to: ULO5, Big Data and Learning Analytics