EUN673 Big Data and Learning Analytics
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: | EUN673 |
|---|---|
| Equivalent(s): | EUZ673 |
| Assumed Knowledge: | Assumed Knowledge: Completion of EUN672 or enrolment in a concurrent teaching period is assumed knowledge. |
| Credit points: | 6 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $277 |
| Domestic tuition unit fee | $1,488 |
| International unit fee | $2,010 |
Unit Outline: Flexible Period - 06A 2024, Kelvin Grove, Internal (Start Date: 07 May 2024)
| Unit code: | EUN673 |
|---|---|
| Credit points: | 6 |
| Equivalent: | EUZ673 |
| Assumed Knowledge: | Completion of EUN672 or enrolment in a concurrent teaching period is assumed knowledge. |
| Coordinator: | Kate Thompson | kate.j.thompson@qut.edu.au |
Overview
This unit introduces the fundamental process of data science and provides the necessary computational and statistical foundations for further experimentation in data science. You will learn how and when to use key methods for educational data mining and learning analytics. You will learn how to apply these methods and when to apply them, as well as their strengths and weaknesses for different applications.
This unit is designed to be studied as part of the research pathway in the Masters of Education course. This unit helps prepare students for both practitioner research and further postgraduate study. This unit may also be studied as a single unit option.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of research principles (CLO 1.2).
- Demonstrate cognitive, technical and creative skills to investigate, analyse and synthesise complex information, problems, concepts and theories and to apply established theories to different bodies of knowledge or practice (CLO 2.2).
- Demonstrate communication and technical research skills to justify and interpret theoretical propositions, methodologies, conclusions and professional decisions to specialist and non-specialist audiences (CLO 2.3).
- Demonstrate ability to apply knowledge and skills to plan and execute a significant piece of research-based scholarship in the field of education (CLO 3.2) .
Content
The unit will cover the following topics:
- Definitions and characteristics of big data, deep learning, learning analytics
- The basics of how big data technology works
- Different types of algorithms for extracting patterns
- Data analysis
- Issues of reliability and validity in big data modelling
- Ethical issues including privacy and ownership.
Learning Approaches
In this unit you will learn through engaging in a range of pedagogic activities such as short lectures outlining key concepts, discussions, short group tasks and scenario based activities.
Feedback on Learning and Assessment
You will gain feedback in this unit by participating in workshop activities (online or face-to-face) with academics and peers that contribute to your understanding and skill development. This will include learning tasks to provide you with early feedback for your successful completion of your assessment tasks.
Assessment
Overview
There is one assessment in this unit that asks students to respond to a set of scenarios associated with big data, learning analytics and machine learning to show their understanding.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Big data and learning analytics: scenarios
Students will be given a random selection of scenarios relating to the design, trialling, analysis and use of big data, and learning analytics that will require them to explain how they would respond. This will relate to issues of computation, reliability, validity and decision making that professionals are likely to encounter in their working lives.
This is an assignment for the purposes of an extension.
Relates to learning outcomes
CLOs - 1.2, 2.2, 2.3, 3.2
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
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.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
There are no prescribed texts for this unit. However, here is a list of recommended reading available online through the QUT library.
Resource Materials
Recommended text(s)
Big data : concepts, methodologies, tools, and applications . (2016). Hershey, Pennsylvania: Information Science Reference.
Risk Assessment Statement
There are no risks associated with enrolment in this unit beyond those of day to day living.
Unit Outline: Flexible Period - 06A 2024, Online (Start Date: 07 May 2024)
| Unit code: | EUN673 |
|---|---|
| Credit points: | 6 |
| Equivalent: | EUZ673 |
| Assumed Knowledge: | Completion of EUN672 or enrolment in a concurrent teaching period is assumed knowledge. |
Overview
This unit introduces the fundamental process of data science and provides the necessary computational and statistical foundations for further experimentation in data science. You will learn how and when to use key methods for educational data mining and learning analytics. You will learn how to apply these methods and when to apply them, as well as their strengths and weaknesses for different applications.
This unit is designed to be studied as part of the research pathway in the Masters of Education course. This unit helps prepare students for both practitioner research and further postgraduate study. This unit may also be studied as a single unit option.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of research principles (CLO 1.2).
- Demonstrate cognitive, technical and creative skills to investigate, analyse and synthesise complex information, problems, concepts and theories and to apply established theories to different bodies of knowledge or practice (CLO 2.2).
- Demonstrate communication and technical research skills to justify and interpret theoretical propositions, methodologies, conclusions and professional decisions to specialist and non-specialist audiences (CLO 2.3).
- Demonstrate ability to apply knowledge and skills to plan and execute a significant piece of research-based scholarship in the field of education (CLO 3.2) .
Content
The unit will cover the following topics:
- Definitions and characteristics of big data, deep learning, learning analytics
- The basics of how big data technology works
- Different types of algorithms for extracting patterns
- Data analysis
- Issues of reliability and validity in big data modelling
- Ethical issues including privacy and ownership.
Learning Approaches
In this unit you will learn through engaging in a range of pedagogic activities such as short lectures outlining key concepts, discussions, short group tasks and scenario based activities.
Feedback on Learning and Assessment
You will gain feedback in this unit by participating in workshop activities (online or face-to-face) with academics and peers that contribute to your understanding and skill development. This will include learning tasks to provide you with early feedback for your successful completion of your assessment tasks.
Assessment
Overview
There is one assessment in this unit that asks students to respond to a set of scenarios associated with big data, learning analytics and machine learning to show their understanding.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Big data and learning analytics: scenarios
Students will be given a random selection of scenarios relating to the design, trialling, analysis and use of big data, and learning analytics that will require them to explain how they would respond. This will relate to issues of computation, reliability, validity and decision making that professionals are likely to encounter in their working lives.
This is an assignment for the purposes of an extension.
Relates to learning outcomes
CLOs - 1.2, 2.2, 2.3, 3.2
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
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.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
There are no prescribed texts for this unit. However, here is a list of recommended reading available online through the QUT library.
Resource Materials
Recommended text(s)
Big data : concepts, methodologies, tools, and applications . (2016). Hershey, Pennsylvania: Information Science Reference.
Risk Assessment Statement
There are no risks associated with enrolment in this unit beyond those of day to day living.