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 | $246 |
| Domestic tuition unit fee | $1,296 |
| International unit fee | $1,860 |
Unit Outline: 5 Week Teaching Period - 4 2021, Kelvin Grove, Internal
| Unit code: | EUN673 |
|---|---|
| Credit points: | 6 |
| Equivalent: | EUZ673 |
| Assumed Knowledge: | Completion of EUN672 or enrolment in a concurrent teaching period is assumed knowledge. |
| Coordinator: | Charisse Farr | a.farr@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, machine learning, deep learning, learning analytics
- The basics of how big data technology works
- Different types of algorthms for extracting patterns
- Data analysis
- Issues of reliability and validity in big data moedliing
- 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. This unit will be offered in an intensive mode over two days. There will be some reading that is required to be done prior to commencing the intensive.
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, learning analytics and machine learning: scenarios
Students will be given a random selection of scenarios relating to the design, trialling, analysis and use of big data, learning analytics and machine learning 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.
Relates to learning outcomes
CLOs - 1.2, 2.2, 2.3, 3.2
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 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.
Kalyvas, J., & Overly, M. (2014). Big Data (1st edition). Auerbach Publications.
Risk Assessment Statement
There are no risks associated with enrolment in this unit beyond those of day to day living.
Unit Outline: 5 Week Teaching Period - 4 2021, Online
| 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, machine learning, deep learning, learning analytics
- The basics of how big data technology works
- Different types of algorthms for extracting patterns
- Data analysis
- Issues of reliability and validity in big data moedliing
- 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. This unit will be offered in an intensive mode over two days. There will be some reading that is required to be done prior to commencing the intensive.
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, learning analytics and machine learning: scenarios
Students will be given a random selection of scenarios relating to the design, trialling, analysis and use of big data, learning analytics and machine learning 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.
Relates to learning outcomes
CLOs - 1.2, 2.2, 2.3, 3.2
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 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.
Kalyvas, J., & Overly, M. (2014). Big Data (1st edition). Auerbach Publications.
Risk Assessment Statement
There are no risks associated with enrolment in this unit beyond those of day to day living.