ITO5007 Introduction to data science for learning analytics
Faculty of Information Technology
ITO5007 Introduction to data science for learning analytics is a level 5, 6-credit-point, postgraduate unit from the Faculty of Information Technology. It isn't offered in 2027. It has no prerequisites and unlocks 2 units.
- Credit points
- 6
- Offered in 2027
- Not offered
- Assessment
- No exam
- 2 tasks
- Workload
- 12 hours
- per semester
Reviews
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Requisites
Before ITO5007
No prerequisites or corequisites.
After ITO5007
2 units list ITO5007 as a prerequisite or corequisite.
Overview
This unit will focus on the key data science approaches as commonly used in learning analytics. Students will work on relevant datasets and develop skills in identifying suitable data indicators for specific contexts. This unit provides foundations for feature engineering to extract relevant indicators from raw data about learning. Relevant data analytics tools and techniques will also be introduced. This will provide students with experience in working on common tasks in learning analytics using data science approaches that are situated in established frameworks used for designing and evaluating learning environments.
Offerings in 2027
The 2027 handbook lists no offerings for ITO5007.
Assessment
- Data wrangling and analysisAssignment40%
- CritiqueAssignment60%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
Analyse the role of data in learning analytics;
- 2
Identify and apply basic tools for performing exploratory data analysis, visualisation, and predictive modelling in learning analytics;
- 3
Discuss and evaluate the legal and ethical issues due to the use of data science in learning analytics;
- 4
Develop and critically assess a detailed plan for data science implementation in learning analytics.
Workload and teaching
- Tutorials24 hours
- Teaching approachActive learning
- Teaching approachOnline learning
Minimum total expected workload equals 12 hours per week
independent learning, discussions, collaborative learning, and feedback & reflections.
Contacts
- Chief Examiners
- Dr Guanliang Chen
Common questions
What are the prerequisites for ITO5007?
ITO5007 has no prerequisites.
What can I take after ITO5007?
ITO5007 is a prerequisite or corequisite for 2 units, including ITO5009 and ITO5010.
When is ITO5007 offered?
ITO5007 has no offerings listed in the 2027 handbook.
How much work is ITO5007?
The handbook expects about 12 hours of study across the semester. No students have rated its difficulty yet.
Does ITO5007 have an exam?
No. ITO5007 has 2 assessment tasks and no exam.