Source: Tertiary Education Commission
Last updated 4 February 2021
Last updated 4 February 2021
Guidance and tools for tertiary education organisations (TEOs) to use when dealing with big data to inform learner success initiatives.
Guidance and tools for tertiary education organisations (TEOs) to use when dealing with big data to inform learner success initiatives.
We need a tertiary system which enables every person to gain the skills, knowledge and confidence to create a fulfilling life. However, our current tertiary education system does not always deliver an education experience appropriate to the needs of a large group of learners. Māori, Pacific people and disabled learners are over-represented in this group.
Developing a tertiary system that works well for all learners is complex, as it requires coordination across a wide range of areas. While we have a specific focus on Māori, Pacific and disabled learner achievement, the Ōritetanga learner success approach will identify and support all learners at risk because it is based on a range of nuanced indicators to identify specific learner needs.
TEOs need to be far better at knowing their learners and responding to their needs to ensure they succeed. This means understanding learner journeys, transition and risk points to identify what intervention strategies are necessary and when. Better collection, analysis and use of data is key to TEOs knowing their learners.
The use of data is not without risk – it needs to be done ethically
Using large amounts of student data as the basis for predicting learner success is not without risks. Predictive analytic models typically use millions of data points relating to the characteristics and behaviours of thousands of learners. Collating and using this data raises a number of significant issues and potential risks, including: privacy, informed consent, de-identification of data, and the appropriate collection and management of data. In addition, there are real concerns about how the outputs of any predictive analytic models might be misused, for example, profiling to exclude “high-risk” learners’.
The pages below provide a framework and a set of tools to help TEOs understand the issues of using data and mitigate risks.
Learner analytics
Advice on using personal data including should you use learner analytics, and frameworks and principles you can use to guide your approach. Read more
Key components
The key components of a framework to ethically use student data. This includes transparency, good governance, safe and secure data use, quality systems to ensure privacy, community perspectives. Read more
Tools
TEOs can download and adapt a range of templates for their student data analytics framework. Read more
