Should you use learner analytics?

TEOs should ask themselves this set of questions before they start to use learner analytics. Then use this checklist to help you confirm what documents and process you already have in place.

Before you start using learner analytics, consider the following questions.

Why do you want to apply learner analytics? What value will it add to your organisation?
What are the objectives and boundaries of learner analytics? What data will you collect, for what purpose and for how long?
What data do you already collect? Will you need additional data to apply learner analytics?
Have you involved your stakeholders? Do you know the views of students, staff, the community, Māori, Pasifika, international students, disabled persons?
What is your consent process? Can you clearly explain to students what information is needed and why? Do students have the ability to opt out without consequences?
Can data be aggregated or anonymised and still achieve the desired results? Does all the data need to be identifiable?
Is your data stored securely? Do you know who has access to it and can you control and monitor access?
Do external providers meet data security standards? Do your contracts clearly specify the responsibilities for data security?

Process checklist

Given the risks of poor data management, can your TEO implement a learner analytics programme?

Use the Learner analytics process checklist below to confirm what documents and processes you have in place currently.

If you are lacking anything, read on for guidance on how to establish an ethical data practice, data management principles, and for helpful links and templates to adapt for your TEO.

The size of your organisation, the number of students enrolled, the type of data you want to use, and the interventions you intend to use will all factor into your decision to implement learner analytics.

The checklist includes all documents and processes needed for a large TEO that intends to target interventions to individuals. A small TEO may want to limit interventions and have fewer organisational policies overall. The checklist offers all the documents you need for best-practice privacy generally, and especially before you implement learner analytics.

You can ask for support from the Tertiary Education Commission (TEC) to guide you through this process.

Learner analytics process checklist

You should have these processes in place before you implement learner analytics in order to achieve optimal privacy and ethical practice.

Name	Description
Data governance board	A board should monitor and approve changes to data use, check ethical principles are followed and be accountable for maintaining data-related policies.
Privacy notice (DOCX 32 KB) (Word, 32 Kb)	Commonly located on websites, this document publicly sets out your TEO’s personal information management practices.
Data analytics ethics policy (DOCX 31 KB) (Word, 31 Kb)	This policy outlines ethical management of student data.
Data analytics ethics procedure (DOCX 38 KB) (Word, 38 Kb)	To be read alongside the above policy, this sets out the responsibilities of ethical data analytics.
Code of Conduct	This addresses the need for staff to adhere to the privacy and ethical data use policy.
Privacy training	All staff should be trained in privacy generally; however, those working directly with data must receive specific training in ethical data use.
Frequently asked questions (FAQs)	These clearly explain learner analytics to students who wish to know more about the process.
Data request access process	Your TEO needs a process for managing student information requests and responding in time.
Complaints process	Students need an established channel to complain and give feedback on use of their data.
Consent explainer (DOCX 27 KB) (Word, 27 Kb)	Students must have full knowledge of learner analytics before they consent to participate.
Records retention policy	This provides a clear structure for managing, storing and disposing of the information held.
Privacy impact assessments (PIA)	Any change to systems or processes should be subject to a PIA to mitigate privacy risks.
Risk and assurance model	Strategic data decisions should be made in alignment with your risk and assurance model.
Secure systems	Data must be stored in a system with controlled access and IT security protections including monitoring and auditing to show exceptional access.