Learner analytics uses personal information

Learner analytics uses personal information

Last updated 4 February 2021
Last updated 4 February 2021

Learner analytics uses data to better understand the pastoral and education needs of your students.

Learner analytics activities are designed to help tailor support services and pastoral care to students and improve the quality of teaching.

Learner analytics involves using student data to indicate where an early intervention may improve the student’s experience, or to target specific support to individuals.

Intervention may include referral to student services, academic advice, or sending messages to students to encourage certain actions.

Learner analytics may inform changes to teaching and classroom learning design and may support staff to provide effective information and pedagogical input.

Learner analytics uses personal information

Personal information is collected from students before and during enrolment for verification and reporting purposes. This personal information can include previous education records, and detailed personal information such as date of birth, next of kin, gender, ethnicity, addresses, passport, and birth or marriage certificates.

With more complex learner analytics, information relating to the student’s class attendance and academic performance can be collected during the whole cycle of student life. 

The use of such personal data needs to balance the student’s rights and responsibilities against your rights and responsibilities as the TEO.

There are several risks involved in using personal data to determine whether interventions are needed. You need to comply with privacy law, and you don’t want to be perceived as overly intrusive, or to negatively impact on your TEO’s reputation.

Learner analytics risks

Risks of poor management of personal data include:

  • students may have insufficient knowledge of where their data is and how it’s being used, which could affect their perception of the learner analytics programme
  • inaccurate data can skew results and will not deliver the benefits intended
  • relying on analytical tools without human oversight could result in less tailored results that don’t recognise nuance
  • bias (perceived or actual) can negatively impact the learner analytics programme’s results
  • failing to account for Māori data sovereignty could disempower Māori students, their whānau, hapū or iwi
  • analysing data outside of its cultural context may not help Pasifika students to succeed
  • careless security practices could result in accidental disclosures and susceptibility to theft from outside parties.

These risks would make your TEO non-compliant with privacy law. They could reduce trust in your organisation and lead to reputational damage, disaffected students and declining enrolments.