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How Intellischool anonymises data

Intellischool uses anonymised data to provide insights and comparisons to users.

In order to provide comparitive insights, as well as train predictive models and use machine learning, a massive amount of data is required. No single school can provide enough data to create statistically sound predictive models, or a suitable environment to train an artificial intelligence on recognising patterns in data.

To circumvent this issue, Intellischool uses anonymised data from many schools to facilitate these practices.

The de-identifcation process

When creating anonymised datasets, Intellischool goes through a process that:

  1. Removes any identifiable information from the top-level school record;
  2. Strips personally identifiable information from the student/staff/parent records associated with each school, including name, date of birth, e-mail, phone numbers, addresses, and any unique identifiers (such as student IDs - including CASES21 identifiers);
  3. Searches for and redacts any identifiable information from free text fields, such as comments and task names, including names of persons, school names, postal codes, addresses, phone numbers and e-mail addresses; and
  4. Deletes any temporary data created while anonymising the data set, removing any remaining paths to identifying data.

Types of de-identified data that Intellischool uses

Student and school data that are used in anonymised analysis include:

  • Academic results
  • Aboriginal / Torres Strait Islander status
  • Attendance
  • Gender
  • Geographic location (to a Local Government Area / region within a state)
  • Language background at home
  • Socioeconomic status (SES)
  • Standardised test results
  • Wellbeing indicators
  • Year levels

Privacy and data security are critical at Intellischool. For more information on how we anonymise datasets, or a technical description of the process, please reach out to your Intellischool representative.

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