Auckland University of Technology (AUT) has always analysed data collected routinely from students in order to improve how it operates. In recent years, the increasing amount of information generated by student interactions with online systems has seen the development of new analytics approaches, including learning analytics.
Learning analytics is the measurement, collection, analysis and reporting of data about the progress of learners and the contexts in which learning takes place. It can improve the ways papers and programmes are developed, taught and assessed, and identify points on the student journey where individuals or groups of students may need additional support.
- Logging into Blackboard (the Student Learning Management System)
- Using the Library
- Connecting to wifi
- Making a room or equipment booking
AUT brings together data from different data source systems in its Data Warehouse. AUT collects, uses, stores, and discloses personal information relating to students in accordance with the provisions of the Privacy Act.
Full details of privacy principles
All data selected for use in learning analytics is subject to data quality checks. These determine whether, for instance, the data is valid and the records are up to date.
Fields that could identify individual students such as email addresses or names are stored externally to the AUT Data Warehouse in the Data Vault. The Data Vault is a fully encrypted database with a number of access controls and security features and fulfils Principle 5 of the Privacy Act, which is designed to protect personal information from unauthorised use or disclosure.
Unfortunately, it is too administratively burdensome to have your data excluded. The University uses all student data to analyse patterns of behaviour. The analysis works on the dataset as a whole and does not identify an individual student by name or student ID number.
It is important to maintain a full dataset as any significant loss in student data could undermine the data validity of the predictive model.
Currently, AUT is only piloting the use of a learning analytics model based on student enrolment and study data. Student consent will be explicitly sought once the University begins to use student engagement data to provide targeted individualised learning and support interventions. Here, students are provided with the option to opt-out without attracting any negative consequences.
The potential adverse consequences of doing so will be clearly explained to students and they will retain the right to opt-in subsequently. Student consent for learning analytics interventions will be revised as the pilot project progresses.