Respect for students and instructors will inform all actions and processes related to the use of learning analytics, as it is anticipated that their engagement and agency will be critical to the effectiveness and sustainability of work in this area. The following principles underpin all University of Toronto activity in this domain: 

 Beneficence and Non-Maleficence: Learning analytics will be used to enhance the learning experience and opportunities of our students. Learning analytics employ statistical models to predict behaviours and outcomes and is intended to inform or assist decision-making. It is not intended to define or limit individual students. Therefore, analytics that may have a significant impact on an individual student’s encounter with the university must not be used without human intervention or learner agency.  

Equity: Our student population is highly diverse. Learning analytics must support the learning needs of our entire student body and not just those at risk academically. We will use learning analytics to enhance equity-driven outcomes and reduce systematic barriers to student learning and success.  

Privacy, Confidentiality, and Security: Any use or processing of student data for learning analytics will meet or exceed the requirements of the Freedom of Information and Protection of Privacy Act (FIPPA) and its regulations, and university policies and guidance for the secure management of the data. Uses of student data that fall outside Learning Analytics Initiative Report – April 2021 6 the purview of FIPPA may require individual consent. Any user of the data will be subject to a written confidentiality agreement.  

Transparency: The university will be transparent regarding its conduct of student analytics. Student notices of data collection and use will include a clear description of learning analytics uses. These notices will be readily accessible at critical points where students are providing that information.  

Community Engagement: Involvement of those who are the subjects of learning analytics – chiefly students and course instructors – has the potential to enhance the quality of the learning analytics. This may be through their complementary insights (gained through their lived experience) into what may affect the learning process and outcomes, and their perspectives on the policy implications of the findings. Therefore, students’ and instructors’ involvement will be sought as appropriate at key stages of learning analytics initiatives. These guiding principles will inform shared exploration of the use of learning analytic data at the University of Toronto as we establish ethical practices and promote insight into effective approaches across institutional stakeholders.