Learning Analytics Guiding Principles

Introduction

In 2017, the University Registrar, University Privacy Officer, and the Center for Academic Innovation began working on a set of U-M Learning Analytics Guiding Principles. As we collect and use data for learning analytics, our guiding principles ensure that the creation, collection, analysis, and reporting of learning analytics data remains transparent and secure. These guiding principles also make certain we take privacy and ethical considerations into account.

For example, ensuring that data is not exploited for unexpected or unwanted uses or outcomes, and where appropriate, consent or approval is sought from students we are collecting and/or using data for.

What are learning analytics data?

We define learning analytics as, “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.”

ART's guiding principles infographic

What are AI’s learning analytics guiding principles?

Respect: We respect the dignity of students and their rights. We strive to balance the good of the students, the University of Michigan’s needs, and improving higher education through learning analytics data. We acknowledge that data students contribute to learning analytics research may benefit themselves and fellow students.

Transparency: We act with transparency, purpose, and specification when collecting and using learning analytics data. We disclose to students the educational interest in and benefits of collecting and analyzing learning analytics data. We inform students about the types of information we collect, how it is used, and how it is shared. We help students understand they are part of a community that seeks to improve approaches to teaching and learning by using learning analytics data.

Accountability: We create processes for the security, integrity, and accountability of learning analytics data. We enforce policies and procedures for appropriate data-sharing, privacy, and data stewardship. We provide ways for students to inspect and review their learning analytics data.

Empowerment: We achieve individual participation in and respect the rights and dignity of students. We have a defined process to scrutinize data accuracy. We aim to use anonymized, de-identified, or aggregated data unless there is a legitimate need to use personally identifiable information with a clear and measurable impact on improving teaching and learning. We give students the ability to view their learning analytics data and to make choices about whether certain identifiable data may be shared.

Continuous Consideration: We continually review and reexamine our learning analytics policies and models to maintain a balance between the benefits of learning analytics for the University of Michigan with the privacy and ethical considerations of students. We welcome students to participate in our learning community, and to inquire about their role in supplying learning analytics data.

What if I have more questions about learning analytics or opting-out of research studies?

For questions about Atlas, please contact atlas-support@umich.edu.