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Learning Data & Analytics Key Principles

IMS Global Learning Data & Analytics Key Principles

The IMS Global Learning Consortium Learning Data & Analytics Innovation Leadership Network has released this document articulating eight principles that all higher education institutions should consider when implementing technology for the collection and use of learning data.* As higher education institutions continue to leverage learning data generated by multiple systems, whether on-premises or cloud based, these key principles are meant to guide decisions regarding the collection, access, use, and governance of learning data.

  1. Ownership: Institutions should have a clear policy that articulates the varying degrees of ownership or co-ownership of the data. It is suggested that the policy defines the rights institutional community members have to access, port, and control the disposition and deletion of data they have generated, stored by the institution, its service providers, and their affiliated partners.
  2. Stewardship: As stewards of learning data, institutions bear the responsibility to ensure access, protect the data and the interests of its owners. These should transcend but encompass, existing protocols, such as Institutional Research Board (IRB).
  3. Governance: Institutions need a transparent learning data governance plan that articulates policies for retention, use, control and deletion of learning data on any system. The policies need to express faculty and students ownership, their rights of data access and retrieval, and ability and process for opting-out of the collection and use of reasonable non-aggregated data.
  4. Access: Learning data, whether generated locally or in a vendor-supplied system, is strategic to an institution’s business and mission and must be available to the institution.
  5. Interoperability: The collection, use, and access to learning data require institutional and supplier collaboration, which is dependent upon interoperability standards, protocols, data formats, and content to achieve institutional goals.
  6. Efficacy: Learning data collection, use, and computational transformation are aimed at student and instructor learning engagement, academic success, and instructional concerns through prescriptive, descriptive, or predictive methodologies.
  7. Security & Privacy: Individuals’ security and privacy relating to collecting, using, and algorithmically transforming learning data is fundamental and must not be treated as optional. It must also be balanced with the effective use of the data.
  8. Transparency: Individuals have the right to understand the specific reasons, methods, and purposes for which their learning data is collected, used, and transformed. This includes any learning data being shared with third-­party service providers and other institutional affiliates or partners. Individuals also have the right to know how their data is transformed and/or used through processes such as summative or algorithmic modifications, particular outputs, and visualizations.

*Learning data refers to data generated by students, faculty, and/or staff that relates to and documents the teaching and learning experience and academic achievement. It can be used alone or combined with the student record and other data points to support student success research.


We would like to thank the following contributors, IMS Global Learning Data & Analytics Innovation Leadership Network participants, who lent their personal expertise to develop this public draft. Their intent was to create a resource that would inform and help facilitate conversations on student learning data and analytics. The hope is that its thematic, high-level approach will support institutional leaders and other stakeholders as they advance their practices around the use of learning data.

Individual contributors:

John Fritz, University of Maryland, Baltimore County

Adam Recktenwald, University of Kentucky

Oliver Heyer, University of California at Berkeley

Marianne Schroeder, University of British Columbia

Virginia Lacefield, University of Kentucky

Jenn Stringer, University of California at Berkeley

Phillip Long, University of Texas at Austin 

 

 

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