Learning Data & Analytics Key Principles

IMS Global Learning Data & Analytics Key Principles

The IMS Global Learning Consortium Learning Data & Analytics Community of Practice has released this public draft 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.

The purpose of this public draft is for institutional leaders, administrators, and other stakeholders, who are participating in ongoing dialogues specific to the gathering and usage of data, to provide your perspective, feedback and comments. Your responses will be used to further refine this document and additional resources being developed by the IMS Global Communities of Practice.

  1. Ownership: Faculty, staff, and students generate and own their learning data. As governed by institutional policies, individuals, being owners of the data they generate, have the right to access, port, and control the disposition of their data stored by the institution, its service providers, and their affiliated partners.
  2. Stewardship: As stewards of learning data, institutions should have a data governance plan and governance policies that protect the data and the interests of its owners. These should transcend, but encompass, existing protocols, such as IRB.
  3. Governance: Learning data use and retention will be governed by institutional policies, and faculty and students retain the right of data access and retrieval.
  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 requires institutional and supplier collaboration, which is dependent upon interoperability standards, protocols, data formats, and content to achieve institutions goals.
  6. Efficacy: Learning data collection, use, and computational transformation is aimed at student and instructor 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 thru 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 Community of Practice 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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