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Data Ethics

Goals and Best Practices for Learning Analytics at the University of Washington

Learning analytics

Learning analytics refers to the collection, analysis, and use of student data to improve learning, retention, and academic planning. For example, learning analytics may employ statistical modeling or machine learning techniques to recommend degree pathways or identify students who are at risk of leaving the University of Washington (UW).

As UW begins employing learning analytics to improve student success, it is important to establish clear goals and best practices that will help guide the appropriate and ethical use of learning analytics. These goals and best practices serve as a foundation for UW in the rapidly emerging field of learning analytics.

The use of learning analytic data is for UW personnel to support teaching, learning, and the student experience. The goals and best practices in this document are intended to help guide UW faculty, administrators, and staff in the appropriate use of learning analytics.

Goals for the use of learning analytics

Inspired by the UW’s fundamental vision and values, the UW currently has three goals for the use of learning analytics:

  • To help students achieve their learning goals,
  • To improve persistence and retention, and
  • To reduce the time it takes to finish a degree

These goals may be achieved in a variety of ways, including, but not limited to: (1) using learning analytics to support academic departments and units as they strive to customize teaching and learning experiences; (2) matching UW-provided support and services with the students who need them the most; (3) aligning advising practices and student interest or needs; or (4) shaping academic planning tools for students.

UW’s goals for current and planned applications of learning analytics will be communicated broadly and in a timely manner.

Best Practices for the use of learning analytics

Responsibility

The foundational best practice for the use of learning analytics at UW is responsibility.

  • Following its core values, UW has a responsibility to improve student persistence, help students achieve their learning goals, and support their journey toward a degree. This can be accomplished, in part, by extracting meaning from student data via learning analytics.
  • As described in the following sections, UW also has the responsibility to ensure that: learning analytic approaches and practices are valid and effective, UW privacy principles and security policies are upheld, and a governance structure is in place to ensure that such activities do not compromise UW’s values and policies.

Validity and efficacy

Assessment and refinement of modeling, analysis and practices will be an ongoing process to ensure valid results and useful and effective service delivery.

  • The accuracy of the models will be closely scrutinized on a periodic basis to ensure they are meeting an acceptable level of accuracy.
  • Algorithms and other analytical processes performed on student data will be available for review with collaborating institutions so long as review does not expose student data.
  • Modelling and analysis of student data will be free from undesired biases, and practices for mitigating bias in the application of learning analytics will be encouraged.
  • As much as possible and practical, errors in the data will be corrected in the systems from which the data is sourced rather than in the systems in which the data is consumed, analyzed, or displayed.

Governance

Learning analytics may be required or essential to the UW’s mission. However, the benefits and risks associated with learning analytics require careful review to ensure that such activities do not compromise the UW’s values and policies.

  • The Faculty Council for Teaching & Learning will exercise oversight over the goals for the use of learning analytics.
  • The Vice Provost for Academic and Student Affairs or designee, will exercise oversight for the best practices for learning analytics.
  • The UW Privacy Steering Committee will exercise oversight for the privacy implications associated with the purpose and use of personal data.
  • As needed the above governance structure will be re-evaluated and modified in order to stay consistent with the evolving data governance structure at UW.

Privacy

The processing (such as collection, storage, access, use, and analysis) of student data for learning analytics must be consistent with the commitments that the UW made and communicated to students in the privacy notice for prospective, registered, and former students. This includes statements about the categories of data that are collected, the purpose of the data, a description of how the data are used by the UW, and an explanation of the individuals’ rights related to their own personal data.

Organizations that collect or use learning analytic data are required to uphold the UW Values and follow UW privacy principles and related policies, standards, or guidelines that are in place for upholding the UW’s humanitarian, ethical, and legal obligations when it comes to individuals’ privacy. UW Values and privacy principles apply to all personal data and are established by the UW Privacy Office and UW Privacy Steering Committee. They are emphasized and reiterated here:

Values

We uphold the UW’s values to guide decisions about how the collection, use, storage, and retention of data impacts the UW and individuals. We demonstrate our values by honoring our commitments to individuals.

  • Respect. We value individuals’ privacy when we collect and use personal data. We understand that personal data represents and has an impact on individuals and communities.
  • Integrity. We value a system of maintaining and using quality personal data in a way that supports public trust, values the individual, and promotes a shared responsibility for data usage.
  • Diversity. We value various dimensions of diversity and work to honor individuals’ privacy as we work to pursue UW’s vision. We reflect on what privacy means to different geographic regions, cultures, generations or individuals.
  • Excellence. We work to maintain and use quality data. We strive to present clean and reliable data to all who use it within the organization, and where appropriate, to other external organizations to meet business requirements.
  • Collaboration. We value partnership with people and organizations as a means for upholding privacy. We share personal data within the parameters of federal, state, and international law. Any partners any vendors who collect or process data on our behalf are directed through contracts or other means to protect the privacy of personal data.
  • Innovation. We may use personal data to enable the UW’s vision for the future, support the individuals we serve, and advance research. Before using personal data in this way, we endeavor to balance and manage the risk to the UW and privacy impact to individuals.

Privacy Principles

Our privacy principles encompass and expand on UW values to help ensure the collection and use of personal data reflects our humanitarian, ethical, and legal obligations. Our use of personal data stems from the purpose from which we collected the data. Our teaching, research, and service mission compel us to think about how use of personal data, in its identifiable or de-identified format, impacts our trusted relationships with individuals and communities.

  • Minimization. We think carefully about what data are required to fulfill a specific operation, project, or system and collect only the necessary data elements. We only retain personal data as long as needed to fulfill its purpose and consistent with the UW records retention schedules and relevant or applicable law.
  • Accountability. We are accountable for governing and managing the collection, use, and flow of personal data in ways that are consistent with our commitments.
  • Protection. We think carefully about controls to safeguard personal data. Regardless of the form (paper, digital, electronic, etc.), this includes: considering de-identification, anonymization, obfuscation; implementing technical controls such as encryption and role-based access; and securing the physical location and storage.
  • Awareness. We endeavor to be transparent about what data is collected, how it is used, how it is shared, and how it’s stored. We strive to make choices clear and manageable, and to provide opportunities for you to exercise privacy preferences by using either opt-out or opt-in methodologies.

Security

Organizations that manage or utilize information systems with learning analytics data are required to follow the UW information security policies for safeguarding UW institutional information.

Student data used in learning analytics

Common data sources that are in scope include, but are not limited to:

  • Enrollment information. Data includes degree program affiliation, academic probationary status, campus affiliation, and demographics provided by the student.
  • Transcript data. Data from past and current courses, including grade data.
  • Data from teaching and learning tools. Activity in tools like Canvas, Panopto or PollEverywhere, such as viewing patterns, number of discussion board posts, and logins.
  • Student applications data. Data from MyUW, MyPlan and other student systems.

Organizations that engage third-party products or services in the personal data processing, such as learning analytics, are required to have the third-party sign the UW Personal Data Processing Agreement (PDPA). The PDPA establishes the purpose and parameters for data processing and clarifies roles and responsibilities between the UW and a contractor. The student data in scope will not be sold by UW to third-party vendors. Further, third-party vendors who have access to these data to provide services to UW students will not be able to sell these data to other parties and must follow UW data privacy and ownership requirements.

Common data that are not in scope:

  • Health information. Disabilities and data on visits to student health centers.
  • Complaints. Formal complaints made by a student.
  • Affiliations. Affiliations not directly related to academic success, such as religious or political affiliations.
  • Social media activity. Student activity on third-party social networking sites.
  • Special categories of personal data that require affirmative consent before processing: Any records or information relating to minors, older adults or seniors, criminal offenses, citizenship and/or immigration status, race or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, genetic or biometric data used to identify a natural person, health, sex life, or sexual orientation.

Version 4.0/08.21.2020

Last reviewed May 26, 2021