Learning Analytics Privacy and Security
Guidelines for safeguarding data in the digital learning environment
The Learning Analytics team takes seriously its role as a stakeholder in the processes of teaching and learning. Their goal is to promote good teaching practices, enable educational research, and contribute to better educational outcomes for students. For current and prospective services offered by the Learning Analytics team, they clarify their guiding principles through a document they espouse both internally and externally. This document is linked throughout, where users of data and tools are asked to consider these values when using data in their practice or research.
How does the Learning Analytics service protect faculty and student privacy and recognize transparency?
Learning analytics as a part of any course should be an open practice involving faculty and students, where in addition to the current ways we measure and understand the processes of learning, data provides an additional lens to view measures of engagement and knowledge. We encourage open dialogue in all environments where data is being used to help improve instruction and curriculum or to make individual interventions.
The Learning Analytics service is collaborating with the larger University Data Governance committee, seeking to adopt tools and data dictionaries that make it easier for faculty and students to know exactly what is considered Learning data.
Who can access course-level data that includes student or faculty identifiers?
Course-level data coming from instructional tools are released to service users only under specific circumstances where it is deemed appropriate. Based on guidance provided by the Future of Teaching Enhanced Education Committee, data at a course level that includes identifiers for faculty or students is accessible only through the following pathways:
- Instructors or co-instructors may access dashboards that contain student-level data specific to courses where they are a designated instructor in the site or tool.
- Data requests that include specific permission of the instructor of record may be furnished for internal or external research purposes.
Who decides policy around Learning Analytics at NYU?
The central policy body around learning analytics at NYU is the Future of Technology-Enhanced Education Committee. NYU faculty who wish to get more involved in policy around learning analytics are encouraged to reach out to the committee by contacting their college representative.
Principles of Practice for Learning Analytics at NYU
The Principles of Practice for Learning Analytics at NYU, detailed below, is the guide for carrying out activities related to data generated by the digital learning environment. The responsible use of data, whether for curriculum review queries by an instructional technology team, formal educational research by faculty partners, or as part of the team's dashboard service for faculty to enhance instructional efficacy, is a serious responsibility that the team acknowledges. The safeguarding of data and its utilization for the advancement of student learning are paramount concerns.
- All users of data through the Learning Analytics Service commit to safeguarding the legal, ethical, and effective use of data in accordance with other NYU policies.
- In alignment with University-wide Data Governance practices, NYU appoints a Data Steward and appropriate governance groups to create and approve policies around data collection, use, anonymization, retention, processing, and proper use that maximizes good practice and minimize potential harms around the use of learning analytics data and services.
- Clarify the purpose of learning analytics, and provide information about learning analytics data to the community.
- Disclose to students and faculty details about collected data, processing, and presentation of learning analytics data through services. Specify the uses of data such as pedagogical improvements, student self-driven change, and educational research.
- Ensure data security, integrity, and appropriate access privileges.
- Ensure that data is stored in a secure fashion, and that access to data is limited only to those with a legitimate educational purpose.
Enable Positive Interventions
- Provide guidance on the uses of data and analytics to inform teaching, learning, and research.
- Provide training and consultation to faculty, researchers and students that focus on data and tool use that supports learning. Make clear examples of service use and interventions that lead to improved learning outcomes.
Minimize Adverse Effects
- Provide guidance on the risks of using data to drive decisions.
- Provide training and consultation to faculty, researchers and students that minimizes labeling or biases towards students. Specify that learning analytics tools augment teaching and learning processes, and are not a replacement for measures of academic performance.
- Continued consideration of learning analytics policies.
- As the breadth of data and analytical methods surrounding learning analytics expands, the Learning Analytics Service must apply all the Principles of Practice when reviewing the collection of new data or development of new uses for data.
The Learning Analytics team promotes these principles in several ways:
- Alignment with national and international laws for data privacy and protection.
- Alignment with the University’s larger data governance policies related to data protection.
- Data request processes and service training that emphasizes the principles and expectations for data privacy to users.
- Continual dialogue with faculty and staff partner committees to enhance and refine our guiding principles.