Privacy Management for Student Data in Higher Education Learning Analytics Applications
Open Access
- Author:
- Winkler, Stephanie
- Graduate Program:
- Informatics
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- February 23, 2021
- Committee Members:
- Dongwon Lee, Thesis Advisor/Co-Advisor
Daniel Susser, Committee Member
S. Shyam Sundar, Committee Member
Aiping Xiong, Committee Member
Mary Beth Rosson, Program Head/Chair - Keywords:
- learning analytics
privacy
higher education - Abstract:
- University students are currently one of the most targeted groups of people for data collection and analytics. Outside of the likelihood of being recruited for research studies, during their time at the university they are required to interact with various information systems which all collect significant amounts of data. Systems used in the classroom, like learning management systems, are usually the most visible to students but other technologies are also passively collecting student information (e.g. student identification cards, educational applications, social media). Learning analytics includes the analysis of this data in order to understand and optimize learning and the learning environment through the use of data science methodologies. While educational data is widely considered to be sensitive, student privacy is not always considered a high priority in the development and deployment of learning analytics applications beyond basic information security. Prior work has also shown that previous assumptions made with privacy management are no longer applicable with widespread ubiquitous data collection practices. This thesis investigates two research questions RQ1: What factors influence student privacy expectations in learning analytics systems? RQ2: How is policy used as a privacy management strategy for learning analytics in higher education? The first question is examined using quantitative vignettes. The second question is addressed through the qualitative analysis of current learning analytics policies at higher education institutions. The results show an expectation for students to grant consent for their data to be used in learning analytics systems. Further, their willingness to grant consent for data to be used is at least partially dependant on the type of data requested. While protecting privacy through policy may be a common strategy in other sectors, it is rare in United States higher education institutions at this time with only a small fraction of the institutions with learning analytics programs adopting a policy to govern the programs.