The practice of predicting a student's level of success in order to provide targeted assistance, termed "learning analytics,"� emerged from a well-established business intelligence model popularly called "Big Data"�. The ethical impact of Big Data on business practices has been undeniable, however, the ethical concerns of Big Data methodology in academia have yet to be explored, as research in this emerging discipline is relatively new. Thus, the overarching question for this study is as follows: How can we use rhetorical, scientific, and technical communication perspectives to understand ethical concerns in the design, application, and documentation of learning analytics in post-secondary education? To investigate this question, I conducted a five-stage study using a cross-disciplinary perspective based on existing frameworks in rhetoric and scientific and technical communication, united by their ethical lens, from genre, persuasion, human-computer interaction, social power, semiotics, visual design, new media literacy, and pedagogy to create a matrix for understanding ethical concerns in learning analytics in post-secondary education. During this study, the inability of students to provide input into the learning analytics process was the concern most often revealed, followed by a lack of context for interpreting the data by both institutional users and students, and the potential inaccuracies in the predictive model caused by inaccurate or incomplete data. Secondary concerns included an undefined institutional responsibility to act on data, which could put the institution at risk for legal action, as well as the possibility for discrimination to occur during the learning analytics process. I provide strategies and responses to address ethical concerns in the design and documentation of learning analytics that should constitute a minimum level of ethical action. This minimal implementation would ensure that students are shown goodwill by the institution (design), and that institutions are properly implementing learning analytics in terms of transparency of process and equality of benefit to the student (documentation). The strategies and responses to address ethical concerns in the application of learning analytics would be more complex for each situation and type of learning analytics used, but should always consider student engagement and success as the priority.
University of Minnesota Ph.D. dissertation. August 2015. Major: Rhetoric and Scientific and Technical Communication. Advisor: Ann Hill Duin. 1 computer file (PDF); ix, 138 pages.
Understanding Ethical Concerns in the Design, Application, and Documentation of Learning Analytics in Post-secondary Education.
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