Data mining and predictive analytics—collectively referred to as “big data”—are increasingly used in higher education to classify students and predict student behavior. But while the potential benefits of such techniques are significant, realizing them presents a range of ethical and social challenges. The immediate challenge con- siders the extent to which data mining’s outcomes are themselves ethical with respect to both individuals and institutions. A deep challenge, not readily apparent to institutional researchers or administrators, considers the implications of uncritical understanding of the scientific basis of data mining. These challenges can be met by understanding data mining as part of a value-laden nexus of problems, models, and interventions; by protecting the contextual integrity of information flows; and by ensuring both the scientific and normative validity of data mining applications.
Researchers at Harvard University secretly took photographs of roughly 2,000 students to study classroom attendance, prompting privacy complaints from faculty members, The Boston Globe reports.
Researchers in Harvard’s Initiative for Learning and Teaching conducted the study, which was approved by the university’s institutional review board and involved installing cameras in 10 classrooms in the spring of 2013. The cameras took one image every minute, and a computer program scanned the photos to determine which seats were empty and which were filled.
A high-tech effort to study classroom attendance at Harvard University that used secret photo surveillance is raising questions about research ethics among the institution’s faculty members. The controversy heated up on Tuesday night, when a computer-science professor, Harry R. Lewis, questioned the study at a faculty meeting.
During the study, which took place in the spring of 2013, cameras in 10 Harvard classrooms recorded one image per minute, and the photographs were scanned to determine which seats were filled.
Willis and Pistil discuss four themes that emerged during discussion about the ethical implications of learning analytics.