Task-specific information outperforms surveillance-style big data in predictive analytics

Research output: Contribution to journalJournal articleResearchpeer-review


Increasingly, human behavior can be monitored through the collection of data from digital devices revealing information on behaviors and locations. In the context of higher education, a growing number of schools and universities collect data on their students with the purpose of assessing or predicting behaviors and academic performance, and the COVID-19-induced move to online education dramatically increases what can be accumulated in this way, raising concerns about students' privacy. We focus on academic performance and ask whether predictive performance for a given dataset can be achieved with less privacy-invasive, but more task-specific, data. We draw on a unique dataset on a large student population containing both highly detailed measures of behavior and personality and high-quality third-party reported individual-level administrative data. We find that models estimated using the big behavioral data are indeed able to accurately predict academic performance out of sample. However, models using only low-dimensional and arguably less privacyinvasive administrative data perform considerably better and, importantly, do not improve when we add the high-resolution, privacy-invasive behavioral data. We argue that combining big behavioral data with "ground truth" administrative registry data can ideally allow the identification of privacy-preserving taskspecific features that can be employed instead of current indiscriminate troves of behavioral data, with better privacy and better prediction resulting.

Original languageEnglish
Article numbere2020258118
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number14
Publication statusPublished - 6 Apr 2021

    Research areas

  • Academic performance, Big data, Prediction, Privacy

Number of downloads are based on statistics from Google Scholar and www.ku.dk

No data available

ID: 260517561