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Benchmark Keystroke Biometrics Accuracy From High-Stakes Writing Tasks

Choi, Ikkyu; Hao, Jiangang; Deane, Paul; Zhang, Mo
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Report Number:
ETS Research Report
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Subject/Key Words:
Keystroke Logs, Biometrics, Benchmarks, Writing Tasks, Essays, ETS High School Equivalency Test (HiSET), High Stakes Tests, Test Security, Writing Fluency, Process Data, Proctoring, COVID-19, Machine Learning, Classification Accuracy


Biometrics are physical or behavioral human characteristics that can be used to identify a person. It is widely known that keystroke or typing dynamics for short, fixed texts (e.g., passwords) could serve as a behavioral biometric. In this study, we investigate whether keystroke data from essay responses can lead to a reliable biometric measure, with implications for test security and the monitoring of writing fluency and style changes. Based on keystroke data collected from a high-stakes writing testing setting, we established a preliminary biometric benchmark for detecting test-taker identity by using features extracted from their writing process logs. We report a benchmark keystroke biometric accuracy of equal error rate of 4.7% for identifying same versus different individuals on an essay task. In particular, we show that the inclusion of writing process features (e.g., features designed to describe the writing process) in addition to the widely used typing-timing features (e.g., features based on the time intervals between two-letter key sequences) improves the accuracy of the keystroke biometrics. The proposed keystroke biometrics can have important implications for the writing assessments administered through the remotely proctored tests that have been widely adopted during the COVID pandemic.

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