Authentic Assessments: a Method to Detect Anomalies in Assessment Response Patterns Via Neural Network, Kate Cordell, PhD; Himanshu Rao; John Lyons, PhD
Surveys, questionnaires, and assessments are the only approved means for measuring outcomes, yet there is scarce evidence that these tools can perform well as outcomes measures.
A major barrier to utilizing these tools as measures of improvement in care is an inability to recognize when the responses are incomplete, inaccurate, or insincere. In this study, the researchers proposed a hybrid unsupervised-supervised approach to identify inauthentic assessments via anomalous response patterns.
For decades, assessment data has been collected by policy and ignored by practice due to limitations of utility. With the right approaches, we could uncover the patterns of what works for whom that are currently hidden in our existing assessments.
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