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Syst."],"published-print":{"date-parts":[[2018,9,30]]},"abstract":"<jats:p>As commercial pen-centric systems proliferate, they create a parallel need for analytic techniques based on dynamic writing. Within educational applications, recent empirical research has shown that signal-level features of students\u2019 writing, such as stroke distance, pressure and duration, are adapted to conserve total energy expenditure as they consolidate expertise in a domain. The present research examined how accurately three different machine-learning algorithms could automatically classify users\u2019 domain expertise based on signal features of their writing, without any content analysis. Compared with an unguided machine-learning classification accuracy of 71%, hybrid methods using empirical-statistical guidance correctly classified 79\u201392% of students by their domain expertise level. In addition to improved accuracy, the hybrid approach contributed a causal understanding of prediction success and generalization to new data. 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