Dynamic handwriting signal features predict domain expertise

Publication Type:
Journal Article
ACM Transactions on Interactive Intelligent Systems, 2018, 8 (3)
Issue Date:
Filename Description Size
a18-oviatt.pdfPublished Version2.53 MB
Adobe PDF
Full metadata record
© 2018 ACM. 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' 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' 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 79-92% 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. These novel findings open up opportunities to design new automated learning analytic systems and student-adaptive educational technologies for the rapidly expanding sector of commercial pen systems.
Please use this identifier to cite or link to this item: