Dynamic handwriting signal features predict domain expertise

S. Oviatt, K. Hang, J. Zhou, K. Yu, F. Chen

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)


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.
Original languageEnglish
Article number18
Number of pages21
JournalACM Transactions on Interactive Intelligent Systems
Issue number3
Publication statusPublished - Aug 2018


  • Dynamic handwriting
  • Empirical and statistical sciences
  • Hybrid techniques
  • Machine learning
  • Multimodal learning analytics
  • Pen signal features
  • Prediction of domain expertise
  • Total energy expenditure

Cite this