Dynamic handwriting signal features predict domain expertise

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

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)

Abstract

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
Volume8
Issue number3
DOIs
Publication statusPublished - Aug 2018

Keywords

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

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