Detecting learner's to-be-forgotten items using online handwritten data

Hiroki Asai, Hayato Yamana

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


An effective learning system is indispensable for human beings with a limited life span. Traditional learning systems schedule repetition based on both the results of a recall test and learning theories such as the spacing effect. However, there is room for improvement from the perspective of remembrance-level estimation. In this paper, we focus on on-line handwritten data obtained from handwriting using a computer. We collected handwritten data from remembrance tests to both analyze the problem of traditional estimation methods and to build a new estimation model using handwritten data as the input data. The evaluation found that our proposed model can output a continuous remembrance-level value of zero to 1, whereas traditional methods output a only binary decision. In addition, the experiment showed that our proposed model achieves the best performance with an F-value of 0.69.

Original languageEnglish
Title of host publicationCHINZ 2015 - Proceedings of the 15th New Zealand Conference on Human-Computer Interaction
EditorsDavid M. Nichols, Masood Masoodian, Annika Hinze
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Print)9781450336703
Publication statusPublished - 2015 Sept 3
Event15th New Zealand Conference on Human-Computer Interaction, CHINZ 2015 - Hamilton, New Zealand
Duration: 2015 Sept 32015 Sept 4

Publication series

NameACM International Conference Proceeding Series


Other15th New Zealand Conference on Human-Computer Interaction, CHINZ 2015
Country/TerritoryNew Zealand


  • Digital Ink
  • Handwriting
  • ITS
  • Language Learning
  • Rote Learning

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


Dive into the research topics of 'Detecting learner's to-be-forgotten items using online handwritten data'. Together they form a unique fingerprint.

Cite this