@inproceedings{fdbc510d8e02407c8a5dfdd05327af16,
title = "Detecting learner's to-be-forgotten items using online handwritten data",
abstract = "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.",
keywords = "Digital Ink, Handwriting, ITS, Language Learning, Rote Learning",
author = "Hiroki Asai and Hayato Yamana",
note = "Publisher Copyright: {\textcopyright} 2015 ACM International Conference Proceeding Series. All rights reserved.; 15th New Zealand Conference on Human-Computer Interaction, CHINZ 2015 ; Conference date: 03-09-2015 Through 04-09-2015",
year = "2015",
month = sep,
day = "3",
doi = "10.1145/2808047.2808049",
language = "English",
isbn = "9781450336703",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "17--20",
editor = "Nichols, {David M.} and Masood Masoodian and Annika Hinze",
booktitle = "CHINZ 2015 - Proceedings of the 15th New Zealand Conference on Human-Computer Interaction",
}