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.
| Original language | English |
|---|---|
| Title of host publication | CHINZ 2015 - Proceedings of the 15th New Zealand Conference on Human-Computer Interaction |
| Publisher | Association for Computing Machinery |
| Pages | 17-20 |
| Number of pages | 4 |
| ISBN (Print) | 9781450336703 |
| DOIs | |
| Publication status | Published - 2015 Sep 3 |
| Event | 15th New Zealand Conference on Human-Computer Interaction, CHINZ 2015 - Hamilton, New Zealand Duration: 2015 Sep 3 → 2015 Sep 4 |
Other
| Other | 15th New Zealand Conference on Human-Computer Interaction, CHINZ 2015 |
|---|---|
| Country | New Zealand |
| City | Hamilton |
| Period | 15/9/3 → 15/9/4 |
Fingerprint
Keywords
- Digital Ink
- Handwriting
- ITS
- Language Learning
- Rote Learning
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Software
Cite this
Detecting learner's to-be-forgotten items using online handwritten data. / Asai, Hiroki; Yamana, Hayato.
CHINZ 2015 - Proceedings of the 15th New Zealand Conference on Human-Computer Interaction. Association for Computing Machinery, 2015. p. 17-20.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Detecting learner's to-be-forgotten items using online handwritten data
AU - Asai, Hiroki
AU - Yamana, Hayato
PY - 2015/9/3
Y1 - 2015/9/3
N2 - 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.
AB - 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.
KW - Digital Ink
KW - Handwriting
KW - ITS
KW - Language Learning
KW - Rote Learning
UR - http://www.scopus.com/inward/record.url?scp=85051474896&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051474896&partnerID=8YFLogxK
U2 - 10.1145/2808047.2808049
DO - 10.1145/2808047.2808049
M3 - Conference contribution
AN - SCOPUS:85051474896
SN - 9781450336703
SP - 17
EP - 20
BT - CHINZ 2015 - Proceedings of the 15th New Zealand Conference on Human-Computer Interaction
PB - Association for Computing Machinery
ER -