Personalizing Augmented Flashcards Towards Long-Term Vocabulary Learning
| Language | English |
|---|---|
| Type | Proceedings of International Conferences |
| Authors | Yuichiro Iwashita, Andrew Vargo, Motoi Iwata, Koichi Kise |
| Title | Personalizing Augmented Flashcards Towards Long-Term Vocabulary Learning |
| Book title | Proceedings of the Augmented Humans International Conference 2024 |
| Series | AHs '24 |
| Pages | pp.263-266 |
| Number of pages | 4 |
| Publisher | Association for Computing Machinery |
| Address | New York, NY, USA |
| Location | Melbourne, VIC, Australia |
| Reviewed | Reviewed |
| Presentation type | Poster |
| Date | April 2024 |
| Research fields | Learning Analytics, Sensing, Pattern recognition |
| DOI | 10.1145/3652920.3653045 |
| URL | https://doi.org/10.1145/3652920.3653045 |
| Key | Iwashita2024 (id=1969) |
Abstract
Flashcards are one of the most popular tools for learning vocabulary for second-language learners. While flashcard mediated learning is efficient, it may not induce motivation for continued use. Some researchers have proposed augmented flashcards that provide multimedia contexts to motivate people to study. However, the augmented flashcards also have a problem that it takes time to learn each target word. Understanding this tradeoff, we introduce a system that users can learn vocabulary with both standard and augmented flashcards. In addition, our system recommends the best learning strategy to users adaptively, and realizes the long-term vocabulary learning. In this paper, we describe the system and present the results of the preliminary data analysis towards the long-term vocabulary learning.
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