Detail of Publication
| Text Language | English |
|---|---|
| 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 pages |
| Publisher | Association for Computing Machinery |
| Address | New York, NY, USA |
| Location | Melbourne, VIC, Australia |
| Reviewed or not | Reviewed |
| Presentation type | Poster |
| Month & Year | April 2024 |
| 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. |
| DOI | 10.1145/3652920.3653045 |
| URL | https://doi.org/10.1145/3652920.3653045 |
- Following file is available.
- Entry for BibTeX
@InProceedings{Iwashita2024, author = {Yuichiro Iwashita and Andrew Vargo and Motoi Iwata and Koichi Kise}, title = {Personalizing Augmented Flashcards Towards Long-Term Vocabulary Learning}, book_title = {Proceedings of the Augmented Humans International Conference 2024}, year = 2024, month = apr, pages = {263--266}, numpages = {4}, DOI = {10.1145/3652920.3653045}, URL = {https://doi.org/10.1145/3652920.3653045}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, location = {Melbourne, VIC, Australia} }