Helping Mobile Learners Know Unknown Words through their Reading Behavior
| Language | English |
|---|---|
| Type | Proceedings of International Conferences |
| Authors | Riku Higashimura, Andrew W. Vargo, Motoi Iwata, Koichi Kise |
| Title | Helping Mobile Learners Know Unknown Words through their Reading Behavior |
| Journal | CHI Conference on Human Factors in Computing Systems Extended Abstracts (CHI '22 Extended Abstracts) |
| Number of pages | 5 |
| Location | New Orleans, LA, USA |
| Reviewed | Reviewed |
| Presentation type | Poster |
| Date | April 2022 |
| Research fields | Learning Analytics |
| DOI | 10.1145/3491101.3519620 |
| URL | https://dl.acm.org/doi/abs/10.1145/3491101.3519620 |
| Key | Higashimura2022_CHI (id=1854) |
Abstract
Vocabulary acquisition is a fundamental part of learning a new language. In order to acquire new vocabulary, words with meanings that are unknown to the learner must be added to the language learning process. When searching for material in the target language, it is useful to know how much of a document is made up of currently unknown words. One simple way to estimate the unknown words in a document is to use the frequency of occurrence, which indicates the difficulty of the word. However, this approach can lead to missed unknown words. In this study, we aim to improve the accuracy of unknown word estimation by using reading activity data obtained from smartphone sensors and taking into account the individual learner's English reading behavior. We apply a novel user interface which allows us to improve estimation through reading behavior, without the use of eye-trackers.
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