| Project/Area Number |
19J15167
|
|---|
| Research Category |
Grant-in-Aid for JSPS Fellows
|
| Allocation Type | Single-year Grants |
|---|
| Section | 国内 |
|---|
| Review Section |
Basic Section 09070:Educational technology-related
|
|---|
| Research Institution | Kyoto University |
|---|
Principal Investigator |
ABOU KHALIL Victoria 京都大学, 学術情報メディアセンター, 特別研究員(PD)
|
|---|
| Project Period (FY) |
2019-04-25 – 2021-03-31
|
| Project Status |
Completed (Fiscal Year 2020)
|
| Budget Amount *help |
¥2,100,000 (Direct Cost: ¥2,100,000)
Fiscal Year 2020: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2019: ¥1,100,000 (Direct Cost: ¥1,100,000)
|
|---|
| Keywords | Personalization / Vocabulary learning / Language learning / Recommender system / Translation / CALL / Learner-centered / Vocabulary / Informal learning / Language Learning |
|---|
| Outline of Research at the Start |
This research target second language learning. The aim is to create a system that identifies false friends based on the learner’s activity and other learners’ activities, suggest the adequate translation to the student and prevents the learner from making future false friends’ mistakes.
|
| Outline of Annual Research Achievements |
During the last year, I showed through a case study that currently available language learning curriculum have does not answer the real-life needs of language learners as the words taught cover a very small part of the words language learners need. Consequently, I proposed to take advantage of today's availability of learner data and proposed a framework for personalized vocabulary learning. The framework consists of two main parts. The first part provides language learners translation based on their learning logs. The second part is a demographic based, purpose-based and content-based recommendation system. The recommendation system suggests vocabulary that other people with the same demographics or purpose searched for in their digital dictionaries. Learners' data also enables content-based recommendations by suggesting vocabulary that is thematically similar to the one translated by the learner in the past. Additionally, the past logs are used to detect a change in purpose or recommend words based on the situated context of the learner.
We tested each part of the system separately, and showed that the personalized translation can detect the intended meaning of the learner better than Google Translate. We also showed that recommending vocabulary to language learners using their past learning activity increases their learning achievement and motivation.
|
| Research Progress Status |
令和2年度が最終年度であるため、記入しない。
|
| Strategy for Future Research Activity |
令和2年度が最終年度であるため、記入しない。
|
