To embrace why and how people learn and how to combine learner characteristics for recommending foreign language learning mobile applications (apps), this research presents a recommender system based on the relative importance weights of learner-related variables. In developing the system, 100 adult learners used 4-6 foreign language learning apps, resulting in 557 user-satisfaction data to calculate the relative importance of 14 learner-related variables in four categories: (a) demographic information, (b) motivational orientation for language learning (instrumental/integrative), (c) learning style, and (d) learning experience. The result showed that the model considering the relative importance weights of learner-related variables outperforms the dummy model in predicting users' satisfaction with the apps.
|Title of host publication||30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings|
|Editors||Sridhar Iyer, Ju-Ling Shih, Ju-Ling Shih, Weiqin Chen, Weiqin Chen, Mas Nida MD Khambari, Mouna Denden, Rwitajit Majumbar, Liliana Cuesta Medina, Shitanshu Mishra, Sahana Murthy, Patcharin Panjaburee, Daner Sun|
|Publisher||Asia-Pacific Society for Computers in Education|
|Number of pages||3|
|State||Published - 28 Nov 2022|
|Event||30th International Conference on Computers in Education Conference, ICCE 2022 - Kuala Lumpur, Malaysia|
Duration: 28 Nov 2022 → 2 Dec 2022
|Name||30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings|
|Conference||30th International Conference on Computers in Education Conference, ICCE 2022|
|Period||28/11/22 → 2/12/22|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT)(No. 2020R1F1A1073469).
© 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings.
- Feature Importance
- Learner Variables
- Mobile Applications
- Recommender System