Designing a Recommender System for Mobile Applications Focusing on Relative Importance Weights of Learner-related Variables

Woorin Hwang, Hyo Jeong So, Chiyoung Song, Hyeji Jang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings
EditorsSridhar 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
PublisherAsia-Pacific Society for Computers in Education
Pages410-412
Number of pages3
ISBN (Electronic)9789869721493
StatePublished - 28 Nov 2022
Event30th International Conference on Computers in Education Conference, ICCE 2022 - Kuala Lumpur, Malaysia
Duration: 28 Nov 20222 Dec 2022

Publication series

Name30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings
Volume1

Conference

Conference30th International Conference on Computers in Education Conference, ICCE 2022
Country/TerritoryMalaysia
CityKuala Lumpur
Period28/11/222/12/22

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT)(No. 2020R1F1A1073469).

Publisher Copyright:
© 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings.

Keywords

  • Feature Importance
  • Learner Variables
  • Mobile Applications
  • Recommender System

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