Recommendation System of Mobile Language Learning Applications: Similarity versus Diversity in Learner Preference

Juyeong Song, Hyo Jeong So, Kisu Yang, Hyeji Jang

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

Abstract

Recognizing the potential of the preference-inconsistent recommendation systems (RS) for learning, this paper aims to examine two recommendation algorithms for mobile language learning applications: RS with similarity and RS with diversity. Diversity was measured through learning styles (how learners learn) and achievement goals (why learners learn). A total of 160 learners participated in the study for building learner profiles and the recommendation algorithm. Overall, our results with RSME indicate that both RS with similarity and RS with diversity performed better than the random recommendation.

Original languageEnglish
Title of host publicationProceedings of the 15th International Conference on Educational Data Mining, EDM 2022
PublisherInternational Educational Data Mining Society
ISBN (Electronic)9781733673631
DOIs
StatePublished - 2022
Event15th International Conference on Educational Data Mining, EDM 2022 - Hybrid, Durham, United Kingdom
Duration: 24 Jul 202227 Jul 2022

Publication series

NameProceedings of the 15th International Conference on Educational Data Mining, EDM 2022

Conference

Conference15th International Conference on Educational Data Mining, EDM 2022
Country/TerritoryUnited Kingdom
CityHybrid, Durham
Period24/07/2227/07/22

Bibliographical note

Publisher Copyright:
© 2022 Copyright is held by the author(s).

Keywords

  • Diversity
  • Mobile language learning applications
  • Recommendation system

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