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

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

doi:10.5281/zenodo.6853141

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

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

Department of Educational Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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 language	English
Title of host publication	Proceedings of the 15th International Conference on Educational Data Mining, EDM 2022
Editors	[given-name]Antonija Mitrovic, Nigel Bosch
Publisher	International Educational Data Mining Society
ISBN (Print)	9781733673631
DOIs	https://doi.org/10.5281/zenodo.6853141
State	Published - 2022
Event	15th International Conference on Educational Data Mining, EDM 2022 - Durham, United Kingdom Duration: 24 Jul 2022 → 27 Jul 2022

Publication series

Name	Proceedings of the International Conference on Educational Data Mining
ISSN (Electronic)	2960-2866

Conference

Conference	15th International Conference on Educational Data Mining, EDM 2022
Country/Territory	United Kingdom
City	Durham
Period	24/07/22 → 27/07/22

Bibliographical note

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

Keywords

Diversity
Mobile language learning applications
Recommendation system

Access to Document

10.5281/zenodo.6853141

Cite this

Song, J., Yang, K., So, H. J., & Jang, H. (2022). Recommendation System of Mobile Language Learning Applications: Similarity versus Diversity in Learner Preference. In A. Mitrovic, & N. Bosch (Eds.), Proceedings of the 15th International Conference on Educational Data Mining, EDM 2022 (Proceedings of the International Conference on Educational Data Mining). International Educational Data Mining Society. https://doi.org/10.5281/zenodo.6853141

Song, Juyeong ; Yang, Kisu ; So, Hyo Jeong et al. / Recommendation System of Mobile Language Learning Applications : Similarity versus Diversity in Learner Preference. Proceedings of the 15th International Conference on Educational Data Mining, EDM 2022. editor / [given-name]Antonija Mitrovic ; Nigel Bosch. International Educational Data Mining Society, 2022. (Proceedings of the International Conference on Educational Data Mining).

@inproceedings{961d1bf7aef642af9abf4c05ef72384b,

title = "Recommendation System of Mobile Language Learning Applications: Similarity versus Diversity in Learner Preference",

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.",

keywords = "Diversity, Mobile language learning applications, Recommendation system",

author = "Juyeong Song and Kisu Yang and So, \{Hyo Jeong\} and Hyeji Jang",

note = "Publisher Copyright: {\textcopyright} 2022 Copyright is held by the author(s).; 15th International Conference on Educational Data Mining, EDM 2022 ; Conference date: 24-07-2022 Through 27-07-2022",

year = "2022",

doi = "10.5281/zenodo.6853141",

language = "English",

isbn = "9781733673631",

series = "Proceedings of the International Conference on Educational Data Mining",

publisher = "International Educational Data Mining Society",

editor = "[given-name]Antonija Mitrovic and Nigel Bosch",

booktitle = "Proceedings of the 15th International Conference on Educational Data Mining, EDM 2022",

}

Song, J, Yang, K, So, HJ & Jang, H 2022, Recommendation System of Mobile Language Learning Applications: Similarity versus Diversity in Learner Preference. in A Mitrovic & N Bosch (eds), Proceedings of the 15th International Conference on Educational Data Mining, EDM 2022. Proceedings of the International Conference on Educational Data Mining, International Educational Data Mining Society, 15th International Conference on Educational Data Mining, EDM 2022, Durham, United Kingdom, 24/07/22. https://doi.org/10.5281/zenodo.6853141

Recommendation System of Mobile Language Learning Applications: Similarity versus Diversity in Learner Preference. / Song, Juyeong; Yang, Kisu; So, Hyo Jeong et al.
Proceedings of the 15th International Conference on Educational Data Mining, EDM 2022. ed. / [given-name]Antonija Mitrovic; Nigel Bosch. International Educational Data Mining Society, 2022. (Proceedings of the International Conference on Educational Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Recommendation System of Mobile Language Learning Applications

T2 - 15th International Conference on Educational Data Mining, EDM 2022

AU - Song, Juyeong

AU - Yang, Kisu

AU - So, Hyo Jeong

AU - Jang, Hyeji

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Diversity

KW - Mobile language learning applications

KW - Recommendation system

UR - https://www.scopus.com/pages/publications/105023159923

U2 - 10.5281/zenodo.6853141

DO - 10.5281/zenodo.6853141

M3 - Conference contribution

AN - SCOPUS:85174834641

SN - 9781733673631

T3 - Proceedings of the International Conference on Educational Data Mining

BT - Proceedings of the 15th International Conference on Educational Data Mining, EDM 2022

A2 - Mitrovic, [given-name]Antonija

A2 - Bosch, Nigel

PB - International Educational Data Mining Society

Y2 - 24 July 2022 through 27 July 2022

ER -

Song J, Yang K, So HJ, Jang H. Recommendation System of Mobile Language Learning Applications: Similarity versus Diversity in Learner Preference. In Mitrovic A, Bosch N, editors, Proceedings of the 15th International Conference on Educational Data Mining, EDM 2022. International Educational Data Mining Society. 2022. (Proceedings of the International Conference on Educational Data Mining). doi: 10.5281/zenodo.6853141

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

Abstract

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Bibliographical note

Keywords

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