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User experience improvement of japanese language mobile learning application through mental model and A/B testing

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 10 No. 3 (2020)

DOI
10.11591/ijece.v10i3.pp2659-2667
ISSN
2088-8708, 2088-8708
Published
2020-06-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Advances in smartphone technology have led to the strong emergence of mobile learning (m-learning) on the market to support foreign language learning purposes, especially for the Japanese language. No matter what kind of m-learning application, their goal should help learners to learn the Japanese language independently. However, popular Japanese m-learning applications only accommodate on enhancing reading, vocabulary and writing ability so that user experience issues are still prevalent and may affect the learning outcome. In the context of user experience, usability is one of the essential factors in mobile application development to determine the level of the application’s user experience. In this paper, we advocate for a user experience improvement by using the mental model and A/B testing. The mental model is used to reflect the user’s inner thinking mode. A comparative approach was used to investigate the performance of 20 high-grade students with homogenous backgrounds and coursework. User experience level was measured based on the usability approach on pragmatic quality and hedonic quality like effectiveness (success rate of task completion), efficiency (task completion time) and satisfaction. The results then compared with an existing Japanese m-learning to gather the insight of improvement of our proposed method. Experimental results show that both m-learning versions proved can enhance learner performance in pragmatic attributes. Nevertheless, the study also reveals that an m-learning that employs the conversational mental model in the learning process is more valued by participants in hedonic qualities. Mean that the proposed m-learning which is developed with the mental model consideration and designed using A/B testing is able to provide conversational learning experience intuitively.

Collaboration network of the authors

22 authors · 43 co-authorship links · 4 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Brawijaya
  • National Central University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Lutfi FananiUniversity of Brawijaya4
Deron LiangNational Central University4
Ginanjar Wisnu Ifan A.University of Brawijaya2
Reinhard Jonathan SilalahiUniversity of Brawijaya2
Sholeh Hadi PramonoUniversity of Brawijaya2
Mukhammad Sharif HidayatullohUniversity of Brawijaya2
Mohamad Handy NugrahaUniversity of Brawijaya2
Faizatul AmaliaUniversity of Brawijaya1
Rizki Tri SulistyoUniversity of Brawijaya1
Andre DiofanuUniversity of Brawijaya1
Noveria Anggraeni FiajiUniversity of Brawijaya1
Prima ZulvarinaUniversity of Brawijaya1
Ahmad Afif SupiantoUniversity of Brawijaya1
Santi Yunika SufianaUniversity of Brawijaya1
Thareq Abel RamadhanAffiliation unknown1
Thareq Abel RamadhanUniversity of Brawijaya1
Mahardeka Tri AnanataAffiliation unknown1
Dava Mohammad HamkaUniversity of Brawijaya1
Muhamad Fauzan WahyudiAffiliation unknown1
Muhamad Fauzan WahyudiUniversity of Brawijaya1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>User experience improvement of japanese language mobile learning application through mental model and A/B testing</dc:title><dc:creator>Brata, Komang Candra</dc:creator><dc:creator>Brata, Adam Hendra</dc:creator><dc:description>&lt;jats:p&gt;Advances in smartphone technology have led to the strong emergence of mobile learning (m-learning) on the market to support foreign language learning purposes, especially for the Japanese language. No matter what kind of m-learning application, their goal should help learners to learn the Japanese language independently. However, popular Japanese m-learning applications only accommodate on enhancing reading, vocabulary and writing ability so that user experience issues are still prevalent and may affect the learning outcome. In the context of user experience, usability is one of the essential factors in mobile application development to determine the level of the application’s user experience. In this paper, we advocate for a user experience improvement by using the mental model and A/B testing. The mental model is used to reflect the user’s inner thinking mode. A comparative approach was used to investigate the performance of 20 high-grade students with homogenous backgrounds and coursework. User experience level was measured based on the usability approach on pragmatic quality and hedonic quality like effectiveness (success rate of task completion), efficiency (task completion time) and satisfaction. The results then compared with an existing Japanese m-learning to gather the insight of improvement of our proposed method. Experimental results show that both m-learning versions proved can enhance learner performance in pragmatic attributes. Nevertheless, the study also reveals that an m-learning that employs the conversational mental model in the learning process is more valued by participants in hedonic qualities. Mean that the proposed m-learning which is developed with the mental model consideration and designed using A/B testing is able to provide conversational learning experience intuitively.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2020-06-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v10i3.pp2659-2667</dc:identifier><dc:identifier>10.11591/ijece.v10i3.pp2659-2667</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 10 No 3 (2020); 2659</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/20880/pdf</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>