PENAMBAHBAIKAN ELEMEN MULTIMEDIA DALAM APLIKASI PEMBELAJARAN MUDAH ALIH: LEARNING JAPANESE IS FUN: Enhancement of Multimedia Elements in A Mobile Learning Application: Learning Japanese Is Fun

Nur Aisya Insyira Manaf; Nuralyya Natasha Abd. Halim; Siti Rohaida Alimin; Ahmad Khalid Kamarul Saman

doi:10.53840/myjict10-2-223

Authors

Nur Aisya Insyira Manaf Faculty of Creative Multimedia and Computing, Universiti Islam Selangor, Malaysia
Nuralyya Natasha Abd. Halim Faculty of Creative Multimedia and Computing, Universiti Islam Selangor, Malaysia
Siti Rohaida Alimin Faculty of Creative Multimedia and Computing, Universiti Islam Selangor, Malaysia
Ahmad Khalid Kamarul Saman Faculty of Creative Multimedia and Computing, Universiti Islam Selangor, Malaysia

DOI:

https://doi.org/10.53840/myjict10-2-223

Abstract

This study examines the enhancement of multimedia elements in a mobile-based Japanese language learning application, based on a student project titled Learning Japanese is Fun. The application was developed for beginner-level Japanese language learners through the use of multimedia elements such as text, audio, video, animation, and graphics. The study will employ a survey approach using an online questionnaire targeting students from Universiti Islam Selangor (UIS) who are enrolled in multimedia-related courses. It aims to identify user preferences toward multimedia elements in learning applications, analyze the strengths and weaknesses of these elements, and propose improvement strategies based on user feedback. The main research question focuses on determining which multimedia elements influence the effectiveness of a language learning application from the users’ perspective. Findings from the study indicate that UIS students responded positively to the use of multimedia elements in the application. However, respondents also identified several weaknesses, such as excessive text, unsuitable color choices, and unclear audio, that require attention in the application’s enhancement process. The results of this study are expected to help application developers identify key aspects that should be prioritized to improve the effectiveness of language learning through digital approaches.

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References

Al-Rabaiah, H. A., & Medina-Medina, N. (2021). Agile Beeswax: Mobile App Development Process and Empirical Study in Real Environment. Sustainability, 13(4), 1909. https://doi.org/10.3390/su13041909

Amrate, M., & Tsai, P.-H. (2024). Computer-assisted pronunciation training: A systematic review. ReCALL, 36(3), 356–374. https://doi.org/10.1017/S0958344024000181

Ariffin, K., De Mello, G., Husin, M. S., Anuarudin, S., & Omar, N. S. (2020). Utilizing the linguistic landscapes for contextual language learning. International Journal of e-Learning and Higher Education, 12(1), 163–174. Retrieved from https://myjms.mohe.gov.my/index.php/IJELHE/article/view/11128

Bantun, S., Nirsal, T. J., Marpaung, E. D., Tarigan, P. C. D., Hasibuan, J. Y. P. Y. S., & Sari, J. Y. (2024). Step-by-step: Learning Japanese writing on Android for beginners. TEM Journal, 13(2), 1378–1386. https://doi.org/10.18421/TEM132-52

Bang, H. J., Setoguchi, E., Mackey, A., & Fujii, A. (2024). L2 learning outcomes of a research-based digital app for Japanese children. Studies in Second Language Acquisition, 46(2), 504–534. https://doi.org/10.1017/S0272263124000147

Cui, J. (2024). Japanese Language Teaching and Learning in the New Media Era. Lecture Notes on Language and Literature, 7(2), 229–238. https://doi.org/10.23977/langl.2024.070229

Dillon, T. (2023). Effects of pronunciation training using automatic speech recognition. Teaching English with Technology, 23(2), 20–39. https://files.eric.ed.gov/fulltext/EJ1385403.pdf

Godwin-Jones, R. (2011). Emerging technologies: Mobile apps for language learning. Language Learning & Technology, 15(2), 2–11. Retrieved from https://www.researchgate.net/publication/279637329_Emerging_technologies_Mobile_apps_for_language_learning

Gómez, C. A., & Pérez, L. M. (2022). Enhancing English pronunciation through gamification. Revista UIS Humanidades, 51(2), 45–63. https://doi.org/10.18273/revhum.v51n2-2022003

Guo, P., Li, H., & Wang, X. (2024). A systematic review of collaborative mobile-assisted language learning (C-MALL) practices. Humanities and Social Sciences Communications, 11(1), 1–15. https://doi.org/10.1057/s41599-024-03940-3

Inceoğlu, S., Abraham, L. B., Cheng, K. H. C., Chen, H. I., Kim, D., & Wang, J. (2024). Monitoring student behavior in autonomous automatic speech recognition-based pronunciation practice. System, 124, 103387. https://doi.org/10.1016/j.system.2024.103387

Jošt, G., & Taneski, V. (2025). State-of-the-Art Cross-Platform Mobile Application Development Frameworks: A Comparative Study of Market and Developer Trends. Informatics, 12(2), 45. https://doi.org/10.3390/informatics12020045

Kumar, B. A., & Goundar, M. S. (2023). Developing mobile language learning applications: A systematic literature review. Education and Information Technologies, 28, 5651–5671. Retrieved from https://www.researchgate.net/publication/365174375_Developing_Mobile_Language_Learning_Applications_A_Systematic_Literature_Review

Lakens, D. (2022). Sample size justification. Collabra: Psychology, 8(1), Article 33267. https://doi.org/10.1525/collabra.33267

Luo, Z. (2023). The Effectiveness of Gamified Tools for Foreign Language Learning (FLL): A Systematic Review. Behavioral Sciences, 13(4), 331. https://doi.org/10.3390/bs13040331

Mahdi, D. A. (2022). Improving speaking and presentation skills through interactive multimedia environment for non-native speakers of English. SAGE Open, 12(1), 1–12. https://doi.org/10.1177/21582440221079811

Mannino, R. G., Arconada Alvarez, S. J., Greenleaf, M., et al. (2023). Navigating the complexities of mobile medical app development from idea to launch, a guide for clinicians and biomedical researchers. BMC Medicine, 21, 109. https://doi.org/10.1186/s12916-023-02833-7

Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511811678

Mayer, R. E. (2021). Multimedia learning (3rd ed.). Cambridge University Press. https://doi.org/10.1017/9781316941355

Metruk, R. (2024). Mobile-assisted language learning and pronunciation instruction: A systematic literature review. Education and Information Technologies, 29(2), 1437–1456. https://doi.org/10.1007/s10639-024-12453

Mohamad Rohana, N. A., Surtahman, A. W., Saja, I., & Mazlan, N. A. (2023). Multimedia technology in education: Islamic perspective. International Journal of Academic Research in Business and Social Sciences, 13(9), 458–472. https://doi.org/10.6007/IJARBSS/v13-i9/18413

Murphy, D. H., Hoover, K. M., & Castel, A. D. (2023). The effect of video playback speed on learning and mind-wandering in younger and older adults. Memory. https://www.researchgate.net/publication/369825141_The_effect_of_video_playback_speed_on_learning_and_mind-wandering_in_younger_and_older_adults

Okumuş Dağdeler, K., & Koçak, Ö. (2023). Mobile-assisted vocabulary learning: A systematic review of empirical studies. Smart Learning Environments, 10(1), 1–19. https://doi.org/10.1186/s40561-023-00235-z

Rahman, M. M. (2023). Sample size determination for survey research and non-probability sampling techniques: A review and set of recommendations. Journal of Entrepreneurship, Business and Economics, 11(1), 42–62. Retrieved from https://scientificia.com/index.php/JEBE/article/view/201

Rahman, M. M., & Singh, R. (2023). Gamification in English as second language (ESL) learning: A systematic review. Arab World English Journal, 14(4), 277–293. https://doi.org/10.6007/IJARPED/v13-i4/24069

Shohor, A. S., & Hashim, H. (2024). The application of Mobile-Assisted Language Learning (MALL) in ESL classroom: A systematic literature review (2020–2024). International Journal of Academic Research in Progressive Education and Development, 13(3), 3643–3660. https://hrmars.com/papers_submitted/22899/the-application-of-mobile-assisted-language-learning-mall-in-esl-classroom-a-systematic-literature-review-2020-2024.pdf

Sun, H., Wang, T., & Yu, M. (2023). Influence of stroke-order learning on Chinese character recognition among Chinese as a second language learners. SSRN. https://ssrn.com/abstract=4573453

Sun, W., Ma, Q., & Luo, X. (2023). The impact of automatic speech recognition technology on second-language pronunciation and speaking skills: A mixed-methods investigation. Frontiers in Psychology, 14, 1210187. https://doi.org/10.3389/fpsyg.2023.1210187

Takenouchi, T. (2022). The effects of pronunciation instruction using speech recognition software for adult learners of English. Journal of the Practical English Phonetics Society of Japan, 3, 1–29. https://doi.org/10.57512/jpepsj.3.0_1

Tarigan, W. P. L., Sipahutar, H., & Harahap, F. (2023). The impact of an interactive digital learning module on students’ academic performance and memory retention. Computers and Children, 2(2), em004. https://doi.org/10.29333/cac/13654

Terasawa, T., & Segawa, H. (2025). A critical review of L2 teaching and learning research in Japan (2019–2023). Language Teaching, 58(3), 329–358. https://doi.org/10.1017/S0261444825000072

Thurairasu, V. (2022). Gamification-based learning as the future of language learning: An overview. European Journal of Humanities and Social Sciences, 2(6), 62–69. https://doi.org/10.24018/ejsocial.2022.2.6.35

Vithani, T., & Kumar, A. (2014). Modeling the Mobile Application Development Lifecycle. Proceedings of the International MultiConference of Engineers and Computer Scientists 2014 (Vol. I). Hong Kong: IAENG. Retrieved from https://www.iaeng.org/publication/IMECS2014/IMECS2014_pp596-600.pdf

Wambua, A. W., & Kamau, G. N. (2023). Security-aware Mobile Application Development Lifecycle (sMADLC). I. J. Education and Management Engineering, 13(2), 36-42. Retrieved from https://www.mecs-press.org/ijeme/ijeme-v13-n2/IJEME-V13-N2-5.pdf