Enhancing Vocabulary Retention Through Personalized Learning: Evaluating the Impact of the "Baicizhan" App on students' Long-Term Memory

Authors

  • Menglin Huang

    Faculty of Education and Liberal Arts, INTI I International University, Nilai 71800, Malaysia

  • Phawani Vijayaratnam

    Faculty of Education and Liberal Arts, INTI I International University, Nilai 71800, Malaysia

  • Norazrina binti Hamdan

    Faculty of Education and Liberal Arts, INTI I International University, Nilai 71800, Malaysia

  • Shaghayegh Shirzad

    Department of Education, Eiman Narimani Marketing Management Co. L. L.C., Dubai 1971, United Arab Emirates

  • Hamed Barjesteh

    Department of English Language, Islamic Azad University, Amol 4614153358, Iran

DOI:

https://doi.org/10.30564/fls.v7i12.10348
Received: 4 June 2025 | Revised: 11 August 2025 | Accepted: 12 August 2025 | Published Online: 5 November 2025

Abstract

In the digital era, personalized learning has gained significant attention in the realm of English vocabulary acquisition. While numerous mobile applications claim to enhance vocabulary memory, empirical evidence regarding their actual effectiveness and user experiences remains limited. This study aims to systematically evaluate the Baicizhan app in improving English vocabulary retention, meeting the diverse needs of learners, and supporting long-term memory storage. This study utilizes Cognitive Load Theory, Motivation Theory, and Constructivist Learning Theory to explore how the Baicizhan app enhances vocabulary acquisition and long-term memory by optimizing cognitive processing, fostering learner engagement, and promoting active knowledge construction through personalized learning pathways and multimodal resources. Data were collected through interviews with fifteen  diverse users, revealing that Baicizhan significantly enhances learner engagement, motivation, and vocabulary retention through its personalized and interactive features. The results suggest that the app is effective in promoting long-term memory consolidation, aligning with cognitive and metacognitive learning principles. However, to further improve its effectiveness across varied learning environments, the app should be optimized based on user feedback and specific learning profiles. This study provides both theoretical and practical insights for enhancing mobile-assisted language learning tools to improve education quality.

Keywords:

Vocabulary Building; Personalized Learning; Mobile Learning Application; Long-Term Memory

References

[1] Li, J., 2022. Adaptive Learning Model of English Vocabulary Based on Blockchain and Deep Learning. Mobile Information Systems. 2022, 1–13. DOI: https://doi.org/10.1155/2022/4554190

[2] Simanjuntak, C.P., Simanjuntak, D.C., 2025. Transitioning from Silence to Confidence: A Study of Non-Native English-Speaking Students in Higher Education. Journal of English Language and Education. 10(2), 419–440. DOI: https://doi.org/10.31004/jele.v10i2.795

[3] Zeng, Y., Kuo, L.-J., Chen, L., et al., 2025. Vocabulary Instruction for English Learners: A Systematic Review Connecting Theories, Research, and Practices. Education Sciences. 15(3), 262. DOI: https://doi.org/10.3390/educsci15030262

[4] Abdullah Alhebshi, A., Gamlo, N., 2022. The Effects of Mobile Game-Based Learning on Saudi EFL Foundation Year Students’ Vocabulary Acquisition. Arab World English Journal. 13(1), 408–425. DOI: https://doi.org/10.24093/awej/vol13no1.27

[5] Wan, C., Abdullah, A.N., Bolong, J., et al., 2025. Effect of Baicizhan Application on English Vocabulary Knowledge on Non-English Major University Students. Open Journal of Modern Linguistics. 15(02), 162–177. DOI: https://doi.org/10.4236/ojml.2025.152012

[6] Sweller, J., 2011. Cognitive Load Theory In: Mestre, J.P., Ross, B.H. (eds.). Psychology of Learning and Motivation. Elsevier: Cambridge, MA, USA. pp. 37–76. DOI: https://doi.org/10.1016/B978-0-12-387691-1.00002-8

[7] Rodrigues, L., Palomino, P.T., Toda, A.M., et al., 2024. How Personalization Affects Motivation in Gamified Review Assessments. International Journal of Artificial Intelligence in Education. 34(2), 147–184. DOI: https://doi.org/10.1007/s40593-022-00326-x

[8] Gao, Y., Pan, L., 2023. Learning English vocabulary through playing games: the gamification design of vocabulary learning applications and learner evaluations. The Language Learning Journal. 51(4), 451–471. DOI: https://doi.org/10.1080/09571736.2023.2217828

[9] Li, R., 2021. Does Game-Based Vocabulary Learning APP Influence Chinese EFL Learners’ Vocabulary Achievement, Motivation, and Self-Confidence? Sage Open. 11(1), 21582440211003092. DOI: https://doi.org/10.1177/21582440211003092

[10] Li, K.C., Wong, B.T.-M., 2021. Features and trends of personalised learning: a review of journal publications from 2001 to 2018. Interactive Learning Environments. 29(2), 182–195. DOI: https://doi.org/10.1080/10494820.2020.1811735

[11] Tetzlaff, L., Schmiedek, F., Brod, G., 2021. Developing Personalized Education: A Dynamic Framework. Educational Psychology Review. 33(3), 863–882. DOI: https://doi.org/10.1007/s10648-020-09570-w

[12] Sruthi, P., Mukherjee, S., 2020. Byju’s the learning app: An investigative study on the transformation from traditional learning to technology based personalized learning. International Journal of Scientific and Technology Research. 9(3), 5054–5059.

[13] Song, B., Xiong, D., 2023. A comparative study of the effects of social media and language learning apps on learners’ vocabulary performance. Asia Pacific Education Review. DOI: https://doi.org/10.1007/s12564-023-09871-z

[14] Soyupak, O., İpek, H., 2024. Investigation of the Usability and User Experience of Mobile Language Learning Applications: Busuu, Duolingo, and Memrise. Turkish Online Journal of Design Art and Communication. 14(4), 840–855. DOI: https://doi.org/10.7456/tojdac.1510008

[15] Xie, H., Zou, D., Zhang, R., et al., 2019. Personalized word learning for university students: a profile-based method for e-learning systems. Journal of Computing in Higher Education. 31(2), 273–289. DOI: https://doi.org/10.1007/s12528-019-09215-0

[16] Klímová, B., 2019. Mobile Application as Appropriate Support for the Retention of New English Words and Phrases in English-Language Learning. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.). Smart Education and E-Learning 2019, Smart Innovation, Systems and Technologies. Springer: Singapore. pp. 325–333. DOI: https://doi.org/10.1007/978-981-13-8260-4_30

[17] Sato, T., Murase, F., Burden, T., 2020. An Empirical Study on Vocabulary Recall and Learner Autonomy through Mobile?Assisted Language Learning in Blended Learning Settings. CALICO Journal. 37(3), 254–276. DOI: https://doi.org/10.1558/cj.40436

[18] Peng, H., Ma, S., Spector, J.M., 2019. Personalized Adaptive Learning: An Emerging Pedagogical Approach Enabled by a Smart Learning Environment. In: Chang, M., Popescu, E., Kinshuk, et al. (eds.). Foundations and Trends in Smart Learning, Lecture Notes in Educational Technology. Springer: Singapore. pp. 171–176. DOI: https://doi.org/10.1007/978-981-13-6908-7_24

[19] Zhang, R., Zou, D., Xie, H., 2022. Spaced repetition for authentic mobile-assisted word learning: nature, learner perceptions, and factors leading to positive perceptions. Computer Assisted Language Learning. 35(9), 2593–2626. DOI: https://doi.org/10.1080/09588221.2021.1888752

[20] Li, X., 2025. Spaced repetition as a basic structural method for organizing English as a second language teaching. Porta Linguarum An International Journal of Foreign Language Teaching and Learning. (44), 11–28. DOI: https://doi.org/10.30827/portalin.vi44.30170

[21] Castro-Alonso, J.C., De Koning, B.B., Fiorella, L., et al., 2021. Five Strategies for Optimizing Instructional Materials: Instructor- and Learner-Managed Cognitive Load. Educational Psychology Review. 33(4), 1379–1407. DOI: https://doi.org/10.1007/s10648-021-09606-9

[22] Bai, B., Wang, J., Zhou, H., 2022. An intervention study to improve primary school students’ self-regulated strategy use in English writing through e-learning in Hong Kong. Computer Assisted Language Learning. 35(9), 2265–2290. DOI: https://doi.org/10.1080/09588221.2020.1871030

[23] Zhong, L., 2022. Incorporating personalized learning in a role-playing game environment via SID model: a pilot study of impact on learning performance and cognitive load. Smart Learning Environments. 9(1), 36. DOI: https://doi.org/10.1186/s40561-022-00219-5

[24] Engward, H., Goldspink, S., Iancu, M., et al., 2022. Togetherness in Separation: Practical Considerations for Doing Remote Qualitative Interviews Ethically. International Journal of Qualitative Methods. 21, 16094069211073212. DOI: https://doi.org/10.1177/16094069211073212

[25] Bandura, A., 1977. Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review. 84(2), 191–215. DOI: https://doi.org/10.1037/0033-295X.84.2.191

[26] Ryan, R.M., Deci, E.L., 2000. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist. 55(1), 68–78. DOI: https://doi.org/10.1037/0003-066X.55.1.68

[27] Dornyei, Z., Henry, A., 2022. Accounting for long-term motivation and sustained motivated learning: Motivational currents, self-concordant vision, and persistence in language learning. In: Advances in Motivation Science. Elsevier: London, UK. pp. 89–134. DOI: https://doi.org/10.1016/bs.adms.2021.12.003

[28] Kato, T., Kishida, N., Umeyama, T., et al., 2020. A random extraction method with high market representation for online surveys. International Journal of Business Innovation and Research. 22(4), 569–584.

[29] Webber-Ritchey, K.J., Aquino, E., Ponder, T.N., et al., 2021. Recruitment Strategies to Optimize Participation by Diverse Populations. Nursing Science Quarterly. 34(3), 235–243. DOI: https://doi.org/10.1177/08943184211010471

[30] Van Zeeland, I., Van Den Broeck, W., Boonen, M., et al., 2021. Effects of digital mediation and familiarity in online video interviews between peers. Methodological Innovations. 14(3), 20597991211060743. DOI: https://doi.org/10.1177/20597991211060743

[31] Wakelin, K.J., McAra-Couper, J., Fleming, T., 2024. Using an Online Platform for Conducting Face-To-Face Interviews. International Journal of Qualitative Methods. 23, 16094069241234183. DOI: https://doi.org/10.1177/16094069241234183

[32] Moffa, G., Di Gregorio, M., 2023. Exploring the use of WeChat for qualitative social research: The case of Italian digital diaspora in Shanghai. Frontiers in Sociology. 8, 1144507. DOI: https://doi.org/10.3389/fsoc.2023.1144507

[33] Braun, V., Clarke, V., 2006. Using thematic analysis in psychology. Qualitative Research in Psychology. 3(2), 77–101. DOI: https://doi.org/10.1191/1478088706qp063oa

[34] Tu, Y., Zou, D., Zhang, R., 2020. A comprehensive framework for designing and evaluating vocabulary learning apps from multiple perspectives. International Journal of Mobile Learning and Organisation. 14(3), 370. DOI: https://doi.org/10.1504/IJMLO.2020.108199

[35] Zhang, J.-H., Zou, L., Miao, J., et al., 2020. An individualized intervention approach to improving university students’ learning performance and interactive behaviors in a blended learning environment. Interactive Learning Environments. 28(2), 231–245. DOI: https://doi.org/10.1080/10494820.2019.1636078

[36] Gm, D., Goudar, R.H., Kulkarni, A.A., et al., 2024. A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review. IEEE Access. 12, 34019–34041. DOI: https://doi.org/10.1109/ACCESS.2024.3369901

[37] Cavendish, B.A., De Lima, M.F.R., Perícoli, L., et al., 2022. Effects of combining retrieval practice and tDCS over long-term memory: A randomized controlled trial. Brain and Cognition. 156, 105807. DOI: https://doi.org/10.1016/j.bandc.2021.105807

[38] Song, C., Shin, S.-Y., Shin, K.-S., 2024. Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways. Applied Sciences. 14(2), 916. DOI: https://doi.org/10.3390/app14020916

[39] El-Sabagh, H.A., 2021. Adaptive e-learning environment based on learning styles and its impact on development students’ engagement. International Journal of Educational Technology in Higher Education. 18(1), 53. DOI: https://doi.org/10.1186/s41239-021-00289-4

[40] Kohnke, L., 2020. Exploring Learner Perception, Experience and Motivation of Using a Mobile App in L2 Vocabulary Acquisition: International Journal of Computer-Assisted Language Learning and Teaching. 10(1), 15–26. DOI: https://doi.org/10.4018/IJCALLT.2020010102

[41] Jensen, C.J.D., Cadierno, T., 2024. Differences in mobile-assisted acquisition of receptive and productive vocabulary knowledge: a case study using Mondly. The Language Learning Journal. 52(3), 255–270. DOI: https://doi.org/10.1080/09571736.2022.2108123

[42] Xu, Q., Richardson, J., Zhang, Z., et al., 2025. Using a Mobile Vocabulary Application to Enhance L2 Learners’ Vocabulary Acquisition: Possibilities and Challenges. Online Learning. 29(3). DOI: https://doi.org/10.24059/olj.v29i3.4918

[43] Kowang, T.O., Yew, L.K., Yen, H.W., et al., 2022. Relationship between teaching quality factors and employability among Technology Management students. International Journal of Evaluation and Research in Education (IJERE). 11(3), 1154. DOI: https://doi.org/10.11591/ijere.v11i3.21836

Downloads

How to Cite

Huang, M., Vijayaratnam, P., binti Hamdan, N., Shirzad, S., & Barjesteh, H. (2025). Enhancing Vocabulary Retention Through Personalized Learning: Evaluating the Impact of the "Baicizhan" App on students’ Long-Term Memory. Forum for Linguistic Studies, 7(12), 303–315. https://doi.org/10.30564/fls.v7i12.10348

Issue

Article Type

Article (This article belongs to the Topical Collection on "Affective Reactions and Foreign Language Anxieties: Focus on Debilitating Anxiety")