This study explored how a mobile app, Shanbay Dan-Ci, supports English vocabulary learning among 70 Chinese undergraduates. Although users outperformed peers using traditional learning methods, results weren't statistically significant. Learners valued mobile app affordance like personalization, rich content, and collaboration. Implications for mobile learning and future research are discussed.

1. Introduction
Mobile-assisted language learning (MALL) offers promising opportunities for flexible, learner-centered second language (L2) instruction. Vocabulary acquisition is foundational for L2 proficiency (Nation, 2022), but traditional methods often fail to meet the needs of learners. This study investigates the effectiveness of a popular Chinese mobile app, Shanbay Dan-Ci (SBDC), in supporting explicit English vocabulary learning among Chinese undergraduate learners. We also examine learners’ experiences and perceptions through the lens of the iPAC framework (Kearney et al., 2020), focusing on personalization, authenticity, and collaboration.

Explicit vocabulary learning, supported by repetition, self-regulation, and meaning-form linkage, is critical for L2 acquisition (Schmitt, 2010; Nation, 2022). MALL tools, particularly vocabulary-focused apps, can enhance learning outcomes by providing personalized review plans, spaced repetition, and gamified feedback (Elaish et al., 2019; Kohnke, 2020).

However, research findings on the effectiveness of mobile learning remain mixed. While some studies report improved performance (Chen et al., 2019; Li & Hafner, 2022), others reveal limited or statistically insignificant gains (Stockwell, 2007, 2010). This study builds on this body of work by examining both outcomes and perceptions over a 10-week period in informal learning contexts. Research questions of this study are:

1.     Does using the MAVL app SBDC improve L2 learners’ vocabulary form-meaning linkage (VFML) knowledge compared to traditional paper wordlists?

2.     What are L2 learners’ perceived learning experiences using SBDC?

2. Theoretical Framework
Grounded in sociocultural theory, the iPAC mobile learning framework (Kearney et al., 2020) emphasizes three core dimensions: personalization (learner agency and customization), authenticity (context and task relevance), and collaboration (social interaction and co-creation). This study applies the iPAC framework to explore how SBDC supports or limits these mobile learning dimensions.

3. Methods
A 10-week explanatory sequential mixed methods design was employed. Seventy Chinese undergraduate students preparing for the CET 6 exam were divided into an experimental group using SBDC (n=32) and a control group using paper-based wordlists (n=38). Pre- and post-tests measured VFML knowledge, while qualitative data were collected via open-ended surveys (N=32) and follow-up interviews (N=8) from the experimental group. Pre/post-tests assessed VFML knowledge using a cluster matching format (Webb et al., 2017). ANCOVA controlled for pre-test scores when analyzing post-test performance. Qualitative data were analyzed using NVivo, with coding aligned to the iPAC framework. Survey and interview data were triangulated for validity.

4. Results
4.1 RQ1: The effects of using SBDC on L2 learners’ vocabulary form-meaning linkage (VFML) performance

The experimental group outperformed the control group in post-test VFML scores (M = 90.42% vs. 89.12%), despite starting lower. However, the improvement was not statistically significant (F(1, 67) = 1.02, p = .315).

4.2 RQ2: L2 Learners’ Perceived Learning Experiences Using the SBDC App

4.2.1 Theme 1: Personalized Learning within SBDC

Participants appreciated the app’s personalized review plans, which used spaced repetition and learner input to tailor vocabulary practice. The review feature automatically tracked unfamiliar words and prompted repeated exposure, supporting individualized learning. Learners described this feature as essential for reinforcing form-meaning connections. For example, one student explained that the app "forced me to review what I thought I knew but didn’t," increasing retention through corrective feedback loops.

Goal-setting was another popular feature. SBDC allowed learners to define their daily learning targets and adjust their workload based on personal schedules. Some learners set ambitious goals initially, then revised them to maintain a sustainable pace. Progress dashboards and visual indicators of mastery motivated learners by making their achievements visible.

Mobile flexibility also enabled learners to study across various settings, such as dorms, cafeterias, and public transit, demonstrating the app's capacity to integrate learning into daily routines. Several learners emphasized that this microlearning format allowed them to make use of idle time and promoted habit formation.

4.2.2 Theme 2: Rich Content and Multimedia Support

Learners found the app’s multimedia elements, such as audio pronunciation, bilingual translations, and contextualized example sentences, particularly helpful. These resources supported auditory and contextual learning, which many participants said was missing from traditional wordlists. One student noted, “I remember the words better when I hear them and see how they’re used in a sentence.”

Participants valued example sentences not only for helping with meaning inference but also for offering limited but useful exposure to grammar and usage patterns. Several learners said the integration of these features helped them feel more immersed in the learning process, even when studying independently.

4.2.3 Theme 3: Collaborative Learning through Badge Sharing

The collaborative component of SBDC was primarily driven by digital badge sharing within social media groups (e.g., QQ). This simple feature emerged as a powerful motivator. Learners said seeing their peers share daily progress pushed them to stay accountable and maintain consistency in their own learning.

In particular, some students mentioned that earning and sharing “spark” badges, which rewarded sustained participation, created a gamified environment that encouraged ongoing effort. One learner reflected, “I didn’t want to lose my streak, so I kept learning every day, even when I didn’t feel like it.”

4.2.4 Theme 4: Challenges of Learning in a Mobile Environment

Despite the benefits, learners identified several limitations. First, while receptive knowledge (form-meaning linkage) improved, the app did not adequately support productive vocabulary use. Learners reported difficulty applying new words in writing or speaking contexts due to the app’s narrow instructional scope. As one participant said, “I know the meaning when I see the word, but I’m not sure how to use it in a sentence.”

In addition, distractions, both digital (notifications, multitasking) and environmental (noise, lack of structured space), were frequent obstacles. Some learners indicated they lacked a consistent learning routine, often studying “on the go,” which sometimes reduced focus and retention.

5. Discussion
The non-significant gains align with prior research suggesting that short-term mobile interventions may not yield statistically significant vocabulary growth (Burston, 2015). Nonetheless, students showed improved VFML performance and strong engagement, especially with features supporting metacognitive awareness.

Findings support the personalization dimension of iPAC, with learners benefiting from goal setting and individualized review. The collaboration element was partially met through badge sharing, though deeper peer interaction was limited. The authenticity dimension was less evident, as students noted the app focused more on receptive than productive skills, echoing critiques in prior MAVL research (Mahdi, 2018; Bahari, 2022).

6. Limitations
The 10-week intervention may have been too short to show robust vocabulary gains. The study focused primarily on form-meaning linkage, excluding productive vocabulary use. Finally, control group participants may have accessed SBDC informally, potentially confounding results.

7. Conclusion
SBDC supported flexible, self-regulated learning and provided personalized feedback that helped learners persist in vocabulary acquisition. While short-term outcome differences were not statistically significant, learners’ positive perceptions of personalization and collaboration suggest promise. Future research should extend intervention durations, examine productive vocabulary development, and integrate more authentic, communicative tasks into MAVL tools.

8. References

Bahari, A. (2022). Affordances and challenges of teaching language skills by virtual reality: A systematic review (2010–2020). E-Learning and Digital Media, 19(2), 163–188. https://doi.org/10.1177/20427530211036583

Burston, J. (2015). Twenty years of MALL project implementation: A meta-analysis of learning outcomes. ReCALL, 27(1), 4–20. https://doi.org/10.1017/S0958344014000159

Castañeda, D. A., & Cho, M. H. (2016). Use of a game-like application on a mobile device to improve accuracy in conjugating Spanish verbs. Computer Assisted Language Learning, 29(7), 1195–1204. https://doi.org/10.1080/09588221.2016.1197950

Chen, C. M., Chen, L. C., & Yang, S. M. (2019). An English vocabulary learning app with self-regulated learning mechanism to improve learning performance and motivation. Computer Assisted Language Learning, 32(3), 237–260.

Elaish, M. M., Ghani, N. A., Shuib, L., & Al-Haiqi, A. (2019). Development of a mobile game application to boost students’ motivation in learning English vocabulary. IEEE Access, 7, 13326–13337. https://doi.org/10.1109/ACCESS.2019.2891504

Kearney, M., Burden, K., & Schuck, S. (2020). Theorising and implementing mobile learning. Springer.

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. https://doi.org/10.4018/IJCALLT.2020010102

Li, Y., & Hafner, C. A. (2022). Mobile-assisted vocabulary learning: Investigating receptive and productive vocabulary knowledge of Chinese EFL learners. ReCALL, 34(1), 66–80. https://doi.org/10.1017/S0958344021000260

Mahdi, H. S. (2018). Effectiveness of mobile devices on vocabulary learning: A meta-analysis. Journal of Educational Computing Research, 56(1), 134–154. https://doi.org/10.1177/0735633117698826

Nation, P. (2022). Learning vocabulary in another language (3rd ed.). Cambridge University Press.

Schmitt, N. (2010). Key issues in teaching and learning vocabulary. In R. Chacon-Beltran, C. Abello-Contesse & M. M. Torreblanca-Lopez (Eds.), Insights into non-native vocabulary teaching and learning (pp. 28–40). Multilingual Matters.

Schmitt, N., & Schmitt, D. (2020). Vocabulary in language teaching (2nd ed.). Cambridge University Press.

Stockwell, G. (2007). Vocabulary on the move: Investigating an intelligent mobile phone-based vocabulary tutor. Computer Assisted Language Learning, 20(4), 365–383.

Stockwell, G. (2010). Using mobile phones for vocabulary activities: Examining the effect of platform. Language Learning & Technology, 14(2), 95–110.

Webb, S., Sasao, Y., & Ballance, O. (2017). The updated Vocabulary Levels Test: Developing and validating two new forms of the VLT. ITL-International Journal of Applied Linguistics, 168(1), 33–69. https://doi.org/10.1075/itl.168.1.02web

Qian Xu

Mrs at Purdue University-Main Campus

Zui Cheng

Dr. at Community Portal Self Registration Account

Zhuo Zhang

Dr at Towson University

Jennifer Richardson

Professor at Purdue University-Main Campus