“Seamlessly Learn Vocabulary!”: A Mixed-Methods Exploration of CFL Learners’ Engagement and Lexical Development in a Mobile-Assisted Learning Environment

In the evolving landscape of educational ecology, informal learning environments offer valuable opportunities to bridge formal instruction with real-world experiences. The Mobile-Assisted Seamless Vocabulary Learning (MASVL) framework integrates mobile technology to support continuous and contextualized Chinese vocabulary acquisition. This study examines the effectiveness of MASVL in enhancing vocabulary development and learner engagement among tertiary-level students. A total of 32 students with diverse proficiency levels participated in a 16-week intervention using DingTalk, a mobile-based social media platform designed for seamless learning. Adopting a design-based research (DBR) methodology with a mixed-methods approach, the study collected both quantitative and qualitative data. Quantitative analysis of 1,921 learner-generated Chinese artifacts employed the Uber Index (U), a lexical diversity metric that calculates the ratio of unique word types to total tokens using a logarithmic formula. Results from a two-way repeated measures ANOVA showed significant effects of time and proficiency group ( p < .001). Lexical diversity increased by 42.2% overall, with high-performing learners improving by 52%, medium performers by 41%, and low performers by 33%. A one-sample t- test confirmed strong perceived learning success ( M = 3.97 out of five, p < .001). Qualitative findings from student reflections and interviews indicated that ease of use, perceived usefulness, and timely peer interaction enhanced motivation and learner autonomy. Some limitations related to platform editability were noted. Overall, MASVL proved to be an effective, learner-centered framework that supports vocabulary acquisition through real-life, mobile-supported interaction. The study offers implications for integrating seamless learning into second language instruction.