| Summary: | The Seamless Language Learning (SLL) model holds the potential for enriching second language (L2) learners’ vocabulary acquisition by focusing on their experiences and perceptions. However, prior research on SLL has suffered from methodological issues and poor understanding of the model. This study aims to address these issues by quantitatively assessing the impact of the SLL model on Chinese as a Foreign Language (CFL) learners’ vocabulary acquisition experience, their perceived use of the DingTalk platform, and vocabulary learning enhancement. To achieve these objectives, a design-based research (DBR) approach is employed, with data gathered from real-world seamless Chinese vocabulary learning activities involving 32 intermediate-level participants with no prior exposure to SLL. Data analysis involves a paired sample t-test. Pre-test and post-test assessments, using a five-point Likert scale, are conducted to gauge the effectiveness of the SLL model. The assessment tool, the Seamless Chinese Vocabulary Learning Questionnaire (SCVLQ), encompasses various dimensions such as participants’ emotional engagement, satisfaction, interaction with SLL, perceptions of the SLL platform (DingTalk), and vocabulary learning enhancement through the utilization of the SLL model. Empirical results from valid SCVLQ responses indicate significant post-test improvements compared to pre-tests, indicating enhanced learner experiences and perceptions. Moreover, the SLL model is found to positively contribute to vocabulary learning enhancement. These findings provide quantitative support, addressing the limitations of previous qualitative approaches, and confirming the efficacy of the SLL model in promoting active learning engagement, enhancing learners’ experiences with the DingTalk platform, and facilitating vocabulary acquisition. This study offers insights for future research to delve into additional factors influencing the SLL model’s effectiveness and to extend its application to diverse language learning contexts. © 2023 by the authors.
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