Comparative Analysis of Mobile Applications and Traditional Methods in Vocabulary Acquisition
Comparative Analysis of Mobile Applications and Traditional Methods in Vocabulary Acquisition

Summary/Abstract: Background/purpose. This study provides a comparative review of mobile applications and traditional classroom methods in English vocabulary acquisition. It explores whether mobile-assisted vocabulary learning (MAVL) results in better retention, engagement, and productive vocabulary use among ESL learners. The study also aims to identify underlying theories supporting each method and assess learner preferences across diverse contexts. Materials/methods. A theoretical, narrative literature review was conducted by analyzing peer-reviewed articles published between 2018 and 2025. Studies were retrieved from databases such as Scopus, Web of Science, ERIC, and Google Scholar using keywords including "mobile learning," "ESL," and "vocabulary acquisition." The review applied strict inclusion/exclusion criteria, focusing on research comparing MAVL with traditional methods, and employed thematic synthesis of learner profiles, research design, outcomes, and theoretical frameworks such as spaced repetition, dual coding, and selfregulated learning. Results. Findings indicate that mobile applications often lead to superior vocabulary retention and learner engagement, largely due to multimedia features, spaced repetition, and gamification. However, traditional methods remain strong in developing productive vocabulary skills through contextual and teacher-mediated instruction. In several cases, a blended approach combining both methods yielded the most effective outcomes. Conclusion. Mobile applications offer significant advantages in ESL vocabulary acquisition, particularly in terms of retention, motivation, and learner autonomy. Yet, to fully develop productive skills and contextual understanding, these tools should be integrated into traditional classroom instruction. Future research should focus on longterm retention, productive language use, and adaptive hybrid models to optimize vocabulary learning outcomes.

• Details
• Contents