ENHANCING LEXICAL COMPETENCE THROUGH THE ENRICHED VIRTUAL MODEL: A MULTIMODAL APPROACH TO DIVERSE LEARNING STYLES
Abstract
Vocabulary mastery plays a crucial role in successful language acquisition, and blended learning approaches have increasingly been adopted to enhance lexical development in higher education. However, existing studies have predominantly focused on general blended learning designs without fully exploring the specific contribution of the Enriched Virtual Model (EVM) in strengthening different dimensions of lexical competence. Moreover, limited research has examined how diverse learning preferences, such as those identified through the VARK framework, influence students’ engagement and vocabulary development within EVM-based instruction. Addressing these gaps, this study investigates the effectiveness of EVM in improving students’ lexical competence and examines how different learning styles contribute to learners’ engagement and vocabulary growth. Employing a mixed-methods design, the research integrates quantitative data from pre- and post-tests with qualitative insights obtained from reflections, observations, and open-ended responses. Participants were undergraduate students enrolled in a Lexical Studies course, who engaged in a combination of multimodal online modules and targeted face-to-face sessions throughout the intervention. Quantitative findings revealed substantial gains across all dimensions of lexical competence—vocabulary breadth, depth, collocational knowledge, and contextual use—with contextual vocabulary demonstrating the highest improvement. Differences were also observed across learning-style groups, with Visual and Read/Write learners showing the greatest lexical gains, while Auditory and Kinesthetic learners benefited most from modality-congruent strategies. Qualitative findings further indicated that EVM supported learner autonomy, enhanced multimodal engagement, and strengthened vocabulary retention through structured online learning and collaborative face-to-face consolidation. Overall, the results demonstrate that EVM provides an effective, flexible, and learner-responsive framework for vocabulary instruction in blended learning environments.
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DOI: http://dx.doi.org/10.29300/ling.v12i1.9570
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