WHERE MICROLEARNING MEETS LANGUAGE LEARNING AND COGNITIVE LOAD: A BIBLIOMETRIC AND CONTENT ANALYSIS

Lucia Ekawati Ikanubun

Abstract


Microlearning has expanded rapidly, yet its links with language learning and cognitive load theory remain unclear. Using a bibliometric analysis and content analysis, this study mapped microlearning and examined where microlearning, language learning, and cognitive load intersect. The corpus comprised 758 English-language journal articles, retrieved from Scopus on 19 july 2026, of which 113 (14.9%) addressed microlearning; data were analysed with Biblioshiny and VOSviewer, complemented by a content analysis of 31 microlearning studies in language learning. Publications grew from 3 articles in 2015 to 231 in 2025, an annual growth rate of about 54%. China, the United States, and Indonesia were the leading countries, and higher education, artificial intelligence, and microlearning were the most frequent keywords. Language learning terms were scattered across several clusters. Cognitive load emerged as one of the newest themes, yet only four articles combined all three themes, and educational psychology and CALL sources formed separate co-citation clusters. All 23 empirical studies relied on perceptions or immediate post-tests; six studies referred to cognitive load theory or CTML, but none of these journal articles reported a cognitive load measure or a delayed post-test. Theory-informed microlearning designs for language learning have been proposed but not yet tested for their effects on cognitive load and retention. Future research should test such designs, measure cognitive load and delayed retention, and apply design-based research in EFL contexts.

Keywords


Cognitive load; Language learning; Microlearning;Bibliometric analysis; Content analysis;

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References


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DOI: https://doi.org/10.17509/e.v25i3.106737

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