ScholarWorks@숙명여자대학교

초록

This study aimed to analyze research trends and data characteristics in international studies conducted between 2022 and 2024 that measured competencies using multimodal learning analytics (MMLA). It retrieved 71 English-language journal articles and conference proceedings from the Scopus database using predefined criteria and conducted a systematic literature review. An analytical framework was used to code and categorize the selected studies. The analysis revealed that collaborative and self-regulated learning competencies were the most frequently studied topics. Most studies targeted university or graduate students, were conducted in experimental settings for research purposes, and aimed to clarify learning processes. In terms of data, discourse/language, self-report/assessment, and voice/audio data were most frequently employed to measure competencies, with usage varying based on the competency. Some studies used datasets integrating multiple modalities to capture competencies more comprehensively. Several analytical methods, including statistical and predictive modeling, were applied across the selected studies. Drawing on these findings, the study discusses the limitations of current MMLA-based competency research and suggests directions for future work.

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