ScholarWorks@숙명여자대학교

초록

This study examined the characteristics of latent giftedness exhibited by university students during a major-design-based learning process using AI-based Multimodal Learning Analytics (MMLA). The participants were 100 undergraduate students enrolled in a major design course within the flexible major program at University A located in the Seoul metropolitan area. Data sources included learning management system (LMS) log data, major exploration reports and reflective journals (text), and major roadmaps and visual artifacts (images) generated throughout the learning process. After quantifying each indicator, Latent Profile Analysis (LPA) was conducted. The findings revealed four distinct latent giftedness profiles based on learner characteristics, demonstrating that latent giftedness manifests in diverse patterns within a major-design-based learning environment.

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