시선추적 데이터 기반 AI 실시간 온라인교육 맞춤형 피드백 시스템 설계 및 사용의향 평가 = Design of a Personalized AI-based Synchronous Online Education Feedback System using Eye Tracking Data and Evaluation of Intention to Use

다국어 초록 (Multilingual Abstract)

With the advancement of AI and big data, it has become possible to deliver personalized feedback to learners by extracting information about their learning process and performance. In a technology-enhanced learning, automated feedback provided to learners at the right time provides an opportunity to compare current performance with desired performance results. This helps learners to identify their learning situation and make maximum efforts to improve their learning immersion and participation. In addition, instructors can provide relevant feedback to learners when needed, saving time and effort required to input repetitive feedback. This study aims to develop a system that provides personalized feedback to promote learning participation, which ultimately leads to better learning outcomes during online learning. Specifically, it analyzes the learner's gaze data, such as gaze duration and eye blinks, to provide customized feedback that helps students immerse themselves in learning. Based on big data analysis of learner behavior data, synchronous learning prescriptions are provided to increase learning participation. In this study, we designed a dashboard for the system's learners and confirmed their intention to use it among actual users university students. As a result, 54.9% of the need was positive, 13.4% were neutral, and 31.7% were negative, and the intention to use was 68.3% positive, 10.7% neutral, and 21.0% negative. These results confirm expectations for future use and suggest implications for system development.

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