What is it about?
Universities have long-faced a significant problem with high dropout rates, often attributed to students' lack of experience and knowledge in navigating their individual learning paths across different courses. To address this issue, adaptive learning management systems have emerged as effective solutions. These systems classify learners' learning styles through questionnaires or computationally intensive algorithms and provide tailored learning paths accordingly. This paper proposes a study design that utilizes eye tracking to classify students' learning styles instead of using outdated methods such as questionnaires or the like for classification.
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Why is it important?
Knowing a person's learning style can enable personalized learning experiences. By identifying how learners process information visually, teachers can tailor instructional strategies, materials and activities to the preferred learning style. This adaptation can improve learning outcomes and engagement.
Perspectives
Eye tracking provides objective data on learners' visual attention and gaze patterns and captures real-time information about how they interact with the learning material. In contrast, questionnaires are based on self-assessments, which can be subjective and influenced by various factors such as personal bias or limited self-awareness. Therefore, it seems very obvious that we should investigate more deeply the identification of learning styles based on eye tracking and test its applicability
Dominik Bittner
Ostbayerische Technische Hochschule Regensburg
Read the Original
This page is a summary of: Towards Eye Tracking based Learning Style Identification, June 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3593663.3593680.
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