Learning style theories have been widely used in adaptive learning systems to enhance learning outcomes. However, the previous studies on adaptive learning systems set a high entry barrier for researchers who lack programming skills, and few of the studies involved authentic everyday learning materials. This author proposes to test the feasibility of eye-tracking technology in identifying learning styles with everyday materials, as well as the identification accuracy. This author selected the Felder-Silverman’s learning style model (FSLSM) as the framework, enlisted the behaviour patterns that can be used to identify the eight learning styles in the FSLSM model, and conducted a quasi-experiment to test whether these behaviour patterns apply to eye movement differences. Then, this author compared the results of eye-tracking identification with participants’ self-report based on Index of Learning Style (ILS) questionnaire for identification accuracy. This quasi-experiment recruited 30 university students, including 19 female and 11 male. Findings showed that eye-tracking technology has the potential to quickly identify learners of different types categorised by the FSLSM theory, with accuracy ranging from 63.50% to 84.67%; however, there are disturbing factors contributing to different levels of identification accuracy, which should be investigated in future research.

学习风格理论已广泛用于自适应学习系统中，以增强学习效果。但是，先前关于自适应学习系统的研究为缺乏编程技能的研究人员设置了较高的进入障碍，并且很少有研究涉及真实的日常学习材料。作者建议测试眼动追踪技术在识别日常材料学习风格中的可行性以及识别的准确性。作者选择了Felder-Silverman的学习风格模型（FSLSM）作为框架，列举了可用于识别FSLSM模型中的八种学习风格的行为模式，并进行了一项准实验以测试这些行为模式是否适用于眼动差异​​。然后，作者将眼动追踪识别的结果与参与者基于学习风格指数（ILS）问卷的自我报告的识别准确性进行了比较。这项准实验招募了30名大学生，其中19名女性和11名男性。研究结果表明，眼动追踪技术有潜力快速识别根据FSLSM理论分类的不同类型的学习者，其准确性范围为63.50％至84.67％；但是，有一些干扰因素会导致不同级别的识别准确性，应在以后的研究中对此进行调查。研究结果表明，眼动追踪技术有潜力快速识别根据FSLSM理论分类的不同类型的学习者，其准确性范围为63.50％至84.67％；但是，有一些干扰因素会导致不同级别的识别准确性，应在以后的研究中对此进行调查。研究结果表明，眼动追踪技术有潜力快速识别根据FSLSM理论分类的不同类型的学习者，其准确性范围为63.50％至84.67％；但是，有一些干扰因素会导致不同级别的识别准确性，应在以后的研究中对此进行调查。