Experimental Dataset on Eye-tracking Activity During Self-Regulated Learning.

IF 6.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-06-09 DOI:10.1038/s41597-025-05304-1

Vojtěch Juřík, Libor Juhaňák, Alexandra Ružičková, Nicol Dostálová, Zuzana Juříková

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Abstract

The cognitive processing of learning materials has been extensively studied within various cognitive theories. Self-regulated learning (SRL) is also recognized as a key factor in learning efficiency. However, evidence linking SRL to learning outcomes remains inconclusive, particularly regarding objective behavioral data during learning. This study presents an original empirical dataset on eye-tracking activity during learning, examining the effects of metacognitive prompts and multimedia content on cognitive processing and learning outcomes. A controlled laboratory experiment with a 2 × 2 mixed factorial design involved 110 university students, resulting in 84 complete recordings of eye-movement activity during learning. Participants studied scientific materials in text-only and multimedia formats, with one group receiving metacognitive prompts and the control group receiving general instructions. Learning performance was assessed via a post-test, and eye-tracking technology captured gaze patterns to provide insights into cognitive engagement and attention distribution. Applications extend to e-learning, virtual environments, and user interface design. While the dataset has some methodological limitations, it remains a robust resource for studying cognitive processes and optimizing educational technologies.

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自我调节学习中眼动追踪活动的实验数据集。

学习材料的认知加工在各种认知理论中得到了广泛的研究。自我调节学习（SRL）也被认为是影响学习效率的关键因素。然而，将SRL与学习结果联系起来的证据仍然没有定论，特别是在学习过程中的客观行为数据方面。本研究提供了学习过程中眼球追踪活动的原始经验数据集，研究了元认知提示和多媒体内容对认知加工和学习结果的影响。一项采用2 × 2混合因子设计的对照实验室实验涉及110名大学生，产生了84份学习期间眼动活动的完整记录。参与者学习纯文本和多媒体格式的科学材料，其中一组接受元认知提示，对照组接受一般说明。学习表现通过后测进行评估，眼球追踪技术捕捉凝视模式，以提供对认知参与和注意力分配的见解。应用程序扩展到电子学习、虚拟环境和用户界面设计。虽然数据集有一些方法上的限制，但它仍然是研究认知过程和优化教育技术的强大资源。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.