网页视觉复杂度影响学习型搜索效果,简约布局更利于知识获取。
Unraveling the Impact of Visual Complexity on Search as Learning
- 用多维度特征量化网页视觉复杂度与美学
- 简约版式页显著提升学习成效,图像数量不影响结果
- 适合教育类信息检索系统设计者参考
信息搜索已成为学习与知识获取的核心手段,提供广泛的信息与学习资源。网页的视觉复杂度已被证实影响搜索行为,以往研究指出用户在首秒内即做出评价判断。然而,现有研究尚缺乏对以学习为目的的搜索中视觉复杂度影响的深入理解,这制约了面向教育目标的优化信息检索(IR)系统的发展。为此,本文通过多种特征建模网页的视觉复杂度与美学,探究其与学习型网络搜索行为的关系。研究基于一项实验室实验的公开数据集,参与者学习雷暴形成知识。结果表明,内容相关性是知识增长的最重要预测因子,但视觉复杂度较低的页面会带来更高的学习成功率。该效应主要体现在页面布局特征上,而非图像数量等简单特征。研究结果揭示了视觉复杂度对学习型搜索的影响,为教育场景下更有效的信息检索系统设计提供了依据。为促进可复现性,代码已开源(https://github.com/TIBHannover/sal_visual_complexity)。
原文摘要 · Abstract (English)
Information search has become essential for learning and knowledge acquisition, offering broad access to information and learning resources. The visual complexity of web pages is known to influence search behavior, with previous work suggesting that searchers make evaluative judgments within the first second on a page. However, there is a significant gap in our understanding of how visual complexity impacts searches specifically conducted with a learning intent. This gap is particularly relevant for the development of optimized information retrieval (IR) systems that effectively support educational objectives. To address this research need, we model visual complexity and aesthetics via a diverse set of features, investigating their relationship with search behavior during learning-oriented web sessions. Our study utilizes a publicly available dataset from a lab study where participants learned about thunderstorm formation. Our findings reveal that while content relevance is the most significant predictor for knowledge gain, sessions with less visually complex pages are associated with higher learning success. This observation applies to features associated with the layout of web pages rather than to simpler features (e.g., number of images). The reported results shed light on the impact of visual complexity on learning-oriented searches, informing the design of more effective IR systems for educational contexts. To foster reproducibility, we release our source code (https://github.com/TIBHannover/sal_visual_complexity).
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