Unraveling the Impact of Visual Complexity on Search as Learning

• URL: http://arxiv.org/abs/2501.05289v1
• Date: Thu, 09 Jan 2025 14:51:58 GMT
• Title: Unraveling the Impact of Visual Complexity on Search as Learning
• Authors: Wolfgang Gritz, Anett Hoppe, Ralph Ewerth,
• Abstract summary: This study investigates the relationship between visual complexity and search behavior during learning-oriented web sessions. Our findings reveal that sessions with less visually complex pages are associated with higher learning success. The reported results shed light on the impact of visual complexity on learning-oriented searches, informing the design of more effective IR systems.
• Score: 7.706941074799756
• License:
• Abstract: 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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