Unraveling the Impact of Visual Complexity on Search as Learning

2025-01-09

AI Summary

This study investigates how webpage visual complexity—particularly layout hierarchy—affects information retrieval behavior and knowledge acquisition during “Search as Learning” (SAL). Using laboratory learning session data centered on thunderstorm formation, we construct a multidimensional model of visual complexity and aesthetic features, integrating regression analysis, behavioral modeling, and validation on public datasets. Our key contribution is the first empirical identification that layout simplicity—not superficial metrics such as image count—significantly improves learning success, thereby bridging a critical gap in understanding the mechanistic link between visual design and educational outcomes. While content relevance remains the strongest predictor of knowledge gain, low layout complexity independently enhances learning efficacy. All code and datasets are publicly released to ensure reproducibility and facilitate future research in human-computer interaction and learning-centered web design.

Technology Category

• Search and Optimization: Learning to Search
• Data Mining & Knowledge Management: Knowledge Acquisition from the Web
• Machine Learning: Learning Preferences or Rankings

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Web data visualization
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

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).

Problem

• Visual Complexity
• Learning Process
• Webpage Complexity

Innovation

• Visual Complexity
• Learning Efficiency
• Web Design Optimization