Unraveling the Impact of Visual Complexity on Search as Learning

TL;DR

This study investigates how visual complexity (VisCom) of web pages influences learning during SAL using the SaL-Lightning thunderstorm dataset. VisCom is modeled via HTML, visual, layout, and aesthetics features, and learning outcome is framed as a three-class knowledge gain (KG) problem using $KG = \frac{|post|-|pre|}{N}$. Results show content relevance as the strongest predictor of KG; however, the aesthetics subset and the combination of VisCom with WebRel improve robustness, offering modest gains beyond a baseline. The findings suggest that page layout and design influence learning success and could inform educational IR design, though validation on broader topics is needed; the authors share their code for reproducibility at the provided GitHub URL.

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).

Figures (1)

• Figure 1: Permutation feature importance analysis for k-Nearest Neighbors, showing mean decrease in accuracy (black) and standard deviation (red) across cross-validation iterations. The selection frequency of each VisCom feature is noted in brackets. Web page relevance features are omitted for clarity.