An Automated Aesthetic Assessment Framework of Mathematical Story Images Validated by Click Counts

Abstract

Some online learning platforms frequently recommend educational materials to attract student engagement, with visual elements playing a critical role in capturing attention. To optimize the visual design of mathematical stories, this study examines the relationship between visual features and click frequency, based on log data from a U.S. platform featuring AI-generated mathematical stories for elementary students. Our methodology involves a multi-level visual feature extraction framework, categorizing features into low-, mid-, and high-level. Low-level features capture fundamental visual elements like color, texture, shape, and composition, commonly used for their simplicity. Mid-level features, inspired by psychological and artistic theories, more directly link to emotional impact, including attributes like brightness and contrast. High-level features focus on semantic content, using AI models to extract aesthetic scores and identify entities. Based on the correlation analysis between visual features and clicks, our findings indicate that images featuring characters and natural landscapes positively correlate with student interest, aligning with theories of situational interest. In contrast, images with pronounced brightness contrasts negatively impact engagement, likely due to increased cognitive load. The study highlights the limited influence of mid-level aesthetic features on elementary students’ engagement, emphasizing the importance of visual clarity and educational relevance over purely aesthetic considerations.

Materials

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Cite

@inproceedings{li2025an,
  author = {Li, Hai and Xing, Wanli and Lyu, Bailing and Zhu, Wangda and Liu, Zifeng and Li, Hongming},
  title = {An Automated Aesthetic Assessment Framework of Mathematical Story Images Validated by Click Counts},
  booktitle = {Proceedings of the Twelfth ACM Conference on Learning @ Scale},
  year = {2025},
  pages = {202--206},
  publisher = {ACM},
  doi = {10.1145/3698205.3733923},
  url = {https://doi.org/10.1145/3698205.3733923}
}