Izmir Tunahan Gizem, Tuysuzoglu Goksu, Altamirano Hector
Department of Architecture, Dokuz Eylul University, Izmir, Türkiye.
Department of Computer Engineering, Dokuz Eylul University, Izmir, Türkiye.
Front Psychol. 2025 Jul 11;16:1642381. doi: 10.3389/fpsyg.2025.1642381. eCollection 2025.
This study presents a comprehensive, data-driven investigation into students' seating preferences within academic library environments, aiming to inform user-centered spatial design. Drawing on over 1.3 million ten-minute passive infrared (PIR) sensor observations collected throughout 2023 at the UCL Bartlett Library, we modeled seat-level occupancy using 24 spatial, environmental, and temporal features through advanced machine learning algorithms. Among the models tested, Categorical Boosting (CatBoost) demonstrated the highest predictive performance, achieving a classification accuracy of 72.5%, with interpretability enhanced through SHAP (Shapley Additive exPlanations) analysis. Findings reveal that seating behavior is shaped not by individual factors but by two dominant dimensions: (1) environmental controllability, including access to personal lighting and fresh air, and (2) distraction management, characterized by quiet surroundings, visual privacy, and low-stimulation workspace finishes. In contrast, features commonly presumed to be influential, such as desk width, fixed computer availability, or daylight alone, had minimal impact on seat choice. Despite extensive modeling and optimization, prediction accuracy plateaued at approximately 72%, reflecting the complexity and variability of human behavior in shared learning environments. By integrating long-term behavioral data with explainable machine learning, this study advances the evidence base for academic library design and offers actionable insights. These findings support design strategies that prioritize individual environmental control, as well as acoustic and visual privacy, offering actionable, evidence-based guidance for creating academic library environments that better support student comfort, focus, and engagement.
本研究对学术图书馆环境中学生的座位偏好进行了全面的数据驱动调查，旨在为以用户为中心的空间设计提供参考。利用2023年全年在伦敦大学学院巴特利特图书馆收集的超过130万次十分钟的被动红外（PIR）传感器观测数据，我们通过先进的机器学习算法，使用24个空间、环境和时间特征对座位占用情况进行了建模。在测试的模型中，分类提升（CatBoost）表现出最高的预测性能，分类准确率达到72.5%，通过SHAP（Shapley加性解释）分析增强了可解释性。研究结果表明，座位选择行为并非由个体因素决定，而是由两个主要维度塑造：（1）环境可控性，包括获得个人照明和新鲜空气的机会；（2）干扰管理，其特征是安静的环境、视觉隐私和低刺激的工作空间装饰。相比之下，通常被认为有影响的特征，如桌子宽度、固定电脑可用性或仅日光，对座位选择的影响最小。尽管进行了广泛的建模和优化，但预测准确率在约72%时趋于平稳，这反映了共享学习环境中人类行为的复杂性和变异性。通过将长期行为数据与可解释的机器学习相结合，本研究推进了学术图书馆设计的证据基础，并提供了可操作的见解。这些发现支持优先考虑个体环境控制以及声学和视觉隐私的设计策略，为创建能更好地支持学生舒适度、注意力和参与度的学术图书馆环境提供了可操作的、基于证据的指导。
