From motion to meaning: understanding students’ seating preferences in libraries through PIR-enabled machine learning and explainable AI
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 UC...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1642381/full |
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| author | Gizem Izmir Tunahan Goksu Tuysuzoglu Hector Altamirano |
| author_facet | Gizem Izmir Tunahan Goksu Tuysuzoglu Hector Altamirano |
| author_sort | Gizem Izmir Tunahan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5194e0dc51a542c895b052d05eba2172 |
| institution | DOAJ |
| issn | 1664-1078 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj-art-5194e0dc51a542c895b052d05eba21722025-08-20T02:40:40ZengFrontiers Media S.A.Frontiers in Psychology1664-10782025-07-011610.3389/fpsyg.2025.16423811642381From motion to meaning: understanding students’ seating preferences in libraries through PIR-enabled machine learning and explainable AIGizem Izmir Tunahan0Goksu Tuysuzoglu1Hector Altamirano2Department of Architecture, Dokuz Eylul University, Izmir, TürkiyeDepartment of Computer Engineering, Dokuz Eylul University, Izmir, TürkiyeUCL Institute for Environmental Design and Engineering, London, United KingdomThis 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.https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1642381/fullseat preferenceoccupancy monitoringacademic librarymachine learningexplainable AIspatial behavior |
| spellingShingle | Gizem Izmir Tunahan Goksu Tuysuzoglu Hector Altamirano From motion to meaning: understanding students’ seating preferences in libraries through PIR-enabled machine learning and explainable AI Frontiers in Psychology seat preference occupancy monitoring academic library machine learning explainable AI spatial behavior |
| title | From motion to meaning: understanding students’ seating preferences in libraries through PIR-enabled machine learning and explainable AI |
| title_full | From motion to meaning: understanding students’ seating preferences in libraries through PIR-enabled machine learning and explainable AI |
| title_fullStr | From motion to meaning: understanding students’ seating preferences in libraries through PIR-enabled machine learning and explainable AI |
| title_full_unstemmed | From motion to meaning: understanding students’ seating preferences in libraries through PIR-enabled machine learning and explainable AI |
| title_short | From motion to meaning: understanding students’ seating preferences in libraries through PIR-enabled machine learning and explainable AI |
| title_sort | from motion to meaning understanding students seating preferences in libraries through pir enabled machine learning and explainable ai |
| topic | seat preference occupancy monitoring academic library machine learning explainable AI spatial behavior |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1642381/full |
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