Given maps of an evacuee population, shelter destinations and a transportation network, the goal of intelligent shelter allotment (ISA) is to assign routes, exits and shelters to evacuees for quick and safe evacuation. ISA is societally important due to emergency planning and response applications in context of hazards such as floods, terrorism, fire, etc. ISA is challenging due to conflicts between movements of evacueegroups heading to different shelters and transportation-network choke-points. State of the practice based on Nearest Exit or Shelter (NES) paradigm addresses the former challenge but not the latter one leading to load-imbalance and slow evacuation. Recent computational development, e.g., capacity-constrained route planning (CCRP), address the latter challenges to speedup evacuation, but do not separate evacuee groups going to different shelter destinations. To address these limitations, we propose a novel approach, namely, Crowd-separated Allocation of Routes, Exits and Shelters (CARES) based on the core idea of spatial anomaly avoidance. Experiments and Hajj case study (Makkah) show that CARES meets both challenges by providing much faster evacuation than NES and much lower evacuee-group movement-conflicts than CCRP.

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Traditional item analyses such as classical test theory (CTT) use exam‐taker responses to assessment items to approximate their difficulty and discrimination. The increased adoption by educational institutions of electronic assessment platforms (EAPs) provides new avenues for assessment analytics by capturing detailed logs of an exam‐taker's journey through their exam. This paper explores how logs created by EAPs can be employed alongside exam‐taker responses and CTT to gain deeper insights into exam items. In particular, we propose an approach for deriving features from exam logs for approximating item difficulty and discrimination based on exam‐taker behaviour during an exam. Items for which difficulty and discrimination differ significantly between CTT analysis and our approach are flagged through outlier detection for independent academic review. We demonstrate our approach by analysing de‐identified exam logs and responses to assessment items of 463 medical students enrolled in a first‐year biomedical sciences course. The analysis shows that the number of times an exam‐taker visits an item before selecting a final response is a strong indicator of an item's difficulty and discrimination. Scrutiny by the course instructor of the seven items identified as outliers suggests our log‐based analysis can provide insights beyond what is captured by traditional item analyses. What is already known about this topic Traditional item analysis is based on exam‐taker responses to the items using mathematical and statistical models from classical test theory (CTT). The difficulty and discrimination indices thus calculated can be used to determine the effectiveness of each item and consequently the reliability of the entire exam. What this paper adds Data extracted from exam logs can be used to identify exam‐taker behaviours which complement classical test theory in approximating the difficulty and discrimination of an item and identifying items that may require instructor review. Implications for practice and/or policy Identifying the behaviours of successful exam‐takers may allow us to develop effective exam‐taking strategies and personal recommendations for students. Analysing exam logs may also provide an additional tool for identifying struggling students and items in need of revision.