# A Serious Mixed-Reality Game for Training Police Officers in Tagging Crime Scenes
## 1 Introduction  
The goal of the process called _crime scene investigation_ is to discover the dynamics of a criminal event and to identify the relative perpetrators. The key principle behind this process is known as Locard's Exchange Principle. This principle declares that whenever someone enters or exits an environment, something physical is added to and removed from the scene [1]. Therefore, the main tasks in crime scene investigation consist of carefully documenting the conditions at a crime scene and recognizing all relevant physical evidence. In general, the collected traces can help to achieve two main goals: (1) linking suspects to the crime scene by recognizing evidence such as blood, bodily fluids, and fingerprints; (2) understanding the activities that occurred during the criminal act by detecting evidence such as a broken window or shoe prints. During an investigation, different theories are produced about who may have committed the crime and the dynamics of the event; the collection of traces allows the different hypotheses to be validated or refuted. Starting from this discussion, the key role of physical evidence in crime scene investigation is evident. Undoubtedly, negligence of the recognition and recovery of evidence leads to the validation of incorrect hypotheses (or, vice-versa, the refusal of the correct ones) and, as a consequence, results in wrongful outcomes being reached, such as the failure to convict a perpetrator or the conviction of the wrong person [2]. In order to avoid this, investigators might be tempted to collect each thing at a crime scene. However, in turn, this strategy could cause legal and investigative issues [3]. Indeed, if everything at a crime scene is collected and analyzed by a forensic laboratory, firstly, the forensic facility will be overwhelmed, valuable resources could be wasted, and, above all, the examination ofobjects unrelated to the criminal event may also provide _false leads_. Therefore, care should be taken by investigators to detect relevant physical evidence and to exclude superfluous traces [3]. This care is achieved when investigators understand how a crime scene search should be executed and routinely exercised [3]. In fact, the importance of experience and training at a crime scene was shown in recent studies [4; 5; 6; 7], where the performance of experts and novices was observed and compared. In particular, in [4], the experiments showed different performances between undergraduate forensic students and experienced investigators in applying evidence search strategies in two simulated burglary scenarios. More precisely, the experts finished the task more quickly and formed better narratives about the crime, suggesting that the experts could better judge the potential utility of evidence for subsequent analyses [4]. Therefore, relevant training programs in evidence-recovery techniques should be attended by novices to improve their skills. However, it is worth noting that transferring the knowledge gained in the classroom to the practical field is a challenging task. Typically, this challenge is solved by on-the-job training in which a new investigator is paired with a more experienced officer. The expectation is that, as the new investigator works on life cases, any mistakes will be identified by the more experienced colleague, who will either prevent or rectify the mistakes [8]. While on-the-job training is effective, it is also time-consuming and limited by the availability of sufficiently experienced officers. In order to contribute to relieving these issues, this paper introduces a serious mixed-reality game to support the training of novice police officers in evidence collection at crime scenes. Serious games are games in which the main purpose is not entertainment, but a serious purpose, such as interactive training. These games are emerging as a promising educational method for various domains, such as the military, politics, management, engineering, and healthcare.