A Serious Mixed-Reality Game for Training Police Officers in Tagging Crime Scenes

Abstract

Recognizing and collecting evidence at a crime scene are essential tasks for gathering information about perpetrators and/or the dynamics of a criminal event. Hence, the success of a crime investigation is strongly based on the ability of forensic investigators to perform these tasks. Recent studies observing and comparing the performance of experts and novices have highlighted the importance of experience and training for search and recovery strategies at crime scenes. Therefore, relevant training programs in evidence-recovery techniques should be attended by novices to improve their skills. However, the knowledge transfer between skills acquired in the classroom and their practical application in the field is a challenging task. In order to relieve this problem, this paper proposes a serious mixed-reality game, which is called TraceGame, aiming to support the training activities of novice forensic investigators by improving their skills related to the search and recovery of evidence at crime scenes. The purpose of the game is to identify the greatest number of useful traces present in a crime scene that is physically reconstructed at the training site as quickly as possible. As shown in an experimental session, TraceGame is a promising tool for supporting the training of novice forensic investigators.

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 of objects 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. Among the benefits of serious games, there is an increased level of interactivity of the application, adaptation to the user’s level of competence, repeatability, and continuous feedback. Moreover, serious games exploit the power of competition and create a sense of achievement to motivate participants to improve their own abilities. Serious games can be supported by different technologies. The main ones are those included in the umbrella term “extended reality” (XR): (i) virtual reality (VR) immerses users in a fully artificial digital world; (ii) augmented reality (AR) enhances the physical world with digital elements, typically by using the camera on a smartphone; (iii) mixed reality (MR) combines aspects of VR and AR by enabling interactions with virtual objects in the real world. Our proposal is based on MR, since this allows interaction with digital items, unlike in AR, and, at the same time, it keeps in touch with the physical world, unlike in VR. In addition, in the literature, several proposals have shown the benefits of using mixed reality [9,10,11]. In detail, the proposed serious mixed-reality game, which is called TraceGame, allows police officers to visualize and manipulate digital objects representing common tags used to report traces at crime scenes. The purpose of the game is to identify the greatest number of useful traces present at a crime scene that has been physically reconstructed at the training site as quickly as possible. At the end of the game, the police officers’ performance is calculated and represented by a score. TraceGame allows the storage of the scores in all the game sessions in order to monitor police officers’ improvements.

The proposed serious mixed-reality game was tested in a physically reconstructed crime scene in which evidence related to a simulated criminal event was placed. The evaluation of the proposed system was carried out by using methods to assess the usability, the engagement of the users, and the ability to enhance novices’ forensic capabilities. According to the experimental results, TraceGame is a promising and innovative tool to be used to train novice police officers.

The remainder of this manuscript is structured as follows. Section 2 describes the state of the art related to serious mixed-reality games applied in the forensic domain. Section 3 gives a detailed description of the materials and methods used to design the proposed system, as well as the architecture and functionalities of TraceGame. Section 4 presents the configuration of the experimental session, whereas Section 5 reports the results of the evaluation of TraceGame. Finally, in Section 6, conclusions and future work are discussed.

2. Related Works

Thanks to their focus on realism and experiential learning, serious games are emerging as powerful tools for knowledge management processes within law enforcement agencies (LEAs), whereby knowledge gained during a game can be applied in an actual operational environment and, as a consequence, facilitate the transition between classroom learning and on-the-job training [8]. In the literature, it is possible to find several serious games that have been used to meet LEAs’ needs and requirements. In particular, in [12], a serious game called PROACTIVE was proposed to empower the prediction of potential terrorist actions. In detail, the game allowed for two teams—law enforcement agencies and insurgents—to play against each other. The purpose of the game depended on the role of the user; if the user was an insurgent, the goal was to successfully complete an attack against a specific location, whereas if the user was a member of the law enforcement agency team, the goal was to prevent the attack. Although this sort of serious game can prepare officers for well-planned terrorist attacks, transferring the game into a virtual environment would allow for more realistic encounters [8].

For this reason, several serious games are based on virtual reality (VR) technology. In particular, in [13], a serious game using a VR environment was proposed for traffic accident investigators. In detail, the main goal of the proposed serious game was to search for and mark clues at an accident scene and to take photos and measurements to allow the reconstruction of the accident scene. In the end, the abilities of the traffic accident investigators were measured by using a marking scheme that weighed the various actions to be performed differently. In addition, in [14], a serious game based on VR was proposed to support police officers’ training in investigative activities. In detail, the scenario implemented in this serious game involved the murder of an old female and eight possible suspects. As a consequence, eight suspected murderous weapons (one for each suspect) were hidden in eight rooms of the house in which the murder took place. The goal of the police officer (game user) was to find the possible murder weapons and analyze them in a virtual forensic laboratory, where procedures such as the matching of fingerprints could be performed. At the end of the game, the police officer had to reveal the murderer and the corresponding murder weapon. Hence, the serious game allowed police officers to perform forensic investigations by virtually replicating the processes used by forensic departments of police authorities. In [15], instead, a serious game characterized by virtual suspects was realized to support training in investigative interviewing. The game was developed by taking opinions of criminologists, psychologists, and police departments into account to create multiple scenarios based on real cases and to provide a range of personalities as interview partners. In addition, Pringle et al. [16] focused on developing a VR forensic geoscience educational eGame loosely based on a closed forensic United Kingdom search case that the authors undertook. In detail, the game involved the four stages of a terrestrial forensic search: (1) a desk study to gain background case and site information, (2) a staged site investigation, including reconnaissance, (3) full site surveys, and (4) the excavation of prioritized suspected burial positions identified from the full site surveys. Finally, in [17], a virtual crime scene application was designed according to schematics for a room in the authors’ institution’s dedicated CSI ‘house’, and several pieces of ‘evidence’ were planted around the area. The users were then instructed to process the crime scene and, after 20 min, were asked to make notes on what evidence they found and what their hypotheses on the crime were.

As demonstrated in all studies related to the cited works, the exploitation of a serious game by using VR results was suitable for training by improving the police officers’ skills. However, the exploitation of VR involves the development of serious games characterized by virtual scenarios that are pre-defined and implemented. In other words, a serious game provides more possible scenarios if these are included when the serious game is realized. Hence, it is not possible for coach investigators to customize the scenarios. Moreover, it is known that exposure to VR can disrupt the sensory system and lead to symptoms such as nausea, dizziness, sweating, pallor, and loss of balance. These symptoms are encapsulated under the umbrella term virtual reality sickness [18] and, in sensitive individuals, may appear within the first few minutes of use.

These considerations led, in this work, to the choice of implementing a serious game based on mixed reality (MR). Indeed, experiments using Microsoft HoloLens, the main headset for MR, have shown that this device causes only negligible symptoms of simulator sickness in all participants [19]. Moreover, thanks to the fact that MR combines virtual objects with the physical world, the proposed serious game allows coach investigators to customize scenarios, since these are reconstructed in the physical world. In the literature, some serious MR games have already been introduced. For example, in [20], a novel framework based on MR and gamification for learning music and piano was proposed. In more detail, visual metaphors support students in understanding the traditional notation system while playing a certain instrument (in the physical world), and gamification principles, such as progression levels, awards, or challenges, are exploited to keep the student’s engagement. However, to the best of our knowledge, until now, no MR serious games have been developed for law enforcement agencies. In this, the great innovation of our proposal can be found.

3. Materials and Methods

This section is devoted to describing the proposed serious mixed-reality game for tagging evidence at a crime scene after the introduction of the basic concepts involved in its development.

3.1. Basic Concepts of Serious Games

Over the years, several definitions have been used to describe serious games (SGs). Despite the different origins of each definition, the core meaning behind them is that SGs are games that can be used for reasons other than mere entertainment [21]. In particular, SGs have often been applied in the education field, where studies have proved that SGs really do change learning outcomes [22]. Despite the great interest in educational SGs, the processes that lead to effective design still remain unclear [21]. However, the majority of SGs tend to have the following elements that ensure that they work properly and are effective for education and training purposes:

• Narrative represents the plot or the main story of an SG. As discussed in [23], a narrative is essential for fostering greater immersion, engagement, motivation, and learning. In particular, studies have shown that the existence of a narrative is associated with greater and more positive learning outcomes [23].
• Game dynamics and design include everything that is based on gamification, such as rankings, status, rewards, badges, or points, which tend to animate and motivate players throughout a game. The design structure and mechanics of the game should have an adequate relationship with the narrative.
• Feedback represents the punishment or reward that a player instantly receives during a game. The purpose of feedback is to direct learners to improve their performance, motivation, or learning outcomes through various methods of providing information to learners about the correctness of their responses [24,25]. Providing feedback allows a learner to evaluate their progress and responses, identify knowledge gaps, and repair faulty knowledge [24,26].

These features will be taken into account in the design of the proposed serious mixed-reality game.

3.2. Mixed Reality: Software and Hardware Aspects

Mixed reality (MR) refers to technology that enables a set-up of environments where digital and real objects coexist and are able to interact with each other in real time. More precisely, digital objects are known as holograms, i.e., objects made of light and sound that look like digital images but support naturalized interactions, as physical objects do. It is possible to live MR experiences thanks to head-mounted displays that allow the embedding of virtual objects into the real surroundings. In particular, the proposed serious mixed-reality game was designed to be an application for HoloLens by Microsoft Corp, which is the world’s first untethered MR head-mounted display (HMD) system, and it was released to developers in March 2016 as a development kit [27]. To create holographic objects in the real world with a high degree of realism, the HoloLens headset has see-through holographic lenses that use an advanced optical projection system to generate multi-dimensional full-color holograms with very low latency [11]. In more detail, the complete equipment of HoloLens includes two optical sensors on each side for peripheral “environment understanding” sensing, a main downward-facing depth camera to pick up hand motions, and specialized speakers that simulate sound from anywhere in the room. The HoloLens also has several microphones, an HD camera, an ambient light sensor, and Microsoft’s powerful custom “Holographic Processing Unit”. In addition to its advanced hardware, HoloLens uses artificial intelligence to provide an experience that is truly engaging through data that are analyzed locally where they are generated, thus minimizing the latency timeframes. Briefly, by using Microsoft HoloLens, it is possible to edit holographic objects with the hands, perform pointing with the gaze, and recognize objects with deep learning. In brief, the set of gestures to be used to edit holograms are:

• bloom, which is specifically used to start the main menu in HoloLens (first generation);
• air-tap, which is almost the same as a mouse click, i.e., by using an air tap, one can select a hologram;
• hold and drag, which is used to move, rotate, or scale holograms.

These gestures will be used to perform interactions with holograms in the proposed serious MR game. As for the creation of the custom holograms introduced in TraceGame, it is necessary to use a software platform that is specific for building 3D objects in a virtual world. As recommended by Microsoft [28], the software named Unity 3D was used for our framework. Unity 3D is a powerful game engine that has been extended to support the development of HoloLens applications. Indeed, the standard Unity toolchain and pipeline have been updated to incorporate support for HoloLens functions, such as gaze, gesture, and voice input, as well as spatial mapping, spatial audio, and the ability to anchor holographic objects to specific locations in the real world [28]. More precisely, Direct3D is the API used with Unity for HoloLens application development.

3.3. System Overview

TraceGame is a serious mixed-reality game for supporting the training activities of novice forensic investigators. In particular, TraceGame is devoted to improving the investigators’ skills related to the search for and recovery of evidence at crime scenes. The goal of the serious game that was realized is to identify the greatest number of useful traces present in a crime scene that has been physically reconstructed in the training site as quickly as possible. During the game, feedback about the selected traces are given to the player in order to provide information on when they have made the correct choice. At the end of the game, a score is computed to evaluate the performance of the whole police office. Therefore, TraceGame perfectly matches the features for definition as a serious game. Indeed, it is characterized by (1) a narrative related to the specific scenario of a crime scene, (2) game dynamics that are represented by the final score, which is computed by considering the number of traces identified and their importance, and (3) feedback, which is represented by positive and informative messages that are visualized as virtual objects when the forensic investigator identifies correct evidence.

From an architectural point of view, TraceGame is an application for the Microsoft HoloLens device, and it was implemented from scratch (see Figure 1). It is composed of three main modules: (i) a scenario creation module that implements the facilities for adding placeholders where evidence is to be recognized; (ii) a game module that implements the facilities for tagging evidence at the crime scene; (iii) an evaluation module that implements the facilities for computing and storing the performance of players.

From the point of view of the user, they can interact with TraceGame when run on Microsoft HoloLens through multi-channel interactions, which include gesture, voice, and eye tracking. TraceGame users can be of two types: experienced or novice investigators. For this reason, TraceGame provides two user modes: (i) scenario creation, which is accessible by expert forensic investigators, whose task is to select the points in the physically reconstructed crime scene that should be identified as traces by novices; (ii) the game session, which accessible by novice forensic investigators to play the serious game and obtain a score for the abilities shown in the game session. It is possible to select the desired mode by using the Start menu of the TraceGame application (see Figure 2). It is worth noting that the labels in TraceGame are in Italian to make the tool easy to use for those who will have to test it. Hereafter, more details about these two modes are given.

3.3.1. User Mode: Scenario Creation

At a physically reconstructed crime scene, an expert forensic investigator has to select the traces that are useful for the investigation activities and that should be correctly identified by the novice forensic investigators who will play the serious game. After that, the expert forensic investigator selects the scenario creation mode from the Start menu by using Microsoft HoloLens gestures. TraceGame provides a functionality that is denoted as Generate evidence placeholder. By using this functionality, expert forensic investigators place a hologram whose the graphical aspect is represented by a green spherical area where a useful trace should be identified (see Figure 3). The green sphere can be resized to be smaller or greater to make the discovery of the corresponding trace harder or easier, respectively. The expert forensic investigator performs this procedure for all useful traces to be found. Figure 4 reports the activity diagram for the scenario creation mode.

3.3.2. User Mode: Game Session

Once a novice forensic investigator is at the reconstructed crime scene, they have to start the serious game by selecting the game session mode from the Start menu by using Microsoft HoloLens gestures (see Figure 2). TraceGame provides two functionalities, which are denoted as Create tag and Terminate. By using the first functionality, the novice forensic investigators place a hologram whose the graphical aspect is represented by a typical crime scene tag where they think there is a useful trace (see Figure 5). The novice forensic investigators perform this procedure for all useful traces that they think should be identified. When they think that there are no more traces at the crime scene, they select the Terminate functionality to finish the game. At this point, TraceGame shows the score of the game session to the forensic investigator. This score is based on the number of traces identified. However, TraceGame also shows the duration of the game session that was terminated by the same novice forensic investigator. Figure 6 reports the activity diagram for the game session mode.

4. Evaluation

This section is devoted to describing the experimental session that was carried out to evaluate TraceGame after discussing possible use-case scenarios and the evaluation methods used.

4.1. Use-Case Scenarios

Today, it is vital to provide training courses for novice forensic investigators and to enable exercises in the different scenarios that can be found at crime scenes. According to [29], different features can be used to classify a crime scene. Some of them are:

• the original location at which the crime was committed, that is, the primary scene or secondary scene. In more detail, the term “primary scene” is used to refer to where the original criminal event happened, and any subsequent scene is labeled as a secondary scene;
• the size of the crime scene, that is, a macroscopic or microscopic scene. More precisely, a criminal investigation involving only the analysis of observable items, such as the body of the victim, can be referred to as an investigation in a macroscopic crime scene; conversely, a criminal investigation involving the collection and analysis of unobservable items, such as DNA, biological cells, and so on, can be referred to as an investigation in a microscopic crime scene.
• the appearance of the crime scene, that is, an organized or disorganized crime scene. Specifically, a crime scene can be defined as organized when the environment in which the violent act occurred is clean and it does not show any signs of struggle. Conversely, a disorganized crime scene is characterized by the presence of several pieces of evidence of struggle and a collection of objects that are chaotically distributed around the environment.

These features should be taken into account to create different scenarios of physically reconstructed crime scenes in which the training activities of novice forensic investigators will be performed.

4.2. Evaluation Methods

The testing and evaluation of the proposed system were carried out by using methods to assess (1) the ability of TraceGame to enhance novices’ forensic capabilities, (2) the engagement of users in the experience provided by TraceGame, and (3) the usability of TraceGame. Indeed, it is essential that TraceGame is effective as a training tool, but also that the experience with TraceGame does not turn out to be frustrating, which would lead to the abandonment of the proposed training tool.

For the first point, the ability of TraceGame to enhance novices’ forensic capabilities was evaluated by analyzing the game scores achieved by the volunteers in the experimental session in two successive game sessions. Indeed, if higher scores were obtained in the second game session with respect to the first one, it would be an obvious demonstration of the ability of TraceGame to improve novices’ forensic capabilities.

To assess the engagement of users in the experience provided by TraceGame, similarly to the experimental study in [11], a questionnaire with the following open questions was designed:

• What adjectives would you use to describe the TraceGame experience?
• What is your physical condition after using TraceGame? For example, do you have eye strain?
• What did you like the most?
• What would you change?

In general, this questionnaire focuses on the device’s acceptability and on the general feeling towards the TraceGame experience.

Finally, for usability purposes, the well-known System Usability Scale (SUS) [30] was used. This method was chosen because it is a highly robust and versatile tool for usability professionals, as shown in the empirical evaluation reported in [31]. Moreover, it has been used in several studies and applied in the evaluation of several projects related to new interaction environments, such as extended-reality systems [32,33]. Briefly, the SUS is a ten-item scale giving a global view of subjective assessments of usability (see the SUS survey in Appendix A,

Table A1. System Usability Scale survey.

Question	Strongly Disagree				Strongly Agree
Q1: I think that I would like to use this system frequently.	□	□	□	□	□
Q2: I found the system unnecessarily complex.	□	□	□	□	□
Q3: I thought the system was easy to use.	□	□	□	□	□
Q4: I think that I would need the support of a technical person to be able to use this system.	□	□	□	□	□
Q5: I found that the various functions in this system were well integrated.	□	□	□	□	□
Q6: I thought there was too much inconsistency in this system.	□	□	□	□	□
Q7: I would imagine that most people would learn to use this system very quickly.	□	□	□	□	□
Q8: I found the system very cumbersome to use.	□	□	□	□	□
Q9: I felt very confident using the system.	□	□	□	□	□
Q10: I needed to learn a lot of things before I could get going with this system.	□	□	□	□	□

). The instrument uses a 5-point Likert scale ranging from 1 to 5, where 1 indicates that the respondent strongly disagrees with the statement and 5 indicates that they strongly agree. The SUS is used after a respondent has had an opportunity to use the system being evaluated. If an item has no answer, the center point of the scale should be marked. By analyzing respondents’ answers, it is possible to compute an overall SUS score by weighting the single responses. In more detail, the procedure first sums the score contributions from each item. Each item’s score contribution ranges from 0 to 4 in the following way: For items 1, 3, 5, 7, and 9, the score contribution is the scale position minus 1, whereas for items 2, 4, 6, 8, and 10, the contribution is 5 minus the scale position. Then, the procedure multiplies the sum of the scores by 2.5 to obtain the overall value of the SUS. SUS scores range from 0 to 100. According to the study in [31], within the interpretation of SUS scores for products, SUS scores less than 50 should be cause for significant concern and should be judged to be unacceptable; SUS scores between 50 and 70 are marginally acceptable and should be improved; SUS scores above 70 are adequate [32].

4.3. Setup of the Experimental Session

The experimental session involved 10 volunteers (5 men and 5 women) whose ages ranged from 18 to 45 years. The experimental session consisted of testing the game session user mode. Due to the difficulty of finding novice police officers, the volunteers for the experimental session were common people. The volunteers were divided into two groups: people with high/medium familiarity with smartphones, computers, and mixed reality (indicated with H) and people with low familiarity (indicated with L). In the experimental session that was carried out, the experience with TraceGame took place in a room in which a primary, macroscopic, and disorganized crime scene was physically reconstructed (see Figure 7). The crime scene contained six traces to detect. Before starting the game session, each volunteer was initially introduced to what TraceGame’s aim was and received an explanation of the Microsoft HoloLens gestures, such as the use of the gaze to point to objects and the air tap for performing a selection. Then, each volunteer used TraceGame autonomously. At the end of the game session, the users completed the SUS survey and gave their personal opinions in the open questionnaire.

5. Results and Discussion

For the first evaluation of the ability of TraceGame to enhance novices’ forensic capabilities, the results reported in

Table 1. Game scores for all volunteers in the first and the second game sessions.

	H1	H2	H3	H4	H5	L1	L2	L3	L4	L5
I	6	3	4	1	4	3	3	2	3	1
II	-	6	5	3	6	4	3	3	5	3

show that all participants improved their game scores at the end of the second game session by identifying more traces. It is worth noting that volunteer H1 did not participate in the second game session, since he already achieved the highest score in the first one.

According to the analysis of the questionnaire with open answers, all participants wore the HoloLens headset with no particular problems, except for two participants who experienced slight discomfort with the device due to its weight. However, it is important to note that none of the participants manifested motion sickness or eye fatigue. This result suggests that this device could be more comfortable and induce fewer physical side effects than VR headsets, although deeper experiments are needed to support this hypothesis. Overall, all participants showed a positive feeling towards Microsoft HoloLens and TraceGame. Participants that provided free comments used terms such as very interesting, engaging, outstanding, innovative, fun, and wonderful to describe the TraceGame experience.

Finally,

Table 2. Responses to individual SUS items for the two considered groups with high/medium or low skills in MR.

	H1	H2	H3	H4	H5	L1	L2	L3	L4	L5
Q1	5	4	4	4	4	5	5	5	5	5
Q2	1	2	3	2	3	2	5	5	3	1
Q3	5	4	5	4	3	3	5	5	4	5
Q4	1	3	4	5	2	3	4	2	2	3
Q5	5	4	4	4	3	4	5	5	4	4
Q6	1	2	3	2	4	3	2	3	3	3
Q7	2	4	4	4	4	3	4	5	5	5
Q8	3	2	3	2	2	2	3	2	1	3
Q9	5	4	2	4	4	5	5	5	5	4
Q10	1	2	3	2	1	2	3	2	1	2
Score	87.5	72.5	57.5	67.5	65	70	67.5	77.5	82.5	77.5

reports the individual SUS responses and the scores obtained for all volunteers.

Table 3. SUS items’ mean score contributions (weighted: range 0–4): group with a high/medium level of skill in MR.

	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10
Mean	3.2	2.8	3.2	2	3	2.6	2.6	2.6	2.8	3.2
Std	0.45	0.84	0.84	1.58	0.71	1.14	0.89	0.55	1.1	0.84

and

Table 4. SUS items’ mean score contribution (weighted: range 0–4): group with a low level of skill in MR.

	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10
Mean	4	1.8	3.4	2.2	3.4	2.2	3.4	2.8	3.8	3
Std	0	1.79	0.89	0.84	0.55	0.45	0.89	0.84	0.45	0.71

report the SUS items’ mean score contributions for all questions for each considered group of volunteers. In these tables, it is possible to see that for volunteers belonging to the first group (i.e., high/medium familiarity with MR), the fourth question—related to the need for the support of a technical person to be able to use the system—was the only question with a low score. For the second group, instead, there were three questions with low score values, that is, Q2, Q4, and Q6, which were related to the complexity of the system, the need for support, and the eventual inconsistency of the system, respectively. To conclude,

Table 5. SUS scores for both the group with high/medium skill in MR (Group H) and that with low skill (Group L).

	Mean	Std	Min	Max	Median
Group H	70	11.18	57.5	87.5	67.5
Group L	75	6.12	67.5	82.5	77.5

reports the statistics of the SUS scores by considering the two groups of volunteers. According to the analysis of this table, both groups evaluated TraceGame as an acceptable tool on average according to the scale reported in [31]. Moreover, no volunteers evaluated TraceGame as an unacceptable tool. Indeed, the minimum SUS score was 57.5, which is greater than 50.

6. Conclusions

Today, the examination of crime scenes and analysis of forensic evidence are becoming key strategies for identifying the perpetrators of criminal acts. For this reason, it is necessary that investigators routinely exercise the proper processing of crime scenes by recognizing and collecting only the relevant physical evidence. TraceGame is a serious mixed-reality game aimed at improving novice forensic investigators’ skills related to the search for and recovery of evidence at crime scenes. As shown in an experimental session involving 10 volunteers, the use of TraceGame resulted in a very effective training tool by providing a very interesting and positive experience. The majority of the experimental session’s participants showed a willingness to reuse the tool and confirmed its good usability.

The use of TraceGame will have both economic and social implications. Currently, in fact, on-the-job training of novice forensic investigators involves pairing them with more experienced agents to investigate criminal events, resulting in many resources being allocated to solving the same criminal event. Thanks to TraceGame, the massive commitment of experienced officers in training novice investigators will be reduced. In addition, the effective training of novice forensic investigators by using TraceGame will lead to an increased probability of solving crime cases, which will have a positive effect on citizens’ perception of safety.

In the future, the idea is to put the tool in place by having it used in the training of investigators in the national barracks. However, to achieve this goal, additional experiments should be carried out to test TraceGame by considering novice forensic investigators as volunteers. Moreover, the experimentation will involve different crime scene scenarios and the scenario creation user mode of TraceGame.