Modeling Task Transitions to Help Designing for Better Situation Awareness Thomas Villaren1,2, Gilles Coppin1, and Angélica Leal2 1 2 Institut Mines-Télécom – Télécom Bretagne Bertin Technologies UMR CNRS 3192 Lab-STICC 10bis, avenue Ampère Brest, France 78053 St-Quentin-en-Yvelines, France
[email protected] [email protected] ABSTRACT
elements can be brought to the operator's attention, and others might become irrelevant in the light of the new task. This idea is conceptualized on Figure 1: both tasks are represented as Venn diagrams; each one corresponds to a set of situation elements. During the transition from T1 to T2, some of these elements remain relevant while others fade away (in T1) or appear (in T2).
In complex systems such as cockpits or unmanned systems, operators manage a set of tasks with high temporal dynamics. Frequent changes of situation within the same mission can sometimes induce a loss of operators’ Situation Awareness. In this paper, we introduce a methodology for design of Human-Computer Interfaces in dynamic systems taking into account the situation elements constituting operators’ activity. We follow a user-centered approach; end-users and domain experts are included along the different steps of this model-based design process. The complete methodology is presented here, from initial task & situation modeling, through transition analysis, to the final recommendations on interface design, applied to an illustrative example.
Figure 1: Venn diagrams of a "simple" transition, both tasks are overlapping, some Situation elements are shared while other are not.
Author Keywords
Methodology; Human Factors; Situation Awareness; modelbased design; interfaces; task switch; interruption.
Other types of transitions, such as task addition or removal, task interruption or retrieval, can also induce changes in context. These switches, particularly when they are frequent, may impact operators at different levels, and induce a decrease of their Situation Awareness (SA) (for accounts of such issues, see for instance [6,13]).
ACM Classification Keywords
H.5.2 [User Interfaces]: Theory and methods; INTRODUCTION
Operators of complex systems such as aircraft cockpits, air traffic control centers or unmanned aerial systems (UAS) are dealing with a set of highly dynamic tasks. Their mission usually involves switching between different tasks, each associated with a different mission context. These frequent changes may impact operators’ Situation Awareness, defined by Endsley [9] as their “perception of elements in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future”.
Transitions in the Literature
In the literature, transitions between tasks have been approached with different angles. Studies on Task Switching have been led in the field of Cognitive Psychology for almost two decades [1,2,18,22]. This research examines the cognitive impacts of switching between low-level tasks (Stroop-like tasks [15] such as identifying and naming a color, a shape, reading a word or classifying a digit as odd or even), particularly the switch time (or “cost”). Although the transitions studied in these works are of interest, we consider tasks of higher level such “piloting a drone in manual mode”.
When switching from a task T1 to a task T2, the mission context associated with each task evolves, new situational Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. EICS’12, June 25–26, 2012, Copenhagen, Denmark. Copyright 2012 ACM 978-1-4503-1168-7/12/06...$10.00.
Other works have dealt with a specific type of task transition: Interruptions [16,17]. These studies focus on different aspects of interruptions such as: coordinating human-computer interactions in order to interrupt operators at the most appropriate time [17], integrating interruptions in the design phase [11] or helping to recover the context
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after an interruption [26]. However, we identified certain types of transitions between tasks that were not addressed in the literature. For instance, “basic” transitions where a task T2 substitutes a task T1 (as in Figure 1) or when a second task is added to the first one.
analysis. Finally, we provide an example of application for this methodology. METHODOLOGY
In order to address the issues raised in the introduction, we propose a 4-step methodology presented in Figure 2.
Goal
In this article, we present a methodology for HumanComputer Interface (HCI) design which takes into account these context-switching situations through a model-based and user-centered approach. The goal of this methodology is two-fold: •
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We aim first at ensuring operators’ cognitive continuity & compatibility, i.e. the ability for operators to correctly perceive and interpret the same concepts in both tasks (similar definitions have been proposed in different domains: Augmented Reality (AR) systems [8], Mixed Reality (MR) systems or multiplatform systems [10]). In other words, the resulting interfaces should help minimizing, or at least reducing, the loss of Situation Awareness resulting from a transition between tasks. We also aim at ensuring task functional continuity, i.e. the ability for operators to execute the new tasks in continuity with the previous ones, with no or little adaptation (see also [10]).
The point of view adopted in our approach is a modeldriven engineering one: rather than focusing on the perception aspects of the transitions (such as [5,25] for instance which could be seen as a potential answer to our analysis process), we model operators’ tasks and required Situation Awareness elements as a starting point for the methodology, attaching our methodology to the Task Analysis approach [4]. We also tackle the Human Factors issues raised by such transitions through a user-centered approach, including end-users and Subject Matter Experts (SME) throughout the design process (interviews, expert validation of models and user tests of final interfaces).
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During the first step, two models are built: operators’ activity is described in a Task Model and Situation Awareness elements corresponding to their activity are described in a SA Model.
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Secondly, each task of the Task Model is associated with a set of SA elements from the SA Model according to the SA requirements for this specific task.
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Once all the tasks have been covered by the Task/SA elements association process, starts the Transition Analysis step: tasks are compared two by two in order to categorize the transitions that link them. Thanks to the use of adequate comparison methods, this analysis allows to detect task/SA elements associations that can raise cognitive issues in transition because of a major dissimilarity or, on the other hand, couples of tasks that transition smoothly and easily.
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Finally, the design expert emits recommendations based on the previous analysis and defines the impacts these transitions might have on HCI design, relying mainly upon his/her own expertise.
The next subsections are devoted to describing the three first steps of this methodology. Task Modeling Phase (Step 1a)
One of the two first concomitant modeling phases of the methodology is the modeling of operators' attended tasks. This phase requires information on operators' everyday activity. This knowledge is elicited through interviews with operators and SMEs [4] but also extracted from other sources, such as expert reports and literature reviews.
The following section introduces this new methodology and describes the two models used to define the tasks and situation elements considered. Then, we discuss in detail the question of task similarity and its consequences on task
All this data is then compiled and structured into a task tree. We rely on the ConcurTaskTrees (CTT) notation [20,21] to build this task model. This notation combines the description of tasks hierarchical linking (on a downward-
Figure 2: our 4-step methodology and its relationship with end-users & SMEs.
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iterative Watch task and the interactive Switch vision mode task (the player can switch between a normal mode and a night vision mode when playing). The Watch task is divided into two tasks: the first, Seek & Move, is iterative and can be interrupted by the Detect & Identify track task. The bottom of this tree pictures two concomitant tasks: a user task, Seek, and an interactive one, Move view. The Seek & Move is an example of a dummy task: it helps disambiguate the transition from between the Move view and Seek tasks interrupted the Detect & Identify track one.
oriented vertical axis) with the temporal relationships between tasks of a same abstraction level (on a rightwardoriented horizontal axis). On the vertical axis, the CTT notation allows the description of tasks at different levels of granularity, from the higher abstraction levels down to the interaction level. While the lower levels describe precisely the interaction means and provide design indications, the higher ones only explicit the main tasks performed in operators' activity. As our goal in this step is not to describe the whole interface and interaction means, but only to describe the main tasks and subtasks attended by operators, we limit ourselves to higher abstraction levels.
Situation Modeling Phase (Step 1b)
The second concomitant modeling phase consists in building a Situation Awareness Model. Amongst the wide range of models existing in the literature (see [23,24] for reviews of existing models), we selected Stanton et al.’s Distributed Situation Awareness (DSA) model [27,28].
The horizontal axis holds the temporal relationships between the tasks of a same abstraction level. At each level, subtasks of a same parent task are linked with CTT temporal operators (inherited from the LOTOS formal description technique [12]). The extent of CTT temporal operator makes it a natural choice when modeling the transitions between tasks.
In this model, SA is depicted as a system-level stateoriented phenomenon, distributed between the different operators of a complex system (each operator being in charge of a specific task). In order to describe the SA requirements for a specific system, the DSA model relies on the schema theory, supported by propositional networks representing the links between the “knowledge objects” (which can be viewed as SA requirements elements).
Figure 3: CTT notation applied to the control of a videogame player’s view.
The left-to-right temporal orientation of CTT trees makes sense only in the case of sequencing tasks. If two sibling tasks are meant to exist in parallel or exclusively from each other, their horizontal position does not matter. The CTT notation proposes [20] to prevent reading ambiguities by either using the priority order defined in LOTOS standard (choice & parallel operators have the priority over sequence ones) or introducing new dummy tasks that act as “factoring” tasks and dissociate groups of sequencing tasks from groups of parallel ones. We prefer relying on the dummy tasks solution, which improves trees readability (see Figure 3). The ConcurTaskTrees notation is supported by the CTTE tool [19].
Figure 4: extract of the DSA graph of a videogame.
During the construction process proposed by Stanton et al., each operator (each managing one task) is interviewed and their knowledge is elicited in order to build a partial graph depicting the SA elements required for this task. The resulting set of graphs (one graph per operator) is then merged into a single graph picturing the overall Situation Awareness elements for the system. In order to retain the link between operator’s task and their corresponding SA elements, color-coding of graph nodes is used: it indicates “in a simple, visual manner the relationship between specific agents and specific objects over the course of the mission” [28].
Figure 3 presents an extract of a player’s CTT tree in the videogame Battlefield 3™: the First Person Shooter multiplayer game will serve as example throughout this article. We focus here on the Control Player’s View task. Two exclusive tasks are present at the top of this tree: the
For the purpose of our methodology, we transposed the multi-operator / single-task paradigm adopted by Stanton et
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al. into a single-operator / multi-task paradigm. The construction of the SA network relies on the same inputs as the definition of the task tree: we build a network of SA elements describing the system’s SA requirements upon the feedbacks from interviewed operators & SMEs as well as domain-specific knowledge. As in Stanton et al.’s construction method [27], we compile each operators’ graph (qualified as a “phenotype”) into a single graph (the “genotype”). The resulting graph is a system-specific compilation of SA requirements, gathered in a unique graph.
The schema on Figure 5 illustrates the association process where two sequencing tasks are each associated with a different subset of the overall network (in our example, this network contains only 9 nodes). The highlight brought by the color-coding allows a fast identification of the SA requirements for each specific task. Models built in the first step and associations defined through this second step are then refined and validated through discussions with experts. CTT and DSA notations use visual representations which make them descriptive enough to be easily understood.
Figure 4 presents an extract of a DSA graph for the First Person Shooter (FPS) videogame Battlefield 3™ with a purposely reduced number of SA elements represented. Propositional networks are built upon ‹subject›‹relation› ‹object› relationships (e.g. “Player has stamina”). These relationships can be extracted from the transcripts of interviews as they correspond. If necessary, the relationships extracted from different sources can be adapted and merge into one triplet expressing the same relationship.
TRANSITION ANALYSIS (STEP 3)
Once the two models are linked, we can start working on the third step of the methodology which consists in analyzing & categorizing the different transitions between tasks. The principle of this step is to provide different means to compare the SA contexts for two tasks and qualify the transition which links these tasks. In the domain of Interruption, McFarlane and Latorella [16] propose a taxonomy of Human Interruptions in HCI. Their taxonomy uses qualitative factors (such as source of interruption, mean of interruption, characteristics of the operator, or effects of interruption…) as different viewpoints for discussing User Interface (UI) design supporting these interruptions.
Associating Task with Situation Awareness Elements (Step 2)
Stanton et al. define DSA as “activated knowledge for a specific task within a system” [28]. Following this definition, and as an analogy with the color-coding of the knowledge network produced in their elicitation process, the second step of our methodology links the different tasks modeled in the first step with subsets of the overall SA elements graph.
Step 3 and 4 of our methodology are inspired by this twostep process, classification of an interruption followed by UI recommendations. Thus, we propose to use different qualification processes in order to categorize the transitions studied. Our focus being the impact of a transition on operators’ SA, we provide different solutions to measure the similarity and differences in terms of SA between two tasks encompassing a transition. Task comparison relies on quantitative measures (Tversky’s similarity ratio model, graph matching, and extraction of salient elements) and more qualitative observations (addition of metadata to characterize SA elements). Tversky’s Similarity Ratio Model
In his work, Tversky [29] proposes different similarity models based to measure the common and distinctive features of two items. We use the ratio model, which provides a normalized similarity measure ( 0 ≤ S ≤ 1 ).
Figure 5: illustration of the Task/SA elements association process.
This “Task/SA Elements association” process is straightforward: to each relevant task of the CTT tree is associated a subset of the overall DSA graph nodes (a subgraph of the main graph), corresponding to the SA requirements for the task. Certain tasks can be considered as irrelevant during this association process, particularly dummy tasks (as defined earlier) which are only present for clarity sake and do not add any relevant information in terms of operators’ activity. There is no association for these tasks.
Equation (1) defines this ratio:
S (t i , t j ) = •
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f (Ti ∩ T j ) f (Ti ∩ T j ) + α . f (Ti \ T j ) + β . f (T j \ Ti )
(1)
f is the counting function. It counts common or distinctive elements between two tasks (noted Ti and Tj). In our study, counted elements are the different SA elements of the DSA graph associated to each task.
→
If they are involved in a transition, these central objects might need additional attention as they represent salient SA elements in the system. Particularly, if one SA element is associated to one task of the transition and not to the other one, this may have an impact on UI design.
f (Ti ∩ T j ) represents the number of SA elements
associated with both tasks Ti and Tj. →
f (Ti \ T j ) represents the number of SA elements associated to Ti only and inversely for f (T j \ Ti ) .
•
SA Elements Tagging
In Tversky’s definition α and β are positive or null coefficients. Setting them to non-null values will increase the importance of elements associated only to one task.
In order to compare SA elements associated to transitioning tasks, we propose to add metadata describing and qualifying the state of these elements, when pertinent.
The scale extremities define two interesting values: •
S = 0 if and only if both tasks are unconnected (they do not have any common SA elements).
•
S = 1 if and only if both tasks are exactly associated to the same common SA elements (the case α = β = 0 we do not considered relevant).
This tagging process would be performed during the “Task/SA elements association step” (step 2), and be based on qualitative information elicited from interviewees. Added tags can address a wide range of information such as elements dynamics, importance, relevance or representation and directly transcribe operators’ specific requests on certain elements. As such, they may directly impact UI design. For instance, depending on the task, certain SA elements are best perceived by operators if displayed as a numerical value (as a number on the screen) or in a more graphical way (as a vector, a gauge…).
All other values of S are defined within these boundaries. The scale S can be used as a first classification method for transition categorization. Graph Matching
In propositional networks, absence or presence of edges between nodes holds as much information as the nodes themselves. When comparing two subgraphs associated with two tasks, seeking which SA elements are common to both tasks is the first logical way to measure similarity. We propose to improve this simple comparison process by seeking the largest common subgraph (LCSG): the idea is to find sets of nodes linked together (clusters) common to both graphs which could carry a specific meaning.
Unlike the previous comparison means (Tversky’s ratio model, graph matching, extraction of salience), the use of metadata to qualify each SA elements is hardly automatable but it provides qualitative information on each SA elements and thus can be used as input for UI design discussions. APPLICATION
In order to illustrate the modeling and association process presented in this article, this section presents an application of this methodology to the First Person Shooter videogame Battlefield 3™. We first introduce the videogame context, and then present an extract of the task tree and SA graph built upon it with a focus on a specific task encountered by players during their game.
Graph matching is a complex domain and many algorithms exist, particularly in the area of image analysis (see [3] for instances). These algorithms typically try to match two supposedly independent graphs built on data extracted from two images and which might, or not, have common features (e.g. face detection algorithms). In our case, the subgraphs compared are both extracted from the same main DSA graph (each subgraph corresponds to a highlight defined by its associated task), thus making them dependent. This dependency lowers the complexity of matching both subgraphs: one needs only to find the largest common subset of nodes between the subgraphs (the edges linking these nodes will be the same).
Context presentation
Battlefield 3™ is a videogame published by Electronic Arts (EA) and developed by EA Digital Illusions Creative Entertainment (DICE). Released in October 2011, it runs within different Operating Systems: Microsoft Windows, Playstation 3 and Xbox 360. This example focuses on the multiplayer mode: settled in a modern warfare environment, several game modes are proposed. We chose the Conquest mode, where two teams (up to 32 players on each team) are fighting for the capture of control points. When the game starts, the player needs to select a character class (Assault, Support, Engineer, Recon) which gives access to a specific set of weapons and items. Each player has also access to different vehicles (jeeps, tanks, aircrafts…) scattered on the battlefield at predefined positions.
We can use the common subgraphs detected as an input for discussions for UI design. Extraction of SA Elements’ Salience
We propose another method to highlight salient elements in a transition. This method also relies on Stanton et al.’s work [28] that identifies the most pertinent information tokens of a network as “those knowledge objects that serve as a central hub to other knowledge objects (i.e., have five or more links to other knowledge objects)”. This extraction is done on the overall network (without any color-coding from a task) and highlights the central objects of the system.
Modeling process
The models presented in this section have been built upon interviews with two frequent players and additional data
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Figure 6: portion of the CTT tree for a Battlefield 3™ player in Conquest mode.
extracted from the official website http://www.battlefield.com/battlefield3/).
(available
on
Situation Awareness Model
We built a SA model composed of 57 nodes and 77 edges as shown on Figure 8. In order to illustrate the principle of the methodology, we focused on the SA elements required for the completion of the two transitioning tasks and elements relative to other tasks are not all represented here.
The transition addressed in this example concerns the task of moving “on foot” in the environment to the task of driving a vehicle, and particularly a tank. Although this transition is triggered by the player, the two tasks are quite different and the switch might impact the player’s SA.
Task/SA Elements Association
The association step combines the SA model with the simplified CTT tree corresponding to our transition (Figure 7). Figure 8 displays this association step:
Task Model
The CTT tree presented in Figure 6 describes the tasks that can be attained by the player. We purposely collapsed the Attack task for space sake, our focus being on the Move on foot and Drive tank tasks. The first is a subtask of the Move task while Drive tank is a sub-subtask of Move. The fact that these tasks don’t belong to the same level is not an issue: the Drive tank task is a particularization of the Use vehicle one (which is a sibling of Move on foot).
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elements colored in salmon are associated with the Move on foot task;
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elements colored in green (white labels) are associated with the Drive tank task;
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elements shared between both of Move’s subtasks are colored with a green and salmon gradient background;
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elements colored in purple (white labels) are associated with the Orient task.
During the transition, the Orient task is distributed to both transitioning tasks as it occurs concomitantly with these tasks. In terms of SA graphs, the SA elements corresponding to this task (the purple ones) are thus present in both graphs associated with the Move’s subtasks. Elements associated with both task during the transition have a dot-and-dash border.
Figure 7: schematization of the switch between walking and driving the tank.
During the entire playing task, in parallel with the different tasks of the action branch, the Orient task is executed by the player. It consists of observing the environment with a focus on the landmarks and paths and is not goal-directed as would be the Seek & Move task.
Transition Analysis
During the switch between the two moving tasks, several changes on SA requirements occur, which may impact the player’s SA. While the Move on foot task requires a few elements of its own, the Drive tank task adds a number of new elements related to the tank that the player needs to be aware of (such as its role in the tank crew for instance). Figure 9 presents two screenshots of the videogame interface before and after the transition (the player enters the tank).
In order to understand what tasks are active during the transition, we schematized it with the CTT notation on Figure 7. This simplified tree shows that the Orient task is always present in the background while moving (whichever moving mean is used).
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Legend:
Notes: Elements associated with the Move on foot task Elements associated with the Drive tank task Elements associated with the Orient task
Elements common to the Move on foot and Drive tank tasks have a salmon & green gradient background color. Elements with a dot-and-dash border are associated to both tasks during the transition: the Orient is concomitant to these tasks, thus always shared.
Figure 8: portion of the DSA graph for a Battlefield 3™ player in Conquest mode.
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Figure 9: annotated screenshots of the Drive tank (top) and Move on foot (bottom) tasks. Annotations’ background colors correspond to the elements on Figure 8.
interface and are visible through “floating” blue pictograms when in the field of view.
Tversky Ratio
Amongst the 57 nodes, 3 are specific the Move on foot task, 12 to the Drive tank one, 2 are shared between those two tasks and 14 are common to the two contexts as they are associated with the Orient task. Tversky ratio for the transition is 0.52 if we set α and β to 1 (both tasks are considered of equal importance). This similarity score doesn’t provide strong information about the type of transition studied.
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From this analysis, the Human Factors expert can express different suggestions: grouping elements from one subgraph on the interface, and accompanying the evolution of certain elements during the transition. The subgraphs extracted here all correspond to “geographical” information which are displayed graphically and will be impacted if the tank doesn’t have the same orientation as the player.
Largest Common Subgraphs
When looking for the Largest Common Subgraphs to both tasks (amongst dot-and-dashed elements), we found four independent subgraphs: •
The “environment” subgraph regroups the different elements related to the landmarks and corresponds to information available on the minimap.
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The “control point” subgraph regroups the elements related to the different mission objectives (points to capture). They are displayed on the interface through “floating” pictograms.
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The last subgraph is a 2-node subgraph, related to spotted enemies. These enemies are marked on the interface (with a “floating” pictogram and a mark on the minimap).
Instances of recommendations may be to accompany the switch with a third-to-first person point of view, displaying the tank from outside for a few seconds with the same angle as the player’s before entering, thus providing awareness about the change in object’s position relatively to the new orientation. Extraction of Salience
The common subgraphs are associated with the Orient task. Amongst the two shared elements between the moving tasks, Player’s position and Current weapon, only the latter is impacted by the transition. Indeed, the player’s position
The “squad” subgraph relates to squad information. The members are listed on the bottom-left corner of the
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doesn’t change when entering the tank. However, the player switches weapon.
proposed in this article. We designed the tool to be used as a mobile application: it will serve as a basis for dialogue with system operators (during the elicitation and validation phases), and could be used as a support for discussions. We plan to use this tool in the context of other industrial projects in order to assess, validate and/or tweak the methodology.
The Current weapon node has 6 edges linked to it, making it a salient element. When entering a tank, the role taken by the player in the crew (driver/gunner versus machinegunner) defines the weapons they can use. In order to enhance the awareness about the weapon switch, we can display a temporary message indicating what weapon is now equipped. This temporary piece of information would strengthen the other elements displayed (weapon image, quantity of ammunition available, crew membership status…).
From a more theoretical point of view, some perspectives are also being addressed: •
We would like to assess the effect of transition on operators' Situation Awareness in relationship with the similar/distinctive features associated with each task. This study could feed the fourth and last step of the methodology as recommendations on UI design.
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In this article, we approach the issues raised through the design angle. But we believe that this methodology can be used as an evaluation tool for improvement of existing complex systems and will study this possibility in our future works.
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Finally, in order to address collaborative and multimodal systems, we may study the use of CTT extensions such as COMM [14].
New Elements
12 new SA elements are introduced with the Drive Tank task. Five are directly linked to elements associated with the Move on foot task (Player’s position, Current weapon, Crew, Vehicle and Turret orientation). We addressed the 2 first in the previous sections, and the Crew and Vehicle elements are linked to the previous ones. However, the Turret orientation, which can be different from the tank’s body orientation, is signaled through a pictogram (at the bottom of the screen), but additional SA could be provided through a third-to-first-person-view animation [7].
ACKNOWLEDGMENTS
Expert’s recommendations may be assessed with end-users through user tests based on mock-ups or prototypes resulting from these recommendations.
The work presented is partly funded by the French Procurement Agency (DGA) as a Ph.D. Thesis grant. The authors would like also to thank the anonymous reviewers.
CONCLUSION AND FUTURE WORK
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In this article, we have presented a methodology for design of complex systems’ Human-Computer Interfaces. This methodology adopts a model-based approach in order to capture and formalize operators’ activity and relies on these models to lessen impact of task transitions on operators’ Situation Awareness.
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