Bruno C Matias
email: brunoclaudinomatias@gmail.com

Victória O Gomes
email: victoria.oliveiragomes@gmail.com

Victor T Sarinho
email: vsarinho@uefs.br

UGC Generator: An Open Access Web Tool to Model Unified Game Canvas

Keywords: Game design canvas, UGC, Open access web tool

Game Design Canvas (GDC) is a framework that can be used to define fundamental game elements, and is able to provide a general view of a game designer idea. Unified Game Canvas (UGC) combines common GDC characteristics in a 5W2H perspective, increasing the design possibilities for a GDC creation of a desired game. This paper presents the UGC Generator, a simple and interactive open access web tool that helps game designers to fill UGC panels and to provide UGC models in a fast and dynamic way.

Introduction

Game design can be defined as "the act of deciding what a game should be" [START_REF] Schell | The Art of Game Design: A book of lenses[END_REF]. It is an important step to view the game as many perspectives as possible [START_REF] Schell | The Art of Game Design: A book of lenses[END_REF], working as a planning/documentation step in the process of creating desired analog/digital games.

Among the different ways of creating game design documents, Game Design Canvas (GDC) is a framework that can be used to define fundamental game elements [START_REF] Vargas | Uma extensão do Design Thinking Canvas com foco em Modelos de Negócios para a Indústria de Games[END_REF]. It allows a quick synthesis of game ideas that will be developed, showing the game project overview in a unique design panel [START_REF] Da Silva | Game thinking is not game design thinking! Uma proposta de metodologia para o projeto de jogos digitais[END_REF]. GDC also describes the information in a systemic, integrated and quick way, showing perceptions about how the developer team should act to compose the desired game [START_REF] Da Silva | Game thinking is not game design thinking! Uma proposta de metodologia para o projeto de jogos digitais[END_REF].

Several types of design canvas were proposed to define the necessary information to model a GDC, being a little exhausting to choose which fits better in a desired game project. As a result, the Unified Game Canvas (UGC) [START_REF] Sarinho | XVI Simpósio Brasileiro de Jogos e Entretenimento Digital[END_REF] was proposed to provide an unified canvas model for game designers. It organizes the modeling information found on available GDCs in a 5W2H perspective [START_REF] Rossato | Uma metodologia para análise e solução de problema[END_REF][START_REF] De Oliveira | Ferramentas para o aprimoramento da qualidade[END_REF][START_REF] Candeloro | Não tenha dúvidas: método 5w2h. Portal Administradores[END_REF], achieving as a result a fast modeling of fundamental game elements available in important GDC models [START_REF] Sarinho | XVI Simpósio Brasileiro de Jogos e Entretenimento Digital[END_REF].

This paper presents the UGC Generator, an open access web tool for the production of desired UGC models. It is a simple and interactive modeling tool that helps game designers to fill UGC panels and to provide UGC models in a fast and dynamic way.

The Unified Game Canvas (UGC)

5W1H is described [START_REF] Rossato | Uma metodologia para análise e solução de problema[END_REF] as a document that, through questioning, identifies actions and responsibilities able to guide what should be developed. Those questions are: "What?", "Why?", "Where?", "When?", "Who?" and "How?" [START_REF] De Oliveira | Ferramentas para o aprimoramento da qualidade[END_REF]. Later, Candeloro [START_REF] Candeloro | Não tenha dúvidas: método 5w2h. Portal Administradores[END_REF] suggest the inclusion of another questioning: "How much?" (the cost), generating the 5W2H tool.

For the UGC model, 5W2H is used as a game design information classifier, which should be related to: representation (what); responsibility (who); decision making (when); motivation (why); execution (where); production approach (how ); and business (how much) [START_REF] Sarinho | XVI Simpósio Brasileiro de Jogos e Entretenimento Digital[END_REF]. This information is organized in panels, that must be fulfilled by the game designer to start the development of a game.

Eight panels are proposed by the UGC [START_REF] Sarinho | XVI Simpósio Brasileiro de Jogos e Entretenimento Digital[END_REF], which should be filled in the following way:

-Game Impact: defines what the game must bring to the player, highlighting motivations and reasons of emotion, fun and learning; -Game Concept: seeks to attend the demand in identify the game responsibility, highlighting their name, objective, intention and inspiration to the game being created; -Game Player: describes the information about who is playing the game, varying from player ages to possible community involved; -Game Play: describes the game representation, highlighting their start, middle and end steps according limitations and rules inside a defined game space; -Game Flow: seeks to represent the game time experience of the player, highlighting the repetition loop, the permanence in the playing act and the concept of "my turn to play"; -Game Core: indicates elements for the game building, describing mechanics for the game rules, dynamics for game systems and the aesthetics for modeled game components; -Game Interaction: presents the execution environment of the game itself, varying in platforms, implementation resources and technology involved; and -Game Business: indicates financial concerns and the market view of the game.

The UGC Generator

Developed by the integration of web technologies, the UGC Generator, which is available at https://ugc-generator.herokuapp.com/, starts on a screen that shows UGC panels waiting to be filled (Fig. 1). Each UGC panel contains fields with placeholders that give hints about the information that is expected to be filled by the user. The user is free to fill the panel with any necessary information for the game design. If a UGC panel has too many information, the application will reduce the text font size to fit all in the respective panel, when the PDF is generated. The "Generate" button (end of the page) produces the respective UGC PDF with all panel information, which can be download immediately. The user can also provide an email to receive the UGC PDF, together with an extra JSON file.

A JSON file represents each subsection that the user can fill in a UGC panel. It provides the necessary content to load the UGC model and perform desired changes on it. To upload an UGC JSON file for edition, is necessary to press the "Load JSON file" button (top of the page) and select the desired file to upload.

Regarding the UGC Generator usage, it was successfully applied in the Master of Process game design [START_REF] Sarinho | Masters of the process: A board game proposal for teaching software management and software development process[END_REF]. It is a paper-based board game developed to teach how to take decisions during software project phases, where you can hire, fire and train employees in development activities. The filled UGC for this game can be checked in https://ibb.co/8B8qwjJ.

Conclusions and Future Work

This paper presented the UGC Generator, an open access web tool able to create and edit UGC models in a fast and dynamic way. For that, the UGC design canvas was described, together with the main functionality of the proposed tool.

The UGC Generator is in an initial development version, but already presents a useful resource to optimize the time of anyone who needs to design games and their features, without having to worry about small details and the canvas formatting.

As future work, some features will be changed to make it more suitable and comfortable for their users, such as personal user account, usage monitoring and usability assessment, for example. The integration with game patterns, game elements and game features, showing possible contents to be filled in a respective panel, will be also performed in the future.

Fig. 1 .

 1 Fig. 1. UGC Generator website.


Andrew Reilly

Dirk Van Rooy

Simon Angus

Dirk Van Rooy

An Exploration of the Relationship Between Personality and Strategy Formation Using Market Farmer: Using a Bespoke Computer Game in Behavioural Research

Keywords: Serious Games, Personality, Openness to Experience, Exploration, Strategy, Research

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Games as Tools for Research

Over the last decade there have been a number of calls for the use of games in psychological research [START_REF] Coovert | Serious games are a serious tool for team research[END_REF] [2] [START_REF] Szell | Measuring social dynamics in a massive multiplayer online game[END_REF] as a means of placing participants in semi-controlled situations where changes in behaviour can be recorded over time [START_REF] Schönbrodt | Virtual social environments as a tool for psychological assessment: Dynamics of interaction with a virtual spouse[END_REF]. In the broader context of methodology, they complement existing approaches by offering a trade-off between the kind of mundane realism of observational studies and the high levels of experimental control available in laboratory experiments [START_REF] Blascovich | Target Article: Immersive virtual environment technology as a methodological tool for social psychology[END_REF]. Furthermore, where studies require participants to be motivated to complete a given task, games engage participants in a way that can produce more authentic behaviour [START_REF] Washburn | The games psychologists play (and the data they provide)[END_REF]. By deliberately incorporating elements that motivate players' needs for competence, autonomy and relatedness [START_REF] Boyle | Psychological aspects of serious games[END_REF], players experience a desire to continue playing the game.

However, as with any approach, games have their drawbacks as research tools. Developing a game is a resource-intensive process and the focus on engagement often comes at the cost of external validity, particularly when mechanisms unrelated to the target phenomena are introduced [START_REF] Washburn | The games psychologists play (and the data they provide)[END_REF]. For example, a number of approaches have used commercially available games, but validity and cross-platform reliability are limited by the existing constraints of the selected game [START_REF] Williams | The mapping principle, and a research framework for virtual worlds[END_REF]. One way of resolving this is to select or produce games that can easily be modified according to the demands of individual studies [START_REF] Elson | Digital games in laboratory experiments: Controlling a complex stimulus Through modding[END_REF]. This leads to a design sciences approach, where a platform is created that is capable of producing a wide variety of games with a common theme or genre relevant to a broad field of research, thus reducing the need to "reinvent the wheel" every time a game is produced [START_REF] Reilly | Design sciences in social psychological research: Using computational models and serious games to complement existing methods[END_REF]. The aim of this pilot study is to develop an online game that is sufficiently open-ended to offer the potential for further development into such a platform, while also displaying sufficient utility as a research tool. In order to demonstrate this utility, the game is used to examine the relationship between personality and strategic cognition.

Individual Differences in Strategic Cognition

Studies in strategic cognition examine the role of cognitive structures and processes in the formation of strategies that can occur, for example, in a business environment [START_REF] Narayanan | The cognitive perspective in strategy: An integrative review[END_REF]. In this context, the conclusions drawn can differ according to the type of environment that managers work in, and recent years have seen a particular focus on the use of the doing first approach to strategy formation as a result of disruptive change in the technology sector [START_REF] Mintzberg | Decision making: It's not what you think[END_REF]. In an environment where there is little understanding of an optimal strategy, this approach advocates action as a means of generating responses from the environment that are then evaluated. Actions that produce positive results are retained and others are discarded, and strategies are thus derived through a process of trial and error [START_REF] Gibcus | The decision-making entrepreneur: A literature review[END_REF].

The trade-off between exploration and exploitation is fundamental to strategic cognition, as the exploitation of existing information or resources may be compromised by exploration that results in new sources [START_REF] Hills | & Cognitive Search Research Group.: Exploration versus exploitation in space, mind, and society[END_REF]. An optimal model of an explorationexploitation strategy produced by Berger-Tal, Nathan, Meron, and Saltz [START_REF] Berger-Tal | The exploration-exploitation dilemma: a multidisciplinary framework[END_REF], suggests that in environments where there is little or no existing knowledge, individuals acquire strategies by initially exploring the environment and gradually acquiring information through feedback. Over time, however, the costs of exploration outweigh the returns from exploiting information gained in the past, and individuals gradually transition to an approach dominated by exploitation.

Considerable interest has been shown in understanding the antecedents of exploration and exploitation related to individual differences. In particular, the Five Factor Model (FFM) personality trait of openness to experience, which reflects a willingness to change and try new approaches [START_REF] Johnson | Measuring thirty facets of the Five Factor Model with a 120-item public domain inventory: Development of the IPIP-NEO-120[END_REF], appears to naturally align with behaviours associated with exploration. Although studies have found small but positive correlations between openness to experience and exploration [START_REF] Keller | Behind managers' ambidexterity-studying personality traits, leadership, and environmental conditions associated with exploration and exploitation[END_REF][17], these focus on establishing relationships between dispositional variables measured through self-report scales rather than on behavioural observation. Furthermore, openness to experience is composed of a number of subfactors, or facets [START_REF] Johnson | Measuring thirty facets of the Five Factor Model with a 120-item public domain inventory: Development of the IPIP-NEO-120[END_REF], each of which may contribute to differences in the way individuals use exploration. Therefore, studies examining relationships between exploration and openness to experience may result in weak or non-existent correlations because different facets of openness to experience may impact on exploration and exploitation at different points in time.

Conventional approaches to examining exploration have been valuable in developing an understanding of individual differences in strategic cognition. However, these approaches have not been able to give us much insight into how individual differences in personality affect the strategic use of exploration over time. As argued at the start, the use of computer games as research tools to gather behavioural data in real time can address this, as they are ideal for capturing data on the dynamic employment of exploration in strategy formation. A combination of approaches, where personality traits are measured before gathering behavioural data, could yield insights into the relationship between personality and the transition from exploration to exploitation. The use of a bespoke game would also allow for the development of a game specifically designed to capture this behaviour, while ensuring that participants begin with little or no knowledge of optimal strategies, thus simulating an environment where these are unknown.

Examining Strategic Cognition with Market Farmer

Development of Market Farmer

Strategic cognition is a process that includes exploration of the environment and the exploitation of information gathered through feedback [START_REF] Cohen | Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration[END_REF]. Activities designed to capture this behaviour over time must provide participants with the opportunity to make decisions regarding the acquisition and use of this information. As our focus is not on decision-making behaviour under time pressure [START_REF] Maule | Effects of time-pressure on decision-making under uncertainty: Changes in affective state and information processing strategy[END_REF], the game is structured in a way that allows participants to control the advancement of the game. Game time was divided into 1200 ticks, with each tick activated by a button or the space bar that triggered game events related to that tick. This allowed players time to deliberate over decisions, though players were limited to a maximum of two hours to complete the game.

Games that succeed in engaging players draw on fundamental motivations of competence and autonomy [START_REF] Boyle | Psychological aspects of serious games[END_REF]. Players enjoy facing and overcoming challenges that fit their skill level, as well as the sense of control in setting and meeting game goals [START_REF] Sweetser | GameFlow: A Model for Evaluating Player Enjoyment in Games ACM Computers in Entertainment[END_REF]. This can be achieved by creating a system of rewards and punishments within the game that motivates players to form and improve on strategies. These considerations resulted in the game Market Farmer, based on a simple mechanic where players plant crops in fields when the price of the crop is low and then wait for the crops to grow while the price changes. When the crops mature, they are automatically sold at the current price, and thus the only control players have over their ability to make a profit is to gauge the best time at which to plant the crops. This provides players with an opportunity to observe changes in crop prices and learn from past mistakes in selecting the right time to plant crops through a system of rewards and punishments that occur when players make a profit or loss. For example, when players plant at the wrong time, the crop will sell at a low price and the return will be less than the cost of planting the crop, thus incurring a loss.

This simple mechanic provides a low-level decision-making process. However, meaningful gameplay that sustains interest over longer periods requires a macro-level decision-making context that interacts with these micro-level decisions in a way that increases the tension experienced by the player [START_REF] Salen | Rules of play[END_REF]. Therefore, the concept of improvements was created to allow players the ability to increase profits and prevent losses. In order to incur more dramatic potential losses, floods that destroy crops and birds that eat them were introduced. This provided the player with the opportunity to install improvements to prevent losses incurred from floods and birds, and increase profits from fields, while constraining the number of improvements in each field forced the player to make decisions regarding which fields improvements should be installed in.

To create further tension in making these decisions, crop varieties were given different qualities according to predictability of price volatility and levels of investment and return, such that varieties with more unpredictable volatility required a larger investment, thus increasing the potential loss, but offering an equally large potential for profit. Thus, players were able to offset the risk of more volatile higher value varieties using improvements, while small differences in volatility between varieties offered players the opportunity to experiment with varying levels of risk. The combination of crop varieties with varying levels of risk and return, floods, birds, and improvements offered players many ways to achieve a high score, with the values, timing, and offsets of each game element carefully balanced to ensure that on average, preferences for a particular improvement type, for example, would not result in a difference in overall score.

Finally, to distract players from the use of the game as a research tool and create an enjoyable experience that would hold their interest for the duration of the game, common game elements such as bright, colourful graphics and lively animations were used to create a sense of action and movement in the game, along with cartoon-like auditory cues to provide feedback to the player. To control for differences in ability, a tutorial was included at the beginning of the game using a narrative to engage the player in a purposeful understanding of what was required, and to show examples of the game mechanics. To maintain motivation, the ability to acquire new crop varieties, fields, and improvements as rewards was staggered throughout the game to encourage goal-setting. Pop-up alerts and hints were also used throughout the game to ensure that players were aware of their options, particularly as new elements were being introduced, or rewards provided.

Playing Market Farmer

On loading the game, the player is presented with an introduction screen showing two farmers of the same gender as the player, who provide instructions on how to play the game in the tutorial that follows. The farmers state the broad aim of the game in terms of maximising the amount of money made, and the player clicks through further screens with each farmer presenting aspects of gameplay from positive and negative perspectives: one stating a problem and the other offering a suggestion for solving it. For example, players are told that they can plant new crops and make improvements, but that some crops are expensive and risky. Players are then given an opportunity to sow and harvest a crop of potatoes, which provides very little profit, but is also very low risk.

After this, the option to sow potatoes is removed and replaced with broccoli, thus signalling the beginning of the game. The player begins with $1000, and if this amount falls below $20, it is automatically topped up to $20, thus allowing the player to sow broccoli crops and continue playing the game. Features of the game such as floods and improvements are explained to the player as they appear in the game in a similar manner to the tutorial. Figure 1 shows a screenshot from the game with a farmer explaining how to access improvements. Over time, the player receives more fields at predetermined points unknown to the them, until they have a maximum of eight fields. Players also receive access to more crop varieties based on the cumulative total of crops sown, i.e. the more crops the player sows, the faster they gain access to new varieties. Players also purchase improvements using farmhands that accrue over time, with a total of 32 farmhands available throughout the game. Information relating to these and other aspects of gameplay is available at the top left of the screen, where the number of ticks remaining, the number of crops that need to be sown in order to gain the displayed variety, the number of farmhands accrued, and a button that advances the game to the next tick are displayed. The amount of money available to the player (score) is displayed at the top centre of the screen. When clicking on the farmhands icon, the screen shown in Figure 2 is displayed, allowing the player to select an improvement when there are farmhands available to pay for it. Once installed in a field, improvements produce a given benefit in that field for the remainder of the game and cannot be removed. The following improvements are available for purchase:

Drainage prevents floods from occurring in a field and costs two farmhands. A total of 27 floods are scheduled to occur at regular intervals throughout the game, and the more often a player sows a crop in a given field, the more likely it is that a flood will occur in that field. When a flood occurs in a field with a crop, the crop is removed from the field and the player loses their entire investment for that crop.

Scarecrow prevents birds from landing in a field and costs one farmhand. Birds land on crops at regular intervals and reduce the profit of the crop by a random amount of up to 20%.

Fertiliser adds a random amount of up to 20% of the profit from a crop when it is sold, but has no effect on crops that are sold at a loss. Fertiliser costs one farmhand.

Irrigation adds 30% of the profit from a crop when it is sold but has no effect on crops that are sold at a loss. Irrigation costs two farmhands.

These improvements are balanced so that the use of drainage and scarecrows, for example, will have the same mean effect on overall profits as irrigation and fertiliser, respectively.

Figure 3 shows the screen in the second half of a game where all of the seven crop varieties are available. Players sow each crop by dragging the relevant icon into a field. The fixed cost of sowing each crop is displayed above the respective icon, while the figure to the right of this shows the amount of money the player would gain if the crop were harvested in this tick. The recent history of these values is displayed in the accompanying chart, which illustrates the increasing volatility of each variety. The combination of increasing volatility and profitability with each crop and the advantages offered by the improvements affords players a number of different strategies for success when defined as the amount of money made at the end of the game. As players are limited to two improvements per field, choices must be made regarding the potential losses and gains provided by a combination of improvements, and the effect this has on mitigating the risk inherent in planting more volatile crops. Initially, there is a strong incentive for players to explore by experimenting with different combinations, and once a successful strategy has been established, players may then continue to exploit that strategy, or explore other strategies. However, players are not forced to choose between these approaches, and it is possible for players to pursue a given strategy in some fields while exploring alternatives in others, or to adopt a broad strategy within which there may be some room for exploration.

3

The Current Study

Introduction

We now demonstrate how Market Farmer can be used to understand the relationship between openness to experience and behavioural patterns of exploration performed in the game, and game performance. These patterns of behaviour are identified according to the extent to which players repeat strategies by trying new crop varieties and improvements, and varying the combination of these (behavioural exploration), or by planting the same crop varieties in fields with the same combination of improvements (behavioural exploitation).

The game consists of 1200 ticks, during which the game pauses so that players can perform actions, but there are a number of phases that occur during the course of the game. Initially, players learn about how to play the game, during which separate elements are introduced in a staggered fashion to prevent overwhelming the player with information. Furthermore, as the player begins with only one crop and one field, the ability to extract meaningful information regarding strategies undertaken is limited until the player receives more crops and fields. As the crop growing times increasingly exceed the remaining game time in the last 100 ticks of the game, players' choices become more limited in regard to the crops that can be sown. For this reason, this period is not included in the analysis. Given that players begin the game with very little knowledge of what an optimal strategy might be, a period of behavioural exploration is expected, during which behavioural exploitation should be quite low. However, as the game progresses and players begin to acquire successful strategies, the situation is expected to reverse in line with the model offered by Berger-Tal et al. [START_REF] Berger-Tal | The exploration-exploitation dilemma: a multidisciplinary framework[END_REF]. This suggests that performance may be associated with an understanding of the optimal time at which exploitation should take precedence over exploration. Therefore, once participants have had enough time to acquaint themselves with potential strategies, and mean levels of behavioural exploration have peaked, participants who decrease their levels of behavioural exploration while increasing behavioural exploitation should experience a concurrent increase in score (in-game money).

Facets of openness to experience likely to moderate the negative relationship between behavioural exploration and score in the latter part of the game include: intellect, as participants scoring higher on this measure may be more likely to recognise and select successful strategies rather than needlessly continuing to explore; and adventurousness, as participants scoring higher on this may be more likely to continue exploring increasingly successful strategies. Also, the lack of a moderating effect of imagination, artistic interests, emotionality, and liberalism on the relationship between behavioural exploration and score means that the overall relationship between openness to experience and behavioural exploration remains low. On the basis of this, it is hypothesised that once mean levels of behavioural exploration have peaked, higher levels of openness to experience facets intellect and adventurousness will positively moderate the negative relationship between behavioural exploration and score, while imagination, artistic interests, emotionality, and liberalism will not moderate the relationship between behavioural exploration and score.

Method

Participants Participants were recruited through an online recruitment service and remunerated for their time. Only participants aged at least 18 and using devices with screen dimensions of at least 640 pixels were able to take part in the study. Due to compatibility issues, participants were not able to complete the study using the Firefox browser. A total of 94 participants completed the study, including 38 females and 56 males with ages ranging from 18 to 62 (M = 31, SD = 9.93).

Procedure After providing demographic information, participants completed a number of surveys as part of a wider study before playing the game, with the time taken to play the game ranging from 21 to 129 minutes (M = 51, SD = 20.81). At the end of the game, participants were shown their score and asked a series of questions on how much they enjoyed playing the game.

Participants completed the IPIP-NEO-120, a short-form adaptation of a longer scale developed as a measure of factors and facets of the FFM. Participants were asked to indicate the extent to which statements accurately described their personality on a 5point Likert scale from 1 (strongly disagree) to 5 (strongly agree). Statements related to facets of openness to experience include "I have a vivid imagination" (imagination), "I love to read challenging material" (intellect) and "I believe that there is no absolute right or wrong" (liberalism). Chronbach's alpha for openness to experience is .83, with alphas for facets ranging from .64 (liberalism) to .76 (imagination and artistic interests).

While dispositional measures use conventional self-report approaches, behavioural exploration-exploitation is measured using participants' activity in playing the game. For each tick, there exists a matrix representing the player's strategy in terms of the number of improvement-crop combinations in use at that point. These strategic instances are then compared to determine the extent to which a player is repeating a previous strategy (exploitation) or creating a new strategy (exploration) that can include combinations of previous strategies or alterations to them. A simplified example of how exploration is calculated in a game with five fields; crop varieties, a, b and c; and improvements, x and y, is shown in the matrix below.

𝑴 𝑡 = -𝑥 𝑦 𝑥𝑦 𝑎 1 0 0 0 𝑏 0 0 2 0 𝑐 0 0 0 1
This indicates that at t, there was a field with no improvements containing variety a, two fields with improvement y containing b, and one field with both improvements x and y containing c. This matrix Mt is then compared to all previous matrices by subtracting it from each matrix and summing the absolute values of the differences to derive a single value, d, representing the difference between both matrices. For example, the matrix at t -1:

𝑴 𝑡-1 =
-𝑥 𝑦 𝑥𝑦 𝑎 1 0 0 0 𝑏 0 0 1 0 𝑐 0 1 0 0 indicates that, at t, the player has added a b crop to a field with improvement y, along with the c crop in a field with improvements x and y. Meanwhile, a crop of c in a field with improvement x has harvested. Subtracting Mt from Mt-1 yields:

-𝑥 𝑦 𝑥𝑦 𝑎 0 0 0 0 𝑏 0 0 -1 0 𝑐 0 1 0 -1 the summed absolute values of which is d = 3. The exploration value for Mt is the lowest value of d obtained after iterating over all previous matrices, multiplied by 1 divided by the number of fields in the game at t to control for the expansion of fields as the game progresses. Therefore, in a game with 5 fields:

𝑒𝑥𝑝𝑙𝑜𝑟𝑎𝑡𝑖𝑜𝑛 = 𝑑 𝑚𝑖𝑛 1 𝑛 = 3 × .2 = .6
This value, calculated once at the end of each tick, provides an indication of the players' level of behavioural exploration at that point in the game. Taken together, these values provide a record of changes to the players' use of exploration as a strategy over time, with higher values at the beginning of the game suggesting a stronger preference for a doing first strategy.

Results Table 1 shows descriptive statistics and correlations for dispositional variables included in the analysis. Note that values of score are calculated as the square root of the in-game value to resolve issues related to differences in magnitude and normality that occurred during analysis. As expected, there were two broad phases in the game. The first of these occurs from tick 250 and extends to 550, when behavioural exploration increases while behavioural exploitation decreases as players extend their strategies to include more crops and improvements as they become available. Once behavioural exploration peaks at around 550, it then declines as successful strategies have been identified, and behavioural exploitation increases as these selected strategies are increasingly exploited.

Table 2 shows results from multilevel regression models, with score as the dependent variable, behavioural exploration as a level 1 predictor, and both openness to experience and its facets as level 2 predictors. As the study focuses on the influence of dispositional variables (level 2) on the slope between behavioural exploration (level 1) and score over time (10), slope values are presented for level 2 on score controlling for level 1 (01), and the moderation of level 2 on the slope between level 1 and score (11). Multilevel regression analysis shows that on average, there was a significant negative slope between behavioural exploration and score (10 = -5.10, p < .001) following the peak in exploration at 550 ticks. Openness to experience reduced the negative slope between behavioural exploration and score (11 = -4.63, p < .05) through the facets of adventurousness (11 = -3.83, p < .01) and liberalism (11 = -2.90, p < .05). However, when controlling for behavioural exploration, Emotionality was negatively related to score (01 = -4.58, p < .05), while Liberalism was positively related to score (01 = -6.42, p < .01).

Discussion

The aim of this study was to assess whether or not theoretical relationships between personality dispositions and behaviours associated with strategy formation could be examined using a game that simulates this in a dynamic environment, where initial knowledge is limited. However, results were mixed. We were expecting that higher levels of openness to experience facets intellect and adventurousness would positively moderate the negative relationship between behavioural exploration and score, while imagination, artistic interests, emotionality, and liberalism would not moderate the relationship between behavioural exploration and score. Although adventurousness moderated the negative relationship between behavioural exploration and score as expected, intellect did not, but liberalism did. This suggests that the ability to recognise successful strategies and curtail further exploration is not related to intellect, and thus the task is not as cognitively demanding as it might be in real-word contexts. However, intellect focuses on reading challenging material, philosophical discussions, and abstract ideas, and may not reflect the kind of pragmatic application of cognition required by this task.

For example, entrepreneurs, who tend to operate in environments with initial levels of low information, are more likely to exhibit lower levels of cognitive motivation and rely more on heuristics and advice from others when solving cognitive tasks [START_REF] Curseu | The psychology of entrepreneurial strategic decisions[END_REF]. This ability to make decisions in the absence of information is also related to entrepreneurs' higher levels of ambiguity tolerance, which may account for the role of liberalism in reducing the negative relationship between behavioural exploration and score. One surprising result was the significant relationship between liberalism and score after controlling for behavioural exploration, as expectations were that traits related to openness to experience would maintain higher levels of exploration at a time in the game when the focus should be shifting to exploitation in order to achieve a high score. However, this result suggests that liberalism is one of the more significant aspects of personality that contributes to success during this period of the game. Liberalism focuses on the rejection of moral absolutes and greater latitude in the use of punishment for crimes. Therefore, it may be that participants with a preference for ambiguity accurately sense that there is no right or wrong strategy to pursue, and are happy to assume this in exploring new strategies.

Conclusion

The current study demonstrates the advantages of using a game to examine behaviours associated with dispositional constructs over time in combination with multilevel modelling. However, it is often difficult to predict how players will respond to different, interacting features of a game, such that any data generated should inevitably lead to the further development and improvement of the game. This blurs the line between development and research, as an iterated development process, where successive versions of the game are produced based on feedback generated by the previous version [23], lends itself well to the successive gathering and analysis of data needed to establish validity. From this perspective, a game may never be truly finished, as further development can occur to improve validity, or to branch off into a different study. In order to engage players and examine more complex interactions between individuals and behaviours, games could incorporate greater functionality, which requires iterative testing for validity. Although the use of games in research is still in its early stages, these possibilities highlight their huge potential.

Fig. 1 .

 1 Fig. 1. A farmer explains how to access improvements in the Market Farmer tutorial.

Fig. 2 .

 2 Fig. 2. A screen in Market Farmer showing the selection of improvements available to the player, the advantages they confer, and the cost in farmhands.

Fig. 3 .

 3 Fig. 3. The screen in Market Farmer showing all of the crop varieties available to the player by the end of the game and their price volatility.

  ; OP = Openness to Experience; IM = Imagination; AI = Artistic Interests; EM = Emotionality; AD = Adventurousness; IN = Intellect; LB = Liberalism; M = Mean; SD = Standard Deviation

Table 1 .

 1 Descriptive statistics and correlations for dispositional variables.

		SC	OP	IM	AI	EM	AD	IN	LB
	SC
	OP	-.05
	IM	-.00	.70**
	AI	-.16	.68**	.39**
	EM	-.18	.61**	.46**	.26*
	AD	.06	.60**	.29**	.28**	.18
	IN	-.13	.63**	.27**	.39**	.25*	.25*
	LB	.25*	.47**	.15	.16	.02	.30**	.19
	M	85.23	3.42	3.57	3.54	3.68	2.94	3.54	3.26
	SD	35.21	0.49	0.82	0.86	0.85	0.76	0.77	0.73
	*p < .05, **p < .01
	SC = Score

Table 2 .

 2 Multilevel regression models with personality traits and facets at level 2, behavioural exploration as a level 1 independent variable, and score as the level 1 dependent variable.

	Level 1	10	Error
	Exploration	-5.10***	1.05
	Level 2	01	Error	11	Error
	Openness	-0.45	3.59	-4.30*	2.06
	Imagination	0.72	2.15	-1.32	1.28
	Artistic Interests	-1.93	2.06	-0.93	1.23
	Emotionality	-4.58*	2.02	-0.39	1.21
	Adventurousness	1.47	2.33	-3.83**	1.37
	Intellect	-1.19	2.29	-1.29	1.29
	Liberalism	6.42**	2.34	-2.90*	1.39
	*p < .05, **p < .01, ***p < .001

  decision-making: A cognitive perspective (pp. 41-67). Cheltenham, UK: Edward Elgar Publishing (2008). 23. Braad, E., Žavcer, G., & Sandovar, A.: Processes and models for serious game design and development. In R. Dorner, S. Gobel, M. Kickmeier-Rust, M. Masuch, & K. Zweig (Eds.), Entertainment computing and serious games (pp. 92-118). Cham, Switzerland: Springer International Publishing (2016).


Filipe M B Boaventura
email: fmbboaventura@gmail.com

Victor T Sarinho
email: vsarinho@uefs.br

A Feature-Based Approach to Develop Digital Board Games

Keywords: Feature modeling, Software product line, Board games

Several types of development strategies are available to provide digital games in a reusable way. However, the idea of a "one-size-fitsall" architecture for digital games can be problematic, being preferable to build dedicated architectures for specific game genres. This paper proposes the development of feature-based artifacts for the production of digital board games. It presents a subdomain game architecture that represents configurable features of core concepts related to board games (the game model and controller ), and implements feature artifacts capable of being executed in distinct game clients (the game view ). For validation purposes, two types of classic board games, together with a proposed web client for board games, were developed, consolidating as a result a software product line approach to develop classic board games.

Introduction

Game development is a complex task that requires technical mastery and integration skills from several areas of computer science [START_REF] Scacchi | Research challenges at the intersection of computer games and software engineering[END_REF]. In fact, many of the major traditional challenges on software engineering arise during the development of digital games as complex software systems, such as large-scale software engineering, game requirements engineering, game software design, and so on [START_REF] Scacchi | Research challenges at the intersection of computer games and software engineering[END_REF]. However, develop digital games is not the same as develop traditional software systems [START_REF] Furtado | Improving digital game development with software product lines[END_REF], which causes some specific problems during their production.

Regarding digital game development strategies, the reuse of specific components for game development, which can be provided or not by SDKs, frameworks and game engines [START_REF] Gregory | Game engine architecture[END_REF], can reduce the development time and cost [START_REF] Folmer | Component based game development: A solution to escalating costs and expanding deadlines?[END_REF] to provide desired games. However, reused game components may restrict or predetermine the types of digital games that can be developed [START_REF] Scacchi | Research challenges at the intersection of computer games and software engineering[END_REF], changing as a consequence the initial game design idea for adapting purposes, and providing as a result an "unwanted" game project for the game designer.

In this sense, one possible solution would be to separate the core of the game logic (the G-factor ) from the implementation resources, allowing a game portability between development environments to be reused in distinct game productions [START_REF] Binsubaih | Game portability using a service-oriented approach[END_REF]. However, the idea of a "one-size-fits-all" architecture for digital games can be problematic for many reasons, being preferable to build dedicated architectures for specific game genres [START_REF] Furtado | Improving digital game development with software product lines[END_REF].

This paper proposes the development of feature-based artifacts for the production of digital board games. The objective is to represent configurable features of core concepts related to board games (the game model and controller ), and implement feature artifacts able to execute configured features in distinct game clients (the game view ). As a result, a simple and scalable digital board game development approach is provided, which can be classified as a Software Product Line [START_REF] Northrop | Software product lines: reuse that makes business sense[END_REF] able to generates new games through configured board game features.

Related Work

Feature Modeling and Development

Features are aspects or characteristics of a domain that are visible to the user [START_REF] Kang | Feature-oriented domain analysis (foda) feasibility study[END_REF], which are used to identify similarities or differences between products in a product line [START_REF] Antkiewicz | Featureplugin: Feature modeling plug-in for eclipse[END_REF], and can be defined as a unit of functionality of a software system that meets a requirement [START_REF] Apel | An overview of feature-oriented software development[END_REF].

Kang et al. [START_REF] Kang | Feature-oriented domain analysis (foda) feasibility study[END_REF] introduced the concept of feature, originally presented as part of Feature-Oriented Domain Analysis (FODA). According to them [START_REF] Kang | Feature-oriented domain analysis (foda) feasibility study[END_REF], Feature Modeling is used to identify system properties during domain analysis, and the feature model represents standard features of a family of systems and the relationships between them [START_REF] Kang | Feature-oriented domain analysis (foda) feasibility study[END_REF].

Feature-Oriented Software Development (FOSD) [START_REF] Apel | An overview of feature-oriented software development[END_REF] is a paradigm for the construction, customization and synthesis of large-scale software systems in terms of features. According to Apel and Kästner [START_REF] Apel | An overview of feature-oriented software development[END_REF], the FOSD is not a single development method or technique but a combination of different ideas, methods, tools, languages, formalisms and theories, connected by the concept of feature.

FOSD and Digital Game Development

Regarding the use of feature based approaches in the development of digital games, Zhang and Jarzabek [START_REF] Zhang | Reuse without compromising performance: Industrial experience from rpg software product line for mobile devices[END_REF] proposed the RPG Product Line Architecture (RPG-PLA), a set of common and variable features where any of the four original RPGs, as well as other similar games, could be derived from the RPG-PLA.

Furtado et al. presented the ArcadEx Software Product Line, a SPL created for 2D arcade games [START_REF] Furtado | Sharpludus revisited: From ad hoc and monolithic digital game dsls to effectively customized dsm approaches[END_REF]. It is an improvement over the SharpLudus project [START_REF] Furtado | Using domain-specific modeling towards computer games development industrialization[END_REF], and it uses feature models to describe the commonality and variability of the 2D game domain.

Zualkernan proposed a Feature Modelling Framework for Ubiquitous Embodied Learning Games (UELG) [START_REF] Zualkernan | A feature modelling framework for ubiquitous embodied learning games[END_REF], which represents educational games where the players interact with an augmented physical environment. It is an approach designed with the intent of reducing the cost and shorten the development lifecycle of such ubiquitous systems, while including features regarding pedagogy and learning concepts [START_REF] Zualkernan | A feature modelling framework for ubiquitous embodied learning games[END_REF].

Finally, Sarinho et al. have been obtained some interesting results in the development of digital games by FODA and FOSD paradigms, such as reusable features focused on digital game concepts and implementation aspects (NESI [START_REF] Sarinho | A feature model proposal for computer games design[END_REF] and GDS [START_REF] Sarinho | A generative programming approach for game development[END_REF] models), SPL structures to provide digital games by feature configurations (FEnDiGa [START_REF] Sarinho | A feature-based environment for digital games[END_REF] and MEnDiGa [START_REF] Boaventura | Mendiga: A minimal engine for digital games[END_REF]), and feature development for specific game genres and categories (AsKME [START_REF] Sarinho | Askme: A feature-based approach to develop multiplatform quiz games[END_REF]).

Board Games

Board games are a specific group of games where figures are manipulated in a competitive game mode played over a surface according to predefined rules [START_REF] Bontchev | Educational quiz board games for adaptive e-learning[END_REF]. The expression board game is used to refer to games played on a table [START_REF] Duarte | Jogos de tabuleiro no design de jogos digitais[END_REF], even those without a board such as classic card games. Board games usually have a delimited surface divided into sectors where a set of pieces, associated with players by shape or color, are moved according to the events of the game [START_REF] Lucchese | Conceituação de jogos digitais[END_REF].

Objectives, rules, strategies and win conditions can be quite diverse on board games, such as conquering the most areas of the board, eliminating pieces of the opponents, achieving the highest score, or gathering the most of some form of currency [START_REF] Lucchese | Conceituação de jogos digitais[END_REF]. Regarding the various mechanics present in board games, the Board Game Geek (BGG) is one of the largest and most used forums dedicated to board games on the internet [START_REF] Kritz | Building an ontology of boardgame mechanics based on the boardgamegeek database and the mda framework[END_REF]. This site is dedicated to cataloging information about physical board games in order to maintain a database for posterity and historical research [START_REF] Boardgamegeek | Board game mechanics[END_REF]. Containing a listing of 99953 board games, 83 categories and 51 game mechanics [START_REF] Boardgamegeek | Board game mechanics[END_REF], the BGG database became a source of information widely used by game designers and researchers alike [START_REF] Kritz | Building an ontology of boardgame mechanics based on the boardgamegeek database and the mda framework[END_REF].

Board Game Models

Based on available information of the BGG database, Kritz, Mangeli, and Xexéo [START_REF] Kritz | Building an ontology of boardgame mechanics based on the boardgamegeek database and the mda framework[END_REF] proposed a board game mechanics ontology, following the concept of mechanics presented in the MDA (Mechanics, Dynamics e Aesthetics) Framework [START_REF] Hunicke | Mda: A formal approach to game design and game research[END_REF]. MDA is a formal approach to analyzing games, built with the goal of understanding the concepts that help designers, researchers and scholars to perform the decomposition of games into coherent and understandable parts [START_REF] Hunicke | Mda: A formal approach to game design and game research[END_REF].

Thus, the proposed ontology organizes several board game mechanics [START_REF] Boardgamegeek | Board game mechanics[END_REF] into two main concepts: Algorithm mechanics and Data Representation mechanics. Algorithm is the general mechanic for the processes that take place in the game and is subdivided into: Action -a set of mechanics that allow the user to interact with the game; Ruleset -mechanics that define, among other aspects, the behavior of the components of the game; and Goal -mechanics that define the various goals to be achieved during the game, such as win conditions or transitory goals. Data Representation is the general mechanic for storing and conveying information in games and is divided into: Component -representing the elements of the game that the players can own and manipulate directly; and Resource -representing the elements of the game that the player must manage to fulfill goals.

Finally, the Quiz Board Game Model proposed a formal model to represent quizzes as board games with multimedia appeal [START_REF] Bontchev | Educational quiz board games for adaptive e-learning[END_REF]. It describes board games as a set containing: objects, positions, functions, actions, rules and effects. The game is presented on a background image, where objects of various types are arranged in positions according to a matrix that describes their initial layout. Quiz questions are distributed according to board game positions, and are presented to the player through multimedia assets as objects reach these positions.

Metodology

As three main stages of this work: 1) a feature model was proposed to represent features for the board game domain; 2) a development approach was defined to describe, configure and interpret the proposed board game features; and 3) a board game client was implemented in order to execute configured features.

Features for Board Games

Feature Models [START_REF] Kang | Feature-oriented domain analysis (foda) feasibility study[END_REF] are used to describe the relationships and dependencies of a set of features belonging to a specific domain [START_REF] Apel | An overview of feature-oriented software development[END_REF]. For this work, a feature model was proposed to describe generic software components for the production of digital board games. These features were designed to represent families of board games according to board game concepts found in literature [START_REF] Boardgamegeek | Board game mechanics[END_REF][START_REF] Bontchev | Educational quiz board games for adaptive e-learning[END_REF][START_REF] Kritz | Building an ontology of boardgame mechanics based on the boardgamegeek database and the mda framework[END_REF].

The proposed model presents 88 features responsible for describing a Board Game, such as the game Id, the game logic as a whole (Game Flow ) and the information that is handled during a gameplay (Game Data). Game Flow is represented in terms of Actions, Rules and Game Events, while Game Data describes the Component Options, the Board Options and the Player Options of the game. A complete diagram of all features proposed by this paper can be found at https://github.com/lenda-uefs/BoardGameEngine.

Regarding the Game Data subfeatures, Component Options represents the configuration of the objects that can be manipulated by the players during matches [START_REF] Kritz | Building an ontology of boardgame mechanics based on the boardgamegeek database and the mda framework[END_REF], such as Dice or Tokens of different types (Token Type) (Fig. 1). A Token Type is identified by a Type Id, which is referenced by a Token, and it configures the visual representation of the Token by mapping a Player Id to a Image File. Tokens are positioned on the board according to their Position Id. Dice appear in two distinct types: the N-sided Die represents a common dice, containing a sequence of numbers from 1 to N; and the Special Die can be used by the Game Designer to represent custom dice, containing a list of custom values (Value Set). Components can belong to a specific player, identified by feature Owner Id.

Still on Game Data (Fig. 2), the Board Options feature represents the game board, which consists of a Background Image and a set of Positions which can Regarding the Game Flow subfeatures (Fig. 3), the Action feature represents the set of actions that can be performed during the game. Each Action Type is presented to the player by an Action Label, which contains a textual description of the Action to be performed. By these actions, the player is able to change the state of the game, manipulating Components and/or Player Attributes. For example, through the feature Roll Dice, the player is able to interact with the Component Dice to generate random numbers. Select Token and Select Position allow the player to mark a Token or a Position respectively. End Turn allows the player to end his movement, giving the turn to the next player. Finally, the feature Move Token is used to move Tokens between Positions, according to the rules that will be discussed ahead.

Performing actions and evaluating rules can trigger several Game Events. Currently, these events include: Dice Event -triggered when a value is retrieved from a dice roll; Passing Event and Stopping Event -which are triggered when a Token, respectively, passes through or stops on a Position; End Turn and End Game -respectively triggered when a turn or game is completed; Token Eliminated -triggered when the player loses a Token; and Token Selecttriggered when the player selects a Token.

Rule features are responsible for evaluating the execution possibility of Action features according to the current state of the game. It also defines the configuration of other aspects of the game, such as Token Movement, the player succession policies throughout the turns (Turn Options), and the Conditions to Win the game.

Regarding turn related rules, Turn Options describes the game settings according to the maximum number of turns (Max Turn Count), the Player Order and the Action Queue to be performed during a turn. The succession of players can happen in a Static Order, which means player 3 will always play after player 2, player 2 after player 1 and so on, or it may vary according to some event (Dynamic Order ). For example, a game may give the turn to a player who has just finished his turn if he or she has fulfilled any special conditions.

For the Movement rules, they are directly affected by the Board Type and the organization of the Positions on the board. In Point to Point boards, where the Positions are organized as a trail of interconnected points, the Tokens can only move to the Next Position adjacent to their current position. This type of movement is known as Point to Point Movement [START_REF] Boardgamegeek | Board game mechanics[END_REF][START_REF] Kritz | Building an ontology of boardgame mechanics based on the boardgamegeek database and the mda framework[END_REF]. If the current position of the Token refers to more than one adjacent position, it is necessary to decide which position the Token should be moved to. This decision is configured by the feature Path Selection Rule, which can be: Manual, delegating to the player the task of selecting the desired position; or Automatic, where a provided function decides the Token location that must be sent.

Regarding the Position Selection Rule, it represents an evaluator to be provided by the game designer to validate a Position selected by the player. This method is used in games that use the Grid Board Type, since the Positions will not hold information on which adjacent Positions the Tokens can move to.

Finally, Condition to Win represents a collection of the winning conditions of the game, as well as their evaluation settings. From the currently available conditions, Reached Position refers to the act of moving a Token to a position with a specific Position Type. Player Attribute Value and Number of Remaining Tokens are conditions related to numerical values and they refer respectively to: a Player Attribute, identified by an Attribute Name; or the remaining amount of Tokens of a certain Token Type. These conditions can be evaluated for the Highest value, Lowest value or compared with a Exact value, as described by the feature Evaluation Option. Finally, the feature Evaluation Event defines the moment when the evaluation of the conditions is performed. The conditions can be evaluated for every update of the game state (On Game Update) or only once per turn (On Turn End ). In both cases, the game ends if the winner is found. If the game ends without a winner, such as the maximum number of turns was reached, the end game victory conditions (On Game End ) are evaluated to decide the winner.

Developing Feature Based Board Games

Once the proposed feature model is defined, it is necessary to define a development approach for the implementation of configured board games. Several FOSD [START_REF] Apel | An overview of feature-oriented software development[END_REF] approaches have been used successfully for the development of digital games, such as generative programming [START_REF] Sarinho | A generative programming approach for game development[END_REF] and SPL instance [START_REF] Furtado | Improving digital game development with software product lines[END_REF]. For this work, the game development approach consist of the definition of a Domain-Specific Language (DSL) for the description and configuration of the proposed board game features, as well as a generic game loop able to interpret and run the board game configurations.

In this sense, by the proposed feature model, a DSL was defined using JavaScript Object Notation (JSON ), where each feature was used to represent the numeric, textual, and procedural values involved in the configuration of a digital board game. Fig. 4 illustrates an example of a partial configuration of the Game Flow feature and its subfeatures Actions, Rules and Game Events.

In order to provide a game loop capable of running the proposed features, an interpreter has been developed using Javascript for the purpose of reading and executing game actions and events, and to control the game flow defined in a given JSON configuration file. The interpreter follows the AsKME strategy for initializing and updating configured games, using the startGameStatus and updateGameStatus functions [START_REF] Sarinho | Askme: A feature-based approach to develop multiplatform quiz games[END_REF]. The updateGameStatus function performs the actions of the game, following the order queued in Action Queue (Fig. 4) and triggering the appropriate Game Events when necessary. Each performed action is removed from the queue, and, when there are no more actions to take, the turn ends, the action queue is restored and the game identifies the next player.

The Board Game Client

As a presentation layer of configured board game features, a web client (Fig. 5) was initially implemented using Phaser, an open source framework for browser games based on HTML5 Canvas and WebGL. The user interface is divided into three main areas. The left sidebar displays the values of the current Player Attributes, as well as its illustrative icon and textual description (displayed on a mouse over event). The central area displays, among other information, the game board, drawn by Phaser on a Canvas with a resolution of 800x600 pixels. Finally, the right sidebar displays the available Actions and feedback messages for the player. User inputs are obtained through click events on hyperlinks on the right sidebar, on the Tokens, or on the Positions of the board. These events execute the updateGameStatus function of the interpreter which in turn executes the appropriate Action according to the current state of the game. After running the updateGameStatus, the interpreter notifies the client to update the current View with the new values from the current game state.

Results and Discussion

For the validation of the proposed board game features, two digital versions of classic board games were implemented (Fig. 5). For the Grid board, a digital version of the popular game Checkers was developed. This version is played by two players on an 8x8 grid, each starting with 12 Tokens. Regular Tokens (called men) can only move and capture enemy Tokens forward. Special Tokens (called Kings) can both capture and move backwards. Captures are mandatory and can be done in sequence, if possible. The winner is the player who captures all enemy Tokens.

For the Point to Point board, a digital version of the Ludo [START_REF] Boardgamegeek | Pachisi -board game[END_REF] was implemented. In this game, the players must guide their Tokens from their bases to the finish line on the center of the board. The movement is controlled by a simple six-sided die that determines how many positions a selected Token should move. However, Tokens can only move from their starting positions if the Roll Dice Action returns a one or six value. Also, if a player roll a six, it gets another dice roll. And if the player roll a six for the third time in a row, the turn ends. If a token stops at a position that contains an opposing token, the latter is "captured" and sent back to its base. If the token occupying the position is not from the opponent, the movement is undone and the turn ends.

Considering the reuse level achieved by FOSD usage in the developed games, some code metrics [START_REF] Frakes | Software reuse: metrics and models[END_REF] were collected using the Plato code analyzer [START_REF]Plato: Javascript source code visualization, static analysis, and complexity tool[END_REF] for the amount of reused code and the cyclomatic complexity. The Amount of Reuse metric is used to evaluate and monitor the reuse improvement effort by tracking percentages of reuse of lifecycle objects over time [START_REF] Frakes | Software reuse: metrics and models[END_REF], which can be defined as the number of reused SLOC (Source Lines of Code) of a given module divided by the total SLOC of the system. The Cyclomatic Complexity (CC ) is used to measure the complexity of the code based on the number of execution flows in the source code [START_REF] Mccabe | A complexity measure[END_REF].

Table 1 shows the obtained metrics for the developed games, where the Reused SLOC and the CC were calculated by the sum of the respective SLOC and CC values for the Phaser client, the JSON interpreter and the JSON configuration file. As a result, 76,32% of SLOC reuse and 79,93% of CC reuse, on average, were obtained from the developed artifacts, confirming the game core reusability and mantainability for the developed board games.

Conclusions and Future Work

This paper presented a feature-based approach for the development of digital board games. It is based on a previous development effort to provide a crossplatform product line architecture for the quiz game domain [START_REF] Sarinho | Askme: A feature-based approach to develop multiplatform quiz games[END_REF], but in this case providing board game features and artifacts able to manage the game logic represented as a game core of simple board games.

To achieve this objective, a feature model has been defined to represent relevant concepts of the board game domain, along with software artifacts capable of configuring and executing the board game dynamics in a web client. In fact, once the components of a desired board game are identified and configured through the proposed feature model, these can be executed by the implemented artifacts in a client application developed for a specific platform, providing as a result new board games for each new configured JSON file.

Regarding the Javascript interpreter for board game features, it provides an interface to retrieve and modify the state of the board game described by the JSON file. The client application, then, is responsible for querying the interpreter about the information needed and to use the obtained information to present the game using its own components. As a result, the Javascript interpreter works as a game controller facade based on features that communicates with developed cross-platform client applications, such as web, mobile platforms, game engines (Unity3D, Godot) and Arduino (Johnny-Five framework), in order to provide configured board games [START_REF] Sarinho | Askme: A feature-based approach to develop multiplatform quiz games[END_REF].

Digital versions of the games Ludo and Checkers were also presented in this work. It is an important verification/validation step of this project as an attempt to show the feasibility of the reusable artifacts to produce digital versions of classic board games. Furthermore, the obtained metrics for the developed software artifacts (SLOC reuse and cyclomatic complexity) presented satisfactory results, with over 76% SLOC reuse and 79% complexity reuse. However, the amount of Javascript functions that need to be programmed in order to configure board game dynamics using the proposed model can compromise the maintainability and scalability of the developed games. Thus, it is necessary to further identify common behaviors between board games in order to build a more detailed board game feature hierarchy. It is also need to develop more games using the proposed artifacts to perform an improved benchmarking of metrics, as well as a better evaluation of their reuse and expansion capabilities.

As future work, it is necessary to expand the proposed feature model to cover more board game mechanics such as trading and collecting cards, for example. The development of new board game clients, such as mobile, Unity3D and embedded systems, along with the support for multiplayer matches, will also be performed in the future, in order to provide a multiplayer and multiplatform SPL for the development of digital board games.

Fig. 1 .

 1 Fig. 1. Component Options subfeatures.

Fig. 2 .

 2 Fig. 2. Partial feature diagram for the Game Data feature.

Finally,

  Player Options represent the game settings regarding the number of players (Player Count), their identifiers (Player Id ) and their attributes (Player Attribute). Player Attribute represents a list containing the various types of resources that players can accumulate during the game, such as score points and values to be used as currency. These resources are represented by Name, Value, a textual description (Description) and an illustrative image (Image).

Fig. 3 .

 3 Fig. 3. Partial feature diagram for the Game Flow feature.

Fig. 4 .

 4 Fig. 4. JSON representation of a partial configuration of a board game.

Fig. 5 .

 5 Fig. 5. Two screenshots of the Phaser client running Ludo and Checkers.

Table 1 .

 1 Obtained SLOC and CC metrics for Ludo and Checkers games.

	Game Name Reused SLOC / Total SLOC Amount of CC / Total CC
	Ludo	948/(315+948) = 75,06%	120/(23+120) = 83,91%
	Checkers	948/(274+948) = 77,58%	120/(38+120) = 75,94%


Andrew J A Seyderhelm
email: andrew.seyderhelm@uon.edu.au

Karen L Blackmore

Keith Nesbitt

Towards Cognitive Adaptive Serious Games: A Conceptual Framework

Keywords: serious games, conceptual model, cognitive load, adaptive

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Overview

Serious games are used for many purposes; throughout this paper reference to serious games will adopt a definition specific to the purposes of education and training: "A serious game is an experience designed using game mechanics and game thinking to educate individuals in a specific content domain" [START_REF] Kapp | The gamification of learning and instruction: game-based methods and strategies for training and education[END_REF], page 15) [START_REF] Kapp | The gamification of learning and instruction: game-based methods and strategies for training and education[END_REF]. It has been suggested that serious games provide a number of advantages over traditional learning or instructional approaches, including flexibility, creative problem solving, greater engagement and enjoyment in the material [START_REF] Connolly | A systematic literature review of empirical evidence on computer games and serious games[END_REF][START_REF] Hamari | Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning[END_REF] and the ability to produce metrics that are valuable to debriefing learners and informing ongoing serious game development [START_REF] Loh | Serious games analytics: Theoretical framework[END_REF][START_REF] Crookall | Serious games, debriefing, and simulation/gaming as a discipline[END_REF]. However, many serious games fail to leverage their full advantages, and this is particularly true in respect of adaptive mechanisms and dynamic difficulty adaption (DDA). Adaptive training in serious games has the benefit of being a cost-effective method of providing training that closely approximates one-on-one tutoring and increases the overall effectiveness of training [START_REF] Landsberg | Review of Adaptive Training System Techniques[END_REF]. Traditional games have explored DDA in 3D games for well over a decade, and have demonstrated the efficacy of these systems on player enjoyment and engagement [START_REF] Hunicke | The case for dynamic difficulty adjustment in games[END_REF][START_REF] Xue | Dynamic difficulty adjustment for maximized engagement in digital games[END_REF]. Serious games have an additional purpose to provide a learning outcome for players. Thus, the mechanisms for adaption may need to be different as the foci is on learning outcomes rather than purely entertainment, although enjoyment and engagement are crucial components of effective learning.

A range of approaches have the potential for being useful for adapting serious games. This includes, but is not limited to, inventory and pick-up adaption [START_REF] Hunicke | The case for dynamic difficulty adjustment in games[END_REF], pedagogical agents [START_REF] Moreno | Interactive Multimodal Learning Environments[END_REF], and a wide range of AI implementations. Serious games that adjust the challenge level in-line with the growing capability and knowledge of the learner are ideal for maintaining engagement, motivation and learning outcomes [START_REF] Hamari | Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning[END_REF]. In this paper, we discuss DDA for adapting serious games, and extend this to consider approaches for measuring cognitive load that could be used to drive the adaptive mechanism in serious games. From this, we present a conceptual framework for cognitive adaptive serious games based on cognitive load theory and discuss future research plans to develop and assess this approach.

2

Core Components for Cognitive Adaptive Serious Games

Dynamic Difficulty Adjustment and Adaptive techniques

The purpose of an adaptive framework is to assess the current state of the individual learner and adjust the training to better suit their needs within the constraints of the training requirements. In a commercial video games context, there has been significant research into developing systems to implement DDA [START_REF] Zohaib | Dynamic Difficulty Adjustment (DDA) in Computer Games: A Review[END_REF][START_REF] Dziedzic | Approaches to Measuring the Difficulty of Games in Dynamic Difficulty Adjustment Systems[END_REF]. The aim of DDA is to provide the player with the correct level of challenge in order to make the game neither too hard nor too easy, thus increasing enjoyment [START_REF] Alexander | An investigation of the effects of game difficulty on player enjoyment[END_REF][START_REF] Dziedzic | Approaches to Measuring the Difficulty of Games in Dynamic Difficulty Adjustment Systems[END_REF]. This concept of optimal challenge for the purpose of enjoyment is implemented with the aim of assisting the player achieve a flow state [START_REF] Burns | Detecting flow in games using facial expressions[END_REF][START_REF] Dziedzic | Approaches to Measuring the Difficulty of Games in Dynamic Difficulty Adjustment Systems[END_REF]. Ideally, the benefit of DDA is that it will continuously adapt to player skill in an appropriate and subtle manner. As previously indicated, the primary aim of a serious game is to deliver instructional, learning, or development outcomes. As a result, DDA in this context needs to relate to the performance of the participant against the serious games purpose rather than solely to enhance entertainment through amplification of flow states. Our conceptual model adopts a three-part framework towards engagement that encompasses flow along with other aspects suited to serious games [START_REF] Hookham | A Systematic Review of the Definition and Measurement of Engagement in Serious Games[END_REF]. One of the crucial aspects we consider is the role of cognitive load in the learning process, acknowledging the role of cognitive load levels in providing insights into schema integration amongst learners [START_REF] Greitzer | Cognitive science implications for enhancing training effectiveness in a serious gaming context[END_REF].

Measuring Cognitive Load

Cognitive load is the degree to which a learning task meets, exceeds, or fails to reach the processing capacity of a learner's cognitive system [START_REF] Mayer | Nine ways to reduce cognitive load in multimedia learning[END_REF]. This is explained through Cognitive load theory (CLT), which originated in the 1980s [START_REF] Paas | Cognitive load theory: New conceptualizations, specifications, and integrated research perspectives[END_REF]. It is a widely accepted concept that describes three states of the cognitive processes involved in learning; intrinsic, extraneous and germane cognitive load [START_REF] Paas | Cognitive load theory and instructional design: Recent developments[END_REF].There are a number of methods of measuring cognitive load, principally subjective and objective measures. Subjective measures are typically completed by participants after undertaking an activity, for example the NASA Task Load Index (NASA-TLX) [START_REF] Hart | Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research[END_REF][START_REF] Hart | NASA-task load index (NASA-TLX); 20 years later[END_REF]. In contrast, objective measures are undertaken during an activity, measuring an observable occurrence, affect, or physiological system, and interpreting that data for cognitive load.

The conceptual framework detailed in this paper proposes the use of an adapted version of the detection-response task (DRT) [START_REF] Harbluk | Detection response tasks: Using remote, headmounted and Tactile signals to assess cognitive demand while driving[END_REF] embedded within a serious game. DRT has been chosen as it is registered through the International Organization for Standardization in ISO 17488:2016 as a proven and effective measure [START_REF] Stojmenova | Detection-Response Task-Uses and Limitations[END_REF]. A virtual DRT, termed the "Remote DRT", has been previously tested in-simulation, and proven to be effective [START_REF] Harbluk | Detection response tasks: Using remote, headmounted and Tactile signals to assess cognitive demand while driving[END_REF]. An updated version of the "Remote DRT" is proposed here, potentially making a cognitive load based adaption an accurate, effective and affordable method for wide scale adoption.

Adapting Serious Games Through the Frame of CLT

CLT provides three aspects of cognitive load that can be manipulated to establish a sophisticated and theoretically sound adaptive framework. A practical description of how these adaptions may be implemented serious game tasks is outlined in Table 1. Alter the task complexity [START_REF] Liu | Task complexity: A review and conceptualization framework[END_REF] by increasing or decreasing the number steps at each stage e.g. making a car automatic rather than manual until the driver has grasped steering etc. Extraneous Cognitive Load (presentation of material)

The way material is presented can lead to an increase, or decrease, in extraneous load, e.g. light or weather effects may increase or decrease cognitive load.

Germane Cognitive Load (development of schemas)

The introduction of a "pedagogical agent" who assists the student may assist the learning process [START_REF] Moreno | Interactive Multimodal Learning Environments[END_REF].

Using CLT as a framework provides a structured way for a combination of cognitive load and various performance metrics to be used to dynamically adapt a serious game to optimize learning.

Conceptual Framework

The conceptual framework detailed here is an extension of the serious games conceptual model proposed by Yusoff et al (2009) [START_REF] Yusoff | A conceptual framework for serious games[END_REF]. However, the Yusoff et al model does not incorporate learner motivation, affect, and prior knowledge as described in the cognitive-affective theory of learning with media (CATLM) [START_REF] Moreno | Interactive Multimodal Learning Environments[END_REF]. Yusoff et al define a range of factors, and the conceptual model outlined below adopts their definitions and adds the following components by drawing on CATLM and others [START_REF] Annetta | The "I's" have it: A framework for serious educational game design[END_REF]:

1. Learner Knowledge & Experience -understanding the learner's current state of intrinsic motivation, knowledge and experience is essential [START_REF] Landsberg | Review of Adaptive Training System Techniques[END_REF]. It is important to assess the player's ability with the serious game controls to avoid the learner struggling with the controls, rather than the learning content within it. 2. Review, Iterate -post-game review otherwise termed after-action review. This is critical to the learning process and is often underutilized in serious games [START_REF] Crookall | Serious games, debriefing, and simulation/gaming as a discipline[END_REF][START_REF] Loh | Serious games analytics: Theoretical framework[END_REF]. It is important for the facilitator to assess the performance of the serious game itself. 3. Play Game -forms the start/finish of the cognitive and performance measure loop.

Cognitive load and performance measures -a constant loop driven by the cognitive

measure. This process includes a performance measure, this is separate to the cognitive measure loop however they are combined to inform the DDA mechanism. 5. Feedback-is similar to Reflection described by Yusoff et al [START_REF] Yusoff | A conceptual framework for serious games[END_REF] and by Moreno and Mayer [START_REF] Moreno | Interactive Multimodal Learning Environments[END_REF]; this is in-game feedback to help and inform the player. 6. DDA +/--This is the point at which an adaption mechanism is implemented making the game easier, harder or the same as described in table 2. 7. Game Performance -outputs the player performance and cognitive load measures to inform the debrief process and other course requirements [START_REF] Crookall | Serious games, debriefing, and simulation/gaming as a discipline[END_REF][START_REF] Loh | Serious games analytics: Theoretical framework[END_REF]. DDA-Player is struggling, make it easier and reduce complexity.

Table 2 presents the various proposed DDA adaptions including concepts discussed in the preceding section (Section 2.3). This follows on from the identification of how difficulty may be adjusted (Table 1). Together, the existing serious game conceptual models, extended to incorporate the inclusion of an adaptive mechanism based on cognitive load, results in a new Cognitive Adaptive Serious Game Framework (CASG-F) (Figure 1). Performance measures and cognitive load will work together to adapt the in-game tasks. However cognitive load will form an adaption mechanism in its own right, e.g. making a simulated car automatic instead of manual to reduce interaction elements (intrinsic cognitive load), or increasing challenge by introducing additional tasks (intrinsic) or weather effects (extraneous cognitive load).

4

Future Research

The CASG-F proposed here has three broad stages in order to encompass instructional design concepts [START_REF] Keller | Development and use of the ARCS model of instructional design[END_REF] and the triadic theoretical framework [START_REF] Rooney | A theoretical framework for serious game design: exploring pedagogy, play and fidelity and their implications for the design process[END_REF]. The CASG-F provides a roadmap for future experiments to incorporate these principles as best practice, and to outline how they are being integrated into the design and development process. The overarching aim of this research is to create an adaptive framework that can work flexibly and effectively using cognitive load as a key adaption measure. Moving forward, robust experimental studies to validate the proposed adaptive process, and also to provide efficacy for the approach, are planned. Firstly, an experimental study is required to assess the in-game use of the DRT cognitive load measure, and to further validate its authenticity using a triangulation approach with an EEG combined with the NASA-TLX. The first experiment enables statistical validation of the affect different interventions have on the participants' cognitive load, providing a 'toolbox' of adaptive techniques for future experiments. Later experimental studies will then be required to compare adaptive serious game performance to non-adaptive variants in order to corroborate the assumption that adaptive serious games are more effective in enhancing human performance. The first experiment involves a driving task in a 3D game environment. This has the benefit of using a task where the real-world efficacy of the DRT has already been established [START_REF] Harbluk | Detection response tasks: Using remote, headmounted and Tactile signals to assess cognitive demand while driving[END_REF]. The proposed experiment will:

• Present three levels with the same driving track layout; the surrounding virtual environment will be different in each layout minimizing repetition for participants, and also provide information on how different visual environments affect cognitive and visual load. Using a randomized approach, participants will drive two loops of the track; one loop will have the DRT and the other will not, the EEG will remain on for both loops. • The levels will include challenge sections, for example navigating a narrow section of road, observation tasks, following a vehicle, and more. • Qualities of the visual environment will be manipulated in order to observe how these changes relate to visual load and cognitive load, e.g. altering in-game weather and lighting. Quantifying the effect of visual environment manipulations on cognitive load will then further contribute to the adaption 'toolbox'.

A study following this design will facilitate evaluation of the effect of the DRT itself on the cognitive load of the player participants. Experimental designs based on the CASG-F should also consider the layering of primary and secondary tasks to allow for robust measurement of cognitive load using the DRT approach [START_REF] Conti | An assessment of cognitive workload using detection response tasks. Advances in human factors and ergonomics series[END_REF]. To this end, the player should be given an additional task to perform requiring different cognitive processes. For example, asking the participant to count or respond to certain assets in the environment, requiring visual and cognitive discrimination, will effectively alter cognitive load [START_REF] Conti | An assessment of cognitive workload using detection response tasks. Advances in human factors and ergonomics series[END_REF].Together, the CASG-F and the proposed experimental design provides an authentic first step toward a generalizable framework for developing serious games that respond dynamically to improve learning outcomes.

Conclusion

In this paper we propose a framework to realize the value of DDA and cognitive measures as applied to serious games. We then discussed how an adaptive framework can be applied through the lens of CLT. Extending existing theoretical frameworks, a conceptual model for Cognitive Adaptive Serious Games was presented that incorporates and recognizes different learning design approaches. Additionally, the proposed model has the potential to deliver real-time personalized training environments to move serious game implementations closer to the overall aim of enhancing human performance. Lastly, a comprehensive experimental design for future studies was presented where in-game cognitive load will be measured and verified by a variety of methods against a range of stimuli. This experiment design provides a roadmap for validating the real-time DRT as a cognitive load measure, and to establish a toolbox of validated methods to manipulate cognitive load based on a robust understanding of the impact of different game mechanics and mechanisms on the cognitive load of end users. Once established, this toolbox can then be applied to serious game implementations to assess the efficacy of adaptive serious games for education and training.

Fig. 1 .

 1 Fig. 1. The Cognitive Adaptive Serious Game Framework (CASG-F)

Table 1 .

 1 Adaptive examples tailored to CLT

	Cognitive Load Element	Method for adjustment
	Intrinsic Cognitive Load
	(interaction elements)

Table 2 .

 2 Performance and Cognitive Load (CL) Adaption Template

	Achievement	Adaption	Description
	Pass assessment with low CL.	DDA increase (+)	Player is finding the task easy, so increase the challenge.
	Pass assessment with mid CL.	DDA static	Player is in the correct level of difficulty. No change.
	Pass assessment with High CL.	DDA static	Player is passing but finding it hard, provide in-game feedback.
	Fail assessment with low CL.	DDA Static	Player has failed but not being mentally challenged, in-game feedback.
			Cognitive load is ideal but failed
	Fail assessment with mid CL.	DDA -	assessment, indicating a lack of knowledge, reduce the difficulty
			slightly and add a hint.
	Fail assessment with
	high CL.


Bruno C Matias
email: brunoclaudinomatias@gmail.com

Victória O Gomes
email: victoria.oliveiragomes@gmail.com

Victor T Sarinho
email: vsarinho@uefs.br

"Ultimate Food Defense": A Serious Game for Healthy Eating Behavior Awareness

Keywords: Tower defense, Healthy eating, Blood glucose level

Taking into account the importance of developing a healthy eating behavior, which starts in childhood, this work presents the Ultimate Food Defense. It is a tower defense digital game designed to help players acquire healthy eating habits. As a result, a playful and entertaining approach has been obtained that can present the harm caused by fatty foods when consumed in excess.

Introduction

The learning of eating behavior is a process that already begins in childhood of an individual. Thus, the family and sociocultural context have an indispensable role in the process of structuring the child eating habits [START_REF] Aparício | Ajudar a desenvolver hábitos alimentares saudáveis na infância[END_REF].

One of the main ways of accessing technology for children is digital games [START_REF] Gros | The impact of digital games in education[END_REF]. Those can be defined as "attractive and interactive environments that captures the player attention, offering challenges that require increasing level of dexterity and skill" [START_REF] Savi | Jogos digitais educacionais: benefícios e desafios[END_REF]. In addition, serious games are often characterizes as games that the main purpose is to transmit knowledge [START_REF] Michael | Serious games: Games that educate, train, and inform[END_REF], something that represents a window of opportunity for the insertion of fun, pleasant and enjoyable elements to play. This paper presents the Ultimate Food Defense, a digital game where the player assumes the role of a defense cell to protects the body against caloric and greasy food, being helped by health food available for consumption.

Related Work

Tower defence is a strategy game subgenre whose objective is prevent that the enemy crosses the map and arrives in a specific reference spot in the game. As an example of tower defense game in health thematic, Smile Battle [START_REF] Rodrigues | Batalha do sorriso: Processo de desenvolvimento de um jogo sério para a saúde bucal[END_REF] is a game developed with the purpose of transmitting oral health knowledge, where the player has the duty of defend its mouth of the poor hygiene bacterium's attack.

Another tower defense for health game is the DigesTower, designed to help prevent childhood obesity [START_REF] Dias | Digestower: serious game como estratégia para prevenção e enfrentamento da obesidade infantil[END_REF]. The scenario is the Elise digestive system, who is the main character in the game, where the foods are the "enemies" and the digestives enzymes represents the player tower defense.

The Ultimate Food Defense Game

The game main menu (Fig. 1) was designed as a child friendly representation of a sales stall containing healthy fruits to eat. For the menu buttons, five options are provided to the player: New Game, Load Game, Instructions, Options and Credits. When starting a new game, the players will see a human mouth, the greasy and caloric food that advances into our organism, and a barrier in the end of the tong that must be defended by the player to avoid health problems caused by bad feeding (Fig. 2). To defend the body against the enemies and prevent blood glucose increase, there is a defense cell controlled by the player that attempts to avoid the advance of the bad food through the throat by shooting them. Some healthy food were also chosen to help the player during the game play. The strawberry for example helps in the blood glucose control [START_REF] Giampieri | The strawberry: composition, nutritional quality, and impact on human health[END_REF], as it has a substance called anthocyanin in its composition. For this reason, in the game, this fruit helps the player to decrease the organism blood glucose level in 2mg/dL. The grape impact in blood glucose level happens because, besides the anthocyanin presence, there is the resveratrol existence. This substance promotes a blood glucose drop, improving cells sensibility to insulin [START_REF] Zunino | Type 2 diabetes and glycemic response to grapes or grape products[END_REF], increasing as a result the defense cell velocity attack to the unhealthy foods in the game. Finally, the lemon fruit has a substance called pectin, a soluble fiber that, besides help in the digestion, it decelerates the absorption of sugar in the body [START_REF] Jenkins | Dietary fiber and the glycemic response[END_REF]. For this reason, in the game, this fruit makes the enemies attacks slow down. To maintain a pleasant game, free use of fruits is not allowed during the game. To control it, the game has a coin (the vitamins), which are received after certain regular intervals of time or when an enemy is defeated. When the player does not have enough vitamins to buy fruits, they remains blocked. About the caloric and greasy game enemies, Pizza, Soda and Hamburger (Fig. 3) were chosen because they are commonly found in fast-food stores and, therefore, consumed in large scale. When the enemies get as closer as possible to the defense cell tower, they start to attack the wall that defends the organism, and the attacks make the blood glucose increase gradually. For every enemy attack against the wall, the blood glucose increases in 2 mg/dL.

The player blood glucose level always starts in 50 mg/dL. Considering that the blood glucose values comprehended between 100 mg/dL and 125 mg/dL are already considered a pre-diabetes indicative [START_REF] Millani | Humanização e acolhimento no instituto da criança com diabetes/hospital-dia[END_REF], the player will loose the match if he were unable to conserve your blood glucose level at a value lower than 99 mg/dL. If this happens, the end game screen will be displayed to the player, which will have the option of try again or go back to the initial menu.

Conclusions and Future Work

This paper presented the Ultimate Food Defense, a digital game for the awareness of the healthy eating importance. For this, a focus has been placed on the body glucose level, giving the player the possibility to verify the impact that healthy and unhealthy foods have on the sugar in our blood.

Regarding the game metaphor, besides the cartoon design to encourage the consumption of healthy foods, the tower defense style reinforce the long-term incentive for good nutrition. Its is possible to observe as the player must keep avoiding the excessive consumption of caloric and greasy foods to cross the barrier, together with the consume of nutritious food to balance the body glycemic level.

As future works, more game levels will be provided, along with the addiction of new enemies and nutritious food. It is also planned to perform a game usability test to identify possible aspects that can be improved, making the game more effective and attractive as possible.

Fig. 1 .

 1 Fig. 1. Initial screen of the game.

Fig. 2 .

 2 Fig. 2. Ultimate Food Defense game play scene.

Fig. 3 .

 3 Fig. 3. Caloric and greasy game enemies.