Marcos Felipe De Menezes Mota

Fagner Leal Pantoja

Matheus Silva Mota
email: matheus.mota@ic.unicamp.br

Tiago De

Araujo Guerra Grangeia

Marco Antonio De Carvalho Filho

André Santanchè

Analytical Design of Clinical Cases for Educational Games

Keywords: Health Education, e-Learning, Serious Games

Preparing medical students to provide emergency care is one of the biggest challenges in health education, as novices must articulate a wide spectrum, ever-growing knowledge of a generalist physician in the shortest possible time. Previous experience has shown that a problembased e-Learning environment, presenting clinical cases to be solved by students, has several benefits in the learning process. This paper describes the design and development of an approach to produce health learning games. The approach focuses on combining student engagement with realistic narratives. The central component is a narrative scripting language that enables rapid prototyping and integration with a data analysis backed authoring process. Our method has been materialized in an educational game authoring environment that allows the creation of complex cases and automatic generation of simpler ones. The analytical design provides a scalable method to create medical learning experiences based on well-defined medical education theories and health data.

Introduction

Training doctors to find the best diagnostic and treatment for each clinical case is one of the biggest challenges in medical education. To become a good clinician, medical students need to apply the best scientific evidence in caring for patients. Medical educators need to design pedagogical strategies that offer medical students the opportunity to repeat the clinical tasks to develop expertise while guaranteeing the safety of patients. Among the main competencies, medical students need to foster is clinical reasoning, the mental strategies to reach a diagnosis and plan a therapeutic intervention. Developing reasoning skills is particularly important for emergency physicians who work under time pressure in situations potentially fatal. In summary, emergency training requires the student to master the spectrum of knowledge of a generalist physician in the shortest possible time.

In a previous successful experience, which is the starting point of this research [START_REF] Grangeia | Cognitive Load and Self-Determination Theories Applied to E-Learning: Impact on Students' Participation and Academic Performance[END_REF], an e-learning environment directed to teach emergency medicine through the solution of clinical cases has shown to improve the engagement of students and their performance.

However, this solution lacks support in three main aspects: narrative, evidence, and evaluation/feedback. The environment to build the narrative of clinical cases, presented to the students, was adapted in an e-learning platform (Moodle), being limited to the possibilities offered by its forum and quiz tools. Clinical cases are manually crafted by tutors (physicians) based on evidence of real-world cases, their own experience, and facts reported in the literature. The system pipeline is still highly dependent on the action of professors to evaluate and give feedback to each answer or decision of the students, hampering the scalability of the system.

Building the narrative of a game for clinical cases training is a challenge encompassing two dimensions. On the one hand, there is a real dimension, as the game aims at training real doctors, each scenario -with diseases, symptoms, medications, etc. -must be supported by real-world phenomena. On the other hand, there is a fantasy dimension, in which the author is not constrained by preexisting evidence, having the freedom of introducing elements of an engaging narrative that do not compete with the real dimension.

In this paper, we present our approach to align these two dimensions through a scripting language that combines the freedom of composing narratives, blending them with data backed by scientific evidence. It is the kernel of an environment for authoring and playing educational health games.

The remainder of the paper is organized as follows: Section 2 introduces the main challenges and background of this research; Section 3 summarizes the health education scenario based on narratives and simulations of clinical cases; Section 4 explains how narrative scripting languages are used in the game industry in order to help authoring of stories; Section 5 proposes a pipeline using analytical design principles and narrative languages to produce realistic cases aided with data; Section 6 details implementation of a system that materializes our proposed design approach; and Section 7 presents the discussion of main results and future work.

Motivation

Real-world cases are the best source to build possible and reliable scenarios, given the complexity of a human body and its interaction with the environment. However, clinical experience and data from the literature are fundamental to enrich the case with alternative paths to the solution and assess student decisionsolving capabilities.

The amount of tutor work to build the cases is paramount and relying on data-driven solutions would boost the capacity of the system to generate clinical cases while saving tutoring time. To address the real dimension we have been building an analytical design backed system, in which the authoring process is the result of an interaction between a human author and an information system. Following a data-driven approach, the system extracts, combines and analyzes health data coming from different data sources. It helps the enrichment of the case, suggesting and advising; the verification of consistency on information; and the production of machine-interpretable outputs to automatize students tracking, evaluation and feedback.

Edward R. Tufte in 2006 coined the six principles of analytical design [START_REF] Tufte | Beautiful evidence[END_REF]. According to Tufte, allowing the viewer to establish: comparisons, causality or mechanism, multivariate analysis, integration of evidence, documentation and rich content, in graphical data is the main quality of good evidence display and reliable scientific data exposition [START_REF] Tufte | Beautiful evidence[END_REF]. A clinical cases writer need to gather evidence from different medical data sources, understand the mechanisms governing the diseases in the case, compare a case with many others used in class, and provide feedback based on a narrative content known by the student. Therefore, although these analytical design principles were originally asserted for graphics, they can be transported to measure the quality of a well written clinical case and guide the development of data-driven e-learning system that allows effective creation of such cases.

There is a tight coupling between the narrative and the computational resources necessary to render it in rich interactive scenarios. This demands a close interaction among health professionals (authors of cases), IT professionals, and designers.

One of the challenges in such interaction is the lack of common ground among health professionals, IT professionals, and designers. In several authoring systems, health professionals can produce a great narrative on their cases but are not able to transform them on interactive learning experiences. Such challenge of integrating the creator (author), the developer, and the designer is also tackled by the computer game industry [START_REF] Lynch | Textual authoring for interactive narrative[END_REF]. An effort to soften the gap between the content creator and the game development process is the introduction of a narrative scripting language, for example, Ink [START_REF] Studios | Ink -inkle's scripting language for writing interactive narrative[END_REF] or March22 [START_REF] Lynch | Textual authoring for interactive narrative[END_REF] languages. Such languages allow the writer to build a narrative closer to a natural language but with lightweight markups that can be compiled in an interactive game ready for the user.

In this work, we have blended elements of Analytical Design with a narrative scripting language, which is the kernel of our authoring and execution system. A third central component is a template-based authoring process. It addresses a natural problem of decision-based games that produces an alternative scenario for each action of the player. The consequence is an unmanageable explosion of alternative scenarios, which are even harder to produce when the alternatives must be backed by evidence data. To circumvent such problem we have developed an authoring method, in which the creative process is conducted by templates. Templates guide the author, avoiding the explosion of alternatives, and also embed the experience of several years in health education from physicians.

Following this approach, we were able to produce case scripts and to turn them into playable cases, following proved educational templates. The developed application can be improved using more data sources, better human-computer interaction and better artificial intelligence. Nevertheless, the design pipeline has shown to be consistent with the goals of physicians health education.

Narrative and Simulation in Health Education

We consider three main components in an approach to support producing clinical scenarios to train doctors: (i) a proper analogy to real-world health phenomena; (ii) an engaging narrative; (iii) rich interactive content to present the case. Components (i) and (ii) have been explored in health education and they are detailed in this section; components (ii) and (iii) have been explored in games development, presented in the next section.

Flexnerian education [START_REF] Flexner | Medical Education in the United States and Canada: A report to the Carnegie Foundation for the Advancement of Teaching[END_REF] refers to the actual health care education approach, in which students start by receiving science-based medical knowledge, usually through traditional lectures and/or problem-based learning approaches. Frenk et al. [START_REF] Frenk | Health professionals for a new century: transforming education to strengthen health systems in an interdependent world[END_REF] consider the introduction of Problem-based Learning (PBL) and disciplinarily integrated curricula the second generation of reforms in health care education. It moves the focus to the problem and the patient, early promoting the articulation among several disciplines, usually through focusing on real-life scenarios. In fact, throughout the diversity of PBL methods, the usual approach consists of a small group of students working in a real-life based case, evaluating the problem and making decisions [START_REF] Neville | Problem-based learning and medical education forty years on. A review of its effects on knowledge and clinical performance[END_REF]. Case-based learning techniques -i.e., based on the resolution of clinical cases -have been a widespread PBL approach to learn clinical reasoning [START_REF] Kassirer | Teaching Clinical Reasoning: Case-Based and Coached[END_REF]. The narrative of each case is the key to blend technical health aspects and engaging properties.

PBL introduced the concept of Standardized Patients -i.e., individuals trained to act as real patients, simulating their behaviors and complaints [START_REF] Frenk | Health professionals for a new century: transforming education to strengthen health systems in an interdependent world[END_REF]. Simulated patients offer the possibility of bringing complexity to students in a stepwise fashion, adapting the task to students developmental level, optimizing the cognitive load.

The information technology has been contributing to the advance of analogies and simulations through the introduction of computer-based clinical scenariosthe Virtual Patients (VP) [START_REF] Cook | Virtual patients: a critical literature review and proposed next steps[END_REF] -as well as high-fidelity Human Patient Simulators (HPS) [START_REF] Bremner | The use of human patient simulators: Best practices with novice nursing students[END_REF], based on automated mannequins. A VP runs a clinical scenario behind a computer screen, with variable interaction levels -from textual narratives accompanied by static images to 2D or 3D interactive simulations. An HPS simulates physical characteristics that resemble real persons -one can auscultate the mannequin; inject drugs; try cardiopulmonary resuscitation, etc.

The route of a case in a VP or a simulation in an HPS is still authored manually by a specialist, which is not ready yet due to the complexity of creating algorithms that properly simulate the dynamics of a human body and possible variations. This route usually takes the form of a graph, with nodes representing case states and edges representing transitions triggered according to actions. State nodes will contain free-text descriptions, for those VPs based on narra-tives, and will contain structured data that drive HPSs or VPs with interactive simulations.

As shown in Fig 1, these instructional modalities can be seen as complementary in a continuum of competency [START_REF] Cook | Virtual patients: a critical literature review and proposed next steps[END_REF]. Narratives and the analogy to real-world health phenomena will play important roles in each modality. Narratives usually come from specialists that write PBL cases, VP scenarios or Standardized Patient scripts; the analogy to real-world phenomena can be seen as part of a bigger scenario, comprising data collected from the real-world to drive predictions, inferences, and discoveries. Even though they are treated as distinct and sometimes alternative disciplines [START_REF] Frasca | Simulation versus Narrative: Introduction to Ludology[END_REF], in the Analytical Design perspective, proposed here, both are treated as interdependent.

Our focus in this paper is in the VP tools context. Existing tools are still highly limited in the support to author engaging narratives when compared to game authoring approaches and lack support to add rich interactive content. In the next section, it will be presented with game authoring approaches, which we exploited to enrich our solution.

Narrative Scripting Languages

Many aspects of clinical cases authoring, described in the previous session, are shared with the development of interactive narratives for games. One of the challenges is the entry barrier of the case writer and the software-based platform that executes it. Also, effective approaches in problem-based learning rely on a narrative format [START_REF] Grangeia | Cognitive Load and Self-Determination Theories Applied to E-Learning: Impact on Students' Participation and Academic Performance[END_REF], therefore common tools can be used. Complex narratives have the drawback of not being suitable for tools like cognitive maps and stories on sticky labels because they branch exponentially for each decision point a user can make.

In Fig 2-A (left), we can see a standard pipeline performed by an author to insert their clinical cases in a web-based e-Learning system, adapted from Lynch et al. [START_REF] Lynch | Textual authoring for interactive narrative[END_REF]. First, the writer gathers information about real clinical cases, the diseases in such cases and relevant references. After the complete case is written, the script is sent to a developer that turns it into an interactive code. Web-designers or game artists create the environment and animations necessary for an interesting learning experience. If the developer does not fulfill the requirements of the author or produce a code with errors the interaction with the author/physician continues. Looking at this pipeline, we can see that the author is decoupled of the game production environment, possibly raising the following problems: many errors can arise due to the difference in the background of the physician and the developer; the feedback in the authoring cycle is too slow, i.e., writers cannot preview the result of their narrative decisions, therefore, they are not able to try different ways to present the case during its conception. Some initiatives, like Ink [START_REF] Studios | Ink -inkle's scripting language for writing interactive narrative[END_REF], March22 [START_REF] Lynch | Textual authoring for interactive narrative[END_REF] and INDIeAuthor [START_REF] Pérez-Berenguer | Indieauthor: A metamodel-based textual language for authoring educational courses[END_REF] rely on textual languages that bridge the narratives with the control of the flow according to decisions. More specifically, in [START_REF] Studios | Ink -inkle's scripting language for writing interactive narrative[END_REF] and [START_REF] Lynch | Textual authoring for interactive narrative[END_REF] is created a narrative scripting language. It is a markup language that allows the writer to script a narrative in a way closer to a natural language but with a structure machine-interpretable, so it can be compiled into a software. In the game context, it can be compiled to an interactive game ready for the player. Usually, these languages are lightweight markup languages enriched with common narrative directives like branching, variables, choices and conditional content. In Fig 2-B (right), we see how a narrative language changes the pipeline of case writing. The main change is more integration of the author through the process of editing and compiling of the narrative language and development team more free to focus on graphical design issues.

Analytical Design of Clinical Cases

Even though the adoption of narrative scripting languages is a design improvement to create clinical cases, the existing approaches, coming from the game development context, are geared towards fantasy stories. Therefore, they lack proper support to represent structured technical information, fundamental to create realistic clinical cases.

Information Layers

To address this limitation we have designed a system backed by a language 3 that blends the three components mentioned in Section 3. They are organized in information layers as presented in Fig 3. Narrative Layer -We designed a language derived from Markdown, which adds elements that represent an extra semantics to build scenarios and the trol flow of the game. This language has some structures based on Ink [START_REF] Studios | Ink -inkle's scripting language for writing interactive narrative[END_REF]. We further present some elements of the language in Table 1.

Data-driven Layer -Elements from the Narrative Layer are annotated and connected to structured data and knowledge structures defined in the Datadriven Layer, as dictionaries and ontologies. These structures are connected with external knowledge bases, e.g., MeSH -Medical Subject Headings. This layer is designed to be consumed by machines to support the automation of tasks as 3 Full language reference at https://github.com/datasci4health/harenaspace/tree/master/src/adonisjs/public/translator Symbol Action # or === Markdown headers delimit scene nodes. -> Node transitions triggered user actions, e.g., a button pressed.

{? } Player inputs that are further matched with lists, dictionaries or knowledge structures defined in the Data-driven layer.

{ }

Annotate terms inside the narrative and connect them with the Data-driven Layer.

{{ }}

Defines a semantic context for the narrative and the respective annotations.

The context guides the interpretation of parts of the narrative.

Table 1. Main narrative language constructs for case writing.

evaluation and feedback. It is also part of a future project to build a full fledged intelligent tutor.

Presentation Layer -The document in the Narrative Layer is compiled to the final product, which is a game. Each node is related to a theme that guides its conversion to the final presentation. Interactive elements are mapped to web components, based in the Digital Content Component Model (DCC) developed by us [START_REF] Santanchè | User-author centered multimedia building blocks[END_REF].

We have been exploring this connection between the Narrative and Datadriven Layers to provide automatic support to produce and enrich cases. The analyses of existing cases and their bridge with structured data supports the discovery of patterns of narrating, for example, the symptoms of a disease and the respective treatment. These patterns can be applied in finding extra knowledge to enrich the case in narrative-based data sources, as scientific papers repositories.

Analytical Design Pipeline

To generalize the concepts presented in the previous section, we propose an analytical design pipeline (Fig. 4), which fulfills most of the requirements of an application to write clinical cases. Besides a method based on the three layers model, another key feature in this pipeline is the introduction of a case template. A template is a previously defined pattern in the system that guides the creation of new scenes or groups of scenes. It defines a fixed backbone structure with spots to be filled or elements to be configured by the author. In the health context, the doctors of our team developed templates based on their long term experience in medical education, as described by [START_REF] Grangeia | Cognitive Load and Self-Determination Theories Applied to E-Learning: Impact on Students' Participation and Academic Performance[END_REF].

Our pipeline has as design guidance the principles of analytical design as specified in [START_REF] Tufte | Beautiful evidence[END_REF]. We enumerate below the principles and how they are ensured by our pipeline:

1. Comparisons -The author can start a case choosing from many different templates or cases to derive. Results achieved from previous adoptions of cases/templates will enable to choose which case is better for her educational intention. 2. Causality, Mechanism, Explanations -The Data-driven Layer enables the production of cases derived from Evidence-based Medicine, which unveils the mechanisms of relating symptoms to diagnostics. Such mechanisms could be useful to increment the case realism even in a fantasy story, once able to mix narrative and data coming from scientific literature, The system also tracks all actions of the users during a case. From its analysis, the author can understand the mechanism for presenting a concept. 3. Multivariate Analysis -The proposed design generates three types of data: medical, player and educational. Each type has multiple dimensions that must be shown to the author. 4. Integration of Evidence -Our three information layers model enables to integrate into a narrative different type of data and makes accessible to the authoring process the best scientific foundation to the case. 5. Documentation -Each case is indexed by its authors, keywords, and data coming from the data-driven layer, as each medical evidence the case is based (e.g. papers, study cases). 6. Content -A template ensures that the case has a health education theory as background and it has educational efficacy proven. Moreover, annotations connected with external bases enable to track their provenance. In the next section, we describe how we implemented the Analytical Design pipeline and our continuous effort to develop a system that allows easy creation of clinical cases for medical educational games.

Implementation

The analytical pipeline was materialized under a system called Harena 4,5 . The system is a web-based application following microservice architecture where ev-ery microservice is a REST API. The system allows authoring cases and the generation of games built from the cases.

The system has two main environments: the author and the player. In the author mode, scenes can be edited in the visual or textual mode. In Fig. 5 we see the visual authoring mode of the system. In this mode, the user can see: the structure of the case in a tree form (left); a preview of the scene node as it will be presented to the player (middle); and attributes of the currently selected scene or element inside it (right). The textual authoring mode switch the preview for a text box where the same scene can be edited using the narrative scripting language. Every change in the narrative script is automatically updated in the visual mode, therefore, we provide two ways of editing one more visual suitable to beginners and a textual mode for advanced users. The player view has the same look of the visual authoring mode preview because both are compilations of a narrative script. The player mode follows the route defined by the author and every interaction of the player is converted to messages stored in a JSON document, further recorded in a database. Such approach allows further data analysis of the player behavior.

The authoring environment and respective games produced on it were tested in workshops developed at the Interactive Science Museum of Unicamp. We have conducted 3 workshops where we tested two modalities of the games. One of the games was manually crafted in conjunction with doctors and the second one was automatically generated, as further described.

Automatic Generation of Cases

The first experiment we performed with the Analytical Design approach was a test for the narrative scripting language, case templates, and the communication between the three information layers. The output was a children's game that confronted them with simple clinical reasoning. Thus, we developed the game ZombieHealth, which is a decision game where the player is a doctor who needs to treat zombies examining their symptoms to identify a disease to be treated.

The generated game was derived from a causal model and its goal was to verify if the children will learn the correlation between causes (diseases) and effects (symptoms) modeled in the game.

Each zombie in the game has a generated name and disease. Each disease is based on a causal model, as defined in [START_REF] Pearl | Causal inference in the health sciences: a conceptual introduction[END_REF], so the disease is the cause of a set of symptoms (effects). The model is as simple as possible but each disease entails a different probabilistic distribution of symptoms from its model. Three diseases and six symptoms were modeled for the game using the probabilistic programming framework Pyro6 . In the game, a zombie has an equal probability of having one of the three diseases and each disease has its symptoms distribution. A set of case attributes were generated from a set of samples following the probabilistic distribution. Thus, models and case attributes form the Data-driven Layer for this experiment.

Provided the case attributes set that form the Data-driven layer in this game, we can proceed to the Narrative Layer. Therefore, we wrote a template in the narrative scripting language and defined multimedia assets to be compiled for the complete game on the Presentation Layer. The script of every case has the same pattern of symptom inquiry, so features of the case can be substituted in the narrative script placeholders. In Fig 6 we can see a scene of an automatically generated case. Despite the simple interaction, the narrative setup and the uncertainty aspect of each case were enough to engage the children on playing and fostering clinical reasoning of cause and effect.

Authoring of Complex Cases

Besides automatically generated cases, we tested the system in much more complex scenarios. Health professionals were able to translate the interaction of [START_REF] Grangeia | Cognitive Load and Self-Determination Theories Applied to E-Learning: Impact on Students' Participation and Academic Performance[END_REF] to the new system. Fig. 4 shows a scene of one of these cases written using Harena. An important new feature of this transition is the possibility of organizing the inner structure of cases as cycles of evidence gathering and a case as a composition of these cycles. Such approach can guide the creation of complex cases in progressive levels of detail. This structure follows the best practices of medical education. New elements as gamified reward, leaderboard or interactive media can be added given tested benefits in medical education. More complex cases are being written to be organized in a course structure to integrate class curriculum at the University of Campinas.

There is an ongoing effort in defining how to assess students performance using Harena. There is an increasing interest in psychometrics in health education and, in the context of this work, it needs to be integrated with game-based assessment. Therefore, future work includes student assessment using complex cases. Harena currently has the infrastructure for evidence-centered assessment, capturing and storing data concerning students evolution suitable for assessment -known in the educational assessments as work products [START_REF] Mislevy | Psychometrics and game-based assessment[END_REF]. It can also store in data stream log files case-specific data using variables and contextual data like timestamp, identifier, actions, and game state. Defined the assessment methodology, these logs can be used for rigorous assessment and psychometrics.

Conclusion

The development of a system, following the Analytical Design pipeline, allowed us to test many of the pipelines characteristics and produced a new potential for the development of serious games. The use of a narrative scripting language bridges the role of the case author and game development, as shown by the creation of complex cases by specialists. It also bridges the cases with a datadriven layer that enables their automatic interpretation by machines, which in turn use this knowledge to evaluate and give feedback to the user. The application of transformations in the narrative script, based on themes combined with software components, enabled the automatic production of a final web product and a preview suitable for visual editing. The complex cases created using our pipeline indicate that templates help manage the narrative complexity and physicians can focus more on the learning goals than the narrative. Users interaction are captured and stored by our system, as defined in the Analytical Design pipeline. Future work will explore techniques of user assessment, template enhancement, automated feedback and gamification testing in the system. The Analytical Design pipeline and its principles can enable computer scientists and physicians to jointly conduct research under the same system and context in medical education, serious games, data-intensive applications, and artificial intelligence.

Fig. 1 .

 1 Fig. 1. Instructional modalities with desired outcomes [2].

Fig. 2 .

 2 Fig. 2. Pipelines for clinical case writing in e-Learning systems.

Fig. 3 .

 3 Fig. 3. Information Layers of our system.

Fig. 4 .

 4 Fig. 4. Analytical Design pipeline for clinical case writing in e-Learning systems.

Fig. 5 .

 5 Fig. 5. System visual authoring view of a case. In the left the narrative scenes tree, in the middle the current scene to edit and on right attributes of the scene.

Fig. 6 .

 6 Fig. 6. Player view of an automatic generated case for ZombieHealth game.

https://github.com/datasci4health/harena

https://ds4h.org/

http://pyro.ai/


Yubin Liang
email: liangyubin@yahoo.com

Wanxiang Li

Kokolo Ikeda
email: kokolo@jaist.ac.jp

Procedural Content Generation of Rhythm Games Using Deep Learning Methods

Keywords: Procedural Content Generation, Rhythm Game, C-BLSTM

The rhythm game is a type of video game which is popular to many people. But the game contents(required action and its timing) of rhythm game are usually hand-crafted by human designers. In this research, we proposed an automatic generation method to generate game contents from the music file of the famous rhythm game "OSU!" 4k mode. Generally, the supervised learning method is used to generate such game contents. In this research some new methods are purposed, one is called "fuzzy label" method, which shows better performance on our training data. Another is to use the new model C-BLSTM. On our test data, we improved the F-Score of timestamp prediction from 0.8159 to 0.8430. Also, it was confirmed through experiments that human players could feel the generated beatmap is more natural than previous research.

Introduction

Rhythm game is a genre of music-themed (action video) game in which players play by taking actions in accordance with rhythm and music [START_REF] Pasinski | Possible benefits of playing music video games[END_REF]. Since music and songs are familiar to ordinary people, it is easy to understand how to play such games. In addition, both of easy stage and hard stage can be created from one music, therefore rhythm game becomes a popular game genre in the whole world.

In many cases, the contents (required action and its timing) of rhythm game are hand-crafted by human designers from music material. Also, there are countless pieces of music, but only a part of them have already been used as game contents. Therefore, to generate contents automatically for rhythm game is required. In this research, two models are used to generate contents from music materials for famous rhythm game "OSU!" [1]. One inputs the audio data, outputs timestamps (timing of action), another inputs the timestamp and outputs action type.

As a Machine Learning task, this approach has some difficulties such as: (1) Because same music may be processed by different authors or has different level settings, one music may have many different beatmaps. (2) Proportion of positive/negative samples is ill. To deal with those problems, in this research we adopt that: (1) Handle the difficulty settings as an input feature. [START_REF] Buda | A systematic study of the class imbalance problem in convolutional neural networks[END_REF] Using fuzzy labels to increase positive samples.

Proposed training methods and new model C-BLSTM are evaluated through F-Score, shows a better performance than previous research on timestamp generation. And from human experiments we can find that beatmaps which generated by purposed method are more natural than beatmaps generated by previous research.

Background

The system structure of purposed method is shown as Fig. 1. The main workflow is from audio files generating the timestamp, and using those timestamps to generate action type by second model. Combining the generated timestamp and action type, we can obtain beatmap for input music.

Fig. 1: System structure of purposed method.

Rhythm game: OSU!

To generate the contents of rhythm game, we use open-sourced game "OSU!" which has over 10 million registered users [1] as our test bench.

"OSU!" consists of many types (or mode) of game, but in this research we foucs on the mode called mania 4k. Players are required to push the button when markers are dropping from the top of screen. When players push the correct button, they would get a high score. On the other hand if the timing or required action was incorrect, system would judge this action as missing. By pushing the buttons, players would feel like they are playing this rhythm by themselves.

These required actions (4 types: click, long push start, long push end and none) and their timing consist the game contents. In "OSU!", beatmap records these information. Generally, beatmap are usually hand-crafted by human designers. So even if there are countless pieces of music, the number of contents that can be played in the game is limited. An automatic generation method is needed in this case.

Previous research

In the field of rhythm game and game contents generation research. There are some previous researches which is essential to our approaches.

Capturing the characteristics of music is an important part in rhythm game content generating. Generally, selection of action timings is based on the sounds or rhythm which can be clearly heard by human. Since the beginning or changing of music is most distinguishable to human, we usually set actions around these timing in rhythm games. To extract these timing, Schlüter et.al proposed an research using deep learning methods to detect the beginning of musical notes (musical onsets) [START_REF] Schlüter | Improved musical onset detection with convolutional neural networks[END_REF]. Their method is used to extract features in our research.

Long Short-Term Memory (LSTM) is a kind of Recurrent Neural Network (RNN) which is effective to suppress gradient vanishing over time [START_REF] Hochreiter | Long short-term memory[END_REF], and it has been proved to be effective for time-based task such as voice detection [START_REF] Parascandolo | Recurrent neural networks for polyphonic sound event detection in real life recordings[END_REF] and translation [START_REF] Kalchbrenner | Grid long short-term memory[END_REF], etc.

Music or rhythm is generally regarded as a type of time-based data, and in the field of rhythm game content generation, LSTM was used to generate game content of a rhythm game called "Dance Dance Revolution" in Donahue et al.'s research [START_REF] Donahue | Dance dance convolution[END_REF]. However, to generate a content which correspond to specify difficult level, or some special patterns (e.g. patterns that player good at or not) is still not so gratifying. Hence, this research is aimed at generating game contents that correspond to specific player's level in rhythm game.

In many rhythm games, long press buttons are basically corresponds to a long sound of music. So it is useful for analyzing the melody to generate the long press properly. In Donahue et al.'s research, although the effectiveness of melody information in action generation is examined, but its application has not been taken yet [START_REF] Donahue | Dance dance convolution[END_REF]. In this research, we use the method which propose by Salamon et al. [START_REF] Salamon | Melody extraction from polyphonic music signals using pitch contour characteristics[END_REF], for extracting the main melody of music. By using their method, we can detect long sounds in music and attempt to employ them in long press action generation.

Preparation of Training Data

Data and difficulty definition

In this research, all training data is collected from the homepage of "OSU!" [1] on June, 2017. Considering to use the most popular beatmaps as our traning data, only beatmaps which has been played over 100 thousands times will be chosen.

In general, one music file corresponds to various beatmaps depending on the different authors and different difficulties. And players can play a suitable difficulty according to their ability. The statistical information of used training data is shown as Table 1. Since there are various difficulties in the training data, and we aims to generate beatmaps with different difficulties from one music. So a proper definition of difficulty is highly required.

Generally, the difficulty is depending on: (1) Complexity of action combination. (2) Number of actions per second (density of actions). ( 3) Speed of action marker. (4) The strictness of judging miss (max allowable time difference). ( 5) Number of miss that could be permitted.

Because (3), ( 4) and ( 5) is the more relative to setting of game, we don't consider these factors in this research. Also, individual players have different action combinations that they are not good at, so it is hard to use factor (1) in automatic generation method. More actions in same time means beatmap is more difficult, so in this research we just use density of actions to define the difficulties for each beatmap.

We divide the training data to 10 levels by density, that is (a) number of action under 3 per second is level-0, (b) number of action from 3 to 17 per second is used for level-1 to level-8 equally (e.g. 3 to 4.75 is level-1 and 4.75 to 6.5 is level-2), (c) number of action more than 17 per second is level-9.

Feature extraction

In order to use the audio data in neural networks, some feature extraction process is needed. Firstly we compute a multiple-timescale short-time Fourier transform (STFT) of every audio file by three window sizes 23ms, 46ms and 93ms and stride of 10ms as 1 frame (that means there are 100 frames per second). Shorter window sizes preserve low-level features such as pitch and timbre while large window sizes provide more context for high-level features such as melody and rhythm [START_REF] Hamel | Building musically-relevant audio features through multiple timescale representations[END_REF].

Then we compute the Mel-scale from 27.5Hz to 16kHz with 80 bands of STFT magnitude spectra to better represent human perception of loudness [START_REF] Stevens | The mel scale equates the magnitude of perceived differences in pitch at different frequencies[END_REF].

Finally we prepend and append n frames of past and future context for each frame, so the final audio representation is a 80 * 3 * (2n + 1) tensor. C-LSTM can only use the information "until now". In this research, "OSU!" has a lot of "long push" action, since most "long push start" action is at the onset of long melody, we need to consider not only the information in past, but also information in the future. One way is to use more frames (i.e. make n bigger), but the high calculation cost and performance reduction is unbearable sometimes. Another way to get more future information without increasing frames is using the C-BLSTM [START_REF] Schuster | Bidirectional recurrent neural networks[END_REF]. The structure of C-BLSTM is shown as Fig. 3. C-BLSTM can predict timestamp using more future information, but has the similar calculation cost as C-LSTM.

Fuzzy label

There are about 7.85 actions per second in our training data. Since we have 100 frames in 1 second, there are just few positive data (less than 8%). One way to solve this problem is to calculate feature with wider stride. For example using 50ms stride will get 20 frames per second, which will make the rate of positive data to 40%. But longer stride will lose some rhythm or melody information.

In this research a method called fuzzy label that can solve the problem is proposed. Shown as Fig. 4, the original data use 0 (no action in this frame) and 1 (there is action in this frame) label which have a drastic changing. And fuzzy label modify the data using Gaussian distribution, transform the data from "whether the frame has actions or not" to "the possibility that the frame has actions", makes the changing smoother.

Threshold selection

Output of network is the "possibility whether one frame should have actions". In practice, we need to transform the possibility into a two-value number (0 or 1), to decide whether there should have actions in this frame. Using some rules such as "when possibility is bigger than a threshold t, there should have actions" can be helpful for judging.

When threshold was too large, output actions would decrease, and output action would increase when it was too small. Both situations are not good for timestamp selection, so a proper threshold is needed. Some data which called "threshold deciding data" is randomly selected to decide the threshold t ,which we will test different threshold in the same model by it, to maximize the F-score. The threshold deciding data is independent to training data and will not take part in the training process.

Experiment

Difficulty distribution in original data is biased, there are just few low difficulty data and high difficulty data. Such an ill data distribution is harmful and causing unstable learning [START_REF] Buda | A systematic study of the class imbalance problem in convolutional neural networks[END_REF]. To solve this problem, we copy the training data in low and high difficulty to make original data distribution balanced. From the result, we can find that comparing to the C-LSTM, C-BLSTM shows a better performance in the beginning, but suddenly facing the problem of degradation in performance. We also find that training error of C-BLSTM deceasing follow the process. This means C-BLSTM faced a serious overfitting problem in the mid of training.

Validation experiment

When comparing the result of C-LSTM to C-LSTM with fuzzy label (label length is 3), we can find that fuzzy label improved the performance dramatically. Moreover, from the result of C-BLSTM with fuzzy label, we can find that fuzzy label not only improved the performance, but also solved the overfitting problem of C-BLSTM.

Experiment of different fuzzy label

We also have experiments on different fuzzy label lengths to compare their performance. In this experiment, all models use C-BLSTM and 80*3*15 (frame length n = 7) tensor as input, also, they use same learning hyper-parameters.

Mean F-score of each epoch is shown as Fig. 6. We can find that our method improved the naive C-BLSTM comparing to the result with no fuzzy label. Also, we find best fuzzy label length is 3 on our data. One possible reason is when length was 3, the rate of positive data would be closed to 50%, which is a wellbalanced data distribution. Experiment of different frame length In this experiment, all model use C-BLSTM, same fuzzy label length of 3 and same learning hyper-parameters. The only difference is the frame length, which will influence the input tensor size. For example when frame length n was 7, the input size would be 80*3*15, and when frame length n was 9, the input size would be 80*3*19.

The result of n =4, 5, 6 and 7 is shown as Fig. 7a. We can find that when n is 4, we would get the best performance, and when n is 7 we would get the similar performance to n =4. From Fig. 7b we can find that n =7 showed the best performance. The result shows that we cannot get better performance by simply adding the by frame length. The time interval is the distance between two actions. We use a 8 bit onehot vector to present it. This 8 bit one-hot vector means the interval is ∼50ms, ∼100ms, ∼200ms, ∼400ms, ∼800ms, ∼1600ms, ∼3200ms and 3200ms∼. Since both the past time interval and future interval is used, the time interval will be presented as two 8 bit one-hot vector. And a 10 units one-hot density label (which present the level of difficulty) is also needed in this model. The input feature is combined with the density, time interval and past action, so input feature has 7 vectors and the data size is 42 bit.

The output is probabilities of all action combinations. Since we have 4 channels and each channel has 4 types of action, the number of all valid actions is 256 (4 4 ). Finally we will choose one action combination that have biggest probability.

The example of input feature and output probabilities is shown as Fig. 8.

Fig. 8: Example of input features and output.

Network structure

In Action type generation part, we uses the network frame called LSTM64 [START_REF] Donahue | Dance dance convolution[END_REF]. Shown as Fig. 9, after input layer the first layer uses 128 LSTM units, and secend layer uses 128 LSTM units. Finally this network output 256 probabilities of all valid actions. Fig. 9: Structure of beatmap generation model.

Experiment of predict actions

Same training data as timestamp generation model are used for training this model. Also, since the difficulty distribution in original data is unbalanced, training data in low and high difficulty is copied to make original data distribution balanced.

The finial accuracy of predicting action is 0.4366, which is not a high accuracy. But we find that even in one music, there are different beatmaps. And according to the different designers, the beatmap is various. For example in Fig. 10, we can find that about half actions are not same between two human designer, even they have used the same music. Fig. 11 shows a result of beatmap generated by network and human designer for same music. We can find that even some actions are different, these two beatmaps are similar, which player may not notice the difference. According to these evidence, we think the result that predicting accuracy is 0.4366 is not bad. Prediction accuracy is not enough for estimating how human players can enjoy the generated beatmaps, so it will be an interesting future work to propose such a measurement.

Evaluation of naturalness

We conduct a experiment though 10 human subjects. 15 beatmaps (5 of them are hand-crafted by human designer, 5 of them are generated by previous research and 5 of them are generated by proposed method) are used in this experiment. "Purposed method" data and "Existing method" data use same action type generation model, but "Purposed method" data use C-BLSTM model with fuzzy label (length=3), frame size n is 7 to generate timestamp. timestamp of "Existing method" data are generated by C-LSTM without fuzzy label, and frame size n is 7.

All Beapmaps are randomly watched by every subjects, and subjects are asked to judge the naturalness for each beatmap. The result is hand-crafted beatmaps get 4.52 points, existing method get 3.30 points and purposed method get 3.72 points. From the result we can find that even beatmaps which generated by purposed method are not as natural as hand-crafted beatmaps, the naturalness is better than beatmaps generated by existing method.

Conclusion

Overall, a new method "fuzzy label" is introduced in game contents generation of rhythm game, and new model C-BLSTM is used in our timestamp generation method. We have proofed that purposed method improved the performance of Timestamp generation, and the F-Score is increased from 0.8159 to 0.8460.

Moreover, a generation model from timestamp to beatmap is introduced. And by experiment on human subjects, we have proofed that beatmaps generated by purposed method is more natural than previous research.

  structure Donahue et al. uses C-LSTM to generate timestamp [3], which has the advantage to learn not only the current frame but also the past and future frames. Structure of C-LSTM is shown as Fig.2. We use 15 * 80 * 3 tensor (n = 7) as input. First layer is a convolution layer which has the size of 7 * 3 * 3 and 1 * 3 max-pooling, output 9 * 26 * 10 matrix to next layer. Second layer is a convolution layer which has the size of 3 * 3 * 10 and 1 * 3 max-pooling, output 7 * 8 * 20 matrix. Adding the 10 units one-hot density (which present the level of difficulty), we input the matrix to 2 LSTM layer which have 200 units. After LSTM there are 2 full-connected layer with 258 units and 128 units separately. Finally we output the result by a single Sigmoid unit, which presents the possibility of been chosen as a timestamp. LSTM layers use tanh function as activation function, while other layers use ReLU.

Fig. 2 :

 2 Fig. 2: Structure of C-LSTM.

Fig. 3 :

 3 Fig. 3: Structure of C-BLSTM.

Fig. 4 :

 4 Fig. 4: Example of fuzzy label.

Fig. 5 :

 5 Fig. 5: Result of validation experiment.

Fig. 6 :

 6 Fig. 6: Result of fuzzy label experiment.

  (a) Result of n =4, 5, 6 and 7 (b) Result of n =7, 8, 9 and 10

Fig. 7 :

 7 Fig. 7: Result of different frames

Fig. 10 :

 10 Fig. 10: Compare of two Hand-crafted beatmaps.

Fig. 11 :

 11 Fig. 11: Compare of Hand-crafted and purposed method.

Table 1 :

 1 Statistical information of traning data

	Number of	Number of	Number of	Number of	Action per
	authors *	music	beatmap	all action *	second *
	300	473	1655	1690000	7.85

* Approximately.

Acknowledgments. This research is financially supported by Japan Society for the Promotion of Science (JSPS) under contract number 17K00506.


Victor T Sarinho
email: vsarinho@uefs.br

"BDD Assemble!": A Paper-Based Game Proposal for Behavior Driven Development Design Learning

Keywords: Behavior driven development, Educational game, Paperbased game

Game-based learning represents a promising alternative to teach computing in higher education. This paper presents "BDD Assemble!", a paper-based game proposal for teaching Behavior Driven Development (BDD) competences. For this, the proposed game and the evaluation approach with software engineering students are described. As a result, a simple, interactive and colaborative game was provided, able to teach BDD concepts in a practical, competitive and fun way.

Introduction

Originally developed by Dan North [START_REF] North | Introducing bdd[END_REF], Behavior Driven Development (BDD) is an "increasingly prevailing agile development approach in recent years, and has gained attentions of both research and practice" [START_REF] Solis | A study of the characteristics of behaviour driven development[END_REF]. It is focused on defining fine-grained specifications of the behaviour of the targeting system, in a way that they can be automated as executable specifications of a system [START_REF] North | Introducing bdd[END_REF][START_REF] Solis | A study of the characteristics of behaviour driven development[END_REF]. It is also an outside-in agile software development process for agile teams that helps in a cooperative way to reduce this type of rework and regain time for new development work [START_REF] Rahman | A reusable automated acceptance testing architecture for microservices in behavior-driven development[END_REF].

BDD uses the Gherkin language to describe software behaviors [START_REF] Kudryashov | The beginner's guide to bdd[END_REF], providing a specific ubiquitous language that helps stakeholders to specify their system tests [START_REF] Solis | A study of the characteristics of behaviour driven development[END_REF]. It starts with textual descriptions of the requirements using specific keywords that tag the type of sentence (Given, When, Then), indicating how the sentence is going to be treated in the subsequent development phases [START_REF] Kudryashov | The beginner's guide to bdd[END_REF][START_REF] De Carvalho | Mapping business process modeling constructs to behavior driven development ubiquitous language[END_REF].

Regarding serious games, "they combine the entertainment value of games with additional objectives such as players acquiring knowledge or skills, receiving guidance and feedback on tasks to perform, or contributing partial solutions to problems" [START_REF] Dalpiaz | Games for requirements engineers: Analysis and directions[END_REF]. They are "intentionally designed to attract, engage, and retain (even addict) players by applying psychology principles" [START_REF] Dalpiaz | Games for requirements engineers: Analysis and directions[END_REF], whose impact for educational purposes has received considerable attention.

Considering the relationship between BDD and serious games, BDD has been used to document user stories as an approach to identify game rules [START_REF] Hermann | Bdd warriors other games[END_REF]. Stakeholders who are exposed to gamified elements can produce more BDD user stories in better quality and with more creative ideas [START_REF] Lombriser | Gamified requirements engineering: model and experimentation[END_REF]. Moreover, as a cooperative game, BDD can be used to demonstrate how issues can arise when requirements are not communicated effectively or clarified with other team members [START_REF]Hindsight: Try our bdd game[END_REF].

This paper presents the "BDD Assemble!", a paper-based game proposal able to complement the list of BDD game options, as well as improve the BDD design competence among agile development students.

Related Work

Regarding BDD usage to design a game, BDD Warriors [START_REF] Hermann | Bdd warriors other games[END_REF] is a card game that describes fictional scenarios as sets of Given-When-Then constructs that must be completed by the players to get points and win the game. Players score collaboratively by completing scenarios, which should make sense for the other players to consider them as complete.

By the application of gamification strategies, the UserStory Game [START_REF] Lombriser | Gamified requirements engineering: model and experimentation[END_REF] is a game on BDD-based requirements elicitation with diversified game elements. It is an online gamified platform for eliciting requirements through user stories and acceptance tests, which was developed to test the effect of gamification on engagement and on performance in requirements engineering (Fig. 1). Finally, focusing on the agile perspective, and without face-to-face communication, software teams playing the BDD Game rely on their interpretation of a set of instructions -while under the pressure of time and resources, which are based on a drawing in a booklet that represents a set of user requirements that need to be implemented [START_REF]Hindsight: Try our bdd game[END_REF].

Methodology

"BDD Assemble!" is a competition among player teams to combine in short time proposed Gherkin sentences. Each team receives a story and 4 possible scenarios related to it. Similar pieces of Gherkin sentences for Given, When and Then are distributed for each team to be fit into their respective scenarios. Teams are challenged to produce correct sentences using the distributed pieces in each scenario, under the pressure of time against the other players. The team wins if it can complete more correct scenarios before the other competitors. Fig. 2 illustrates some prepared Gherkin sentences for possible scenarios in a ATM (Account is in credit, Account is overdrawn past the overdraft limit, Deposit money and Transfer between accounts) to be combined by each player team. As a verification approach for the game, the same was used by master degree students in a Software Engineering (SE) classroom (Fig. 3). In this activity, the students participated in a game match with 4-5 players each team, which was performed after the conclusion of the Acceptance Test class.

Conclusions and Future Work

This paper introduced "BDD Assemble!", a paper-based entertainment approach that allows the production of BDD scenarios by proposed Gherkin sentences in a colaborative and competitive way.

Satisfaction, usefulness, ease of learning and ease of use attributes was also identified during the game play by the evaluated students via USE questionnaire [START_REF] Lund | Measuring usability with the use questionnaire12[END_REF], confirming that the proposed game can combine fun and engaging with BDD learning. As future work, it is intended to produce a digital version of the proposed game, as well as more BDD scenarios together with the application and evaluation of the game in different SE classes.

Fig. 1 .

 1 Fig. 1. UserStory Game example [6].

Fig. 2 .

 2 Fig. 2. "BDD Assemble!" game play by master degree students in a SE classroom.

Fig. 3 .

 3 Fig. 3. "BDD Assemble!" game play by master degree students in a SE classroom.


Tiago Lemos
email: t.lemos@campus.fct.unl.pt

Teresa Romão

Nuno Correia

Miguel Pedro
email: miguel.pedro@watizeet.com

An Educational Game About Math and Magic

Keywords: Mobile game, Educational game, Interaction mechanisms, Shape recognition, Accelerometer

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

Introduction

Students spend a considerable portion of their time interacting with their phones. Mobile games are enjoyable for being simple and fun. However, most of them have no educational purpose. Meanwhile, most students struggle with some school subjects and end up losing the motivation to study. Therefore, we decided to create a mobile game that would be appealing while helping to strengthen math knowledge. We aimed at taking the focus away from the educational aspects, so that the players don't feel like they're studying. Math questions are answered in different ways, like leaning the phone, drawing on the screen or even having a math duel with a wizard.

In recent years, there has been more focus on using videogames as a learning method, mostly because of their ability to captivate attention and hold it for long. Educational games use interactivity to transmit knowledge, by requiring players to strategize, test hypothesis, or solve problems. These games usually include a system of rewards to motivate the player, a context to the activities, and learning content [START_REF] Dondlinger | Educational Video Game Design: A Review of the Literature[END_REF]. A handheld math facts game [START_REF] Lee | More than just fun and games: Assessing the value of educational video games in the classroom[END_REF] for second graders made those who played it solve three times more problems in the same time as those using paper worksheets.

Mobile games recently started being used in support of student learning, both in formal and informal settings [START_REF] Koutromanos | The use of mobile games in formal and informal learning environments: a review of the literature[END_REF]. Since these games can be played anytime and anywhere [START_REF] Jeong | Definitions, key characteristics, and generations of mobile games[END_REF], they don't necessarily need to be used in the classroom [START_REF] Seppala | Mobile learning in teacher training[END_REF] and have the potential to improve efficiency and effectiveness in teaching and learning [START_REF] Dubendorf | Wireless data technologies[END_REF] while also offering various unique and contemporary learning opportunities and promoting collaboration and interaction between players [START_REF] Koutromanos | The use of mobile games in formal and informal learning environments: a review of the literature[END_REF].

These games also help to develop a whole other set of important skills, such as creativity, decision-making, abstract thinking and visual and spatial processing. A study [START_REF] Waraich | Using narrative as a motivating device to teach binary arithmetic and logic gates[END_REF] found that students tend to be more motivated to play games that challenge them to utilize higher order thinking skills, where a strong narrative with fitting and tightly coupled learning tasks help motivate players to learn [START_REF] Swartout | Making a game of system design[END_REF], and that giving the player goals of different levels to achieve helps them being more engaged [START_REF] Dix | Human-computer interaction[END_REF].

Game Description

We created a mobile game to be played by fifth and sixth grade students during their free time aiming at reinforcing the knowledge they obtain at school, while using techniques to move the focus away from the educational aspects of the game. We do this by having not only different ways of answering questions, but also by adding decisionmaking and minigames, which helps the students enjoy playing and also obtain helpful knowledge. We focused on math, because it is a fundamental topic which is not naturally understood by all the students, but the game can be expanded in the future. During the design process we counted with the informal feedback of a fifth-grade student.

In the game, an evil wizard stole all the math in the world and hid in his magic mansion. The player's mission is to find and defeat him. The game consists in walking through the corridors and rooms of the mansion, overcoming the challenges that come up by using different interaction mechanisms. The game ends when the player reaches the wizard's room and defeats (or is defeated) by him in a math duel, consisting in a series of math questions answered by both the player and the wizard simultaneously. When the game starts, the player is asked to choose their school year and difficulty level, so the experience is tailored to their choices. By increasing the difficulty, the player will start the game with less lives available, and the evil wizard will become smarter, answering correctly more often during the final duel.

After a short introductory story to engage the player in the context of the game, the player enters the magic mansion through a first corridor (Fig. 1a). Each corridor has three different doors to choose from, each one leading to a different room. The player will find a different corridor every time they leave a room, which helps providing a feeling of choice and variety. There are several kinds of rooms available.

Key Room: In these rooms the player finds a key. The wizard's room is protected by magical wards, preventing the player to find and unlock it until they have all seven keys. In a key room, the player must click the key to collect it.

Question Room: Here the player finds the wizard, who escapes, locking the way out with a spell. To be able to exit the room the player must answer a math question related to a school subject. If they fail, they lose a life. The game has four kinds of questions:

• Multiple-choice: A question with four predefined answers from which the player must choose one, with no time limit but only one chance. • Drawing: A question to which the player must answer with a simple character (e.g. a one-digit number) by drawing it on the screen. Time limit of thirty seconds, with no limit of tries. • Tilting (Fig. 1b): A question with two possible answers. The player must tilt their phone and guide their character to the correct answer. • Clock (Fig. 1c): A question that requires the player to form an angle with the hands of a clock. The player is able to control only one of the hands, by tilting their phone, while the other hand is fixed.

After answering a question, the player receives feedback about the correct answer. The player then proceeds to another corridor, if they have not lost the game.

Treasure Room: Here, the player finds a chest they can decide to open. It contains one of the following artefacts:

• Crystal Ball: Remove two wrong answers in a multiple-choice question.

• Portal Scroll: Skip a question.

• Feather Pen: Change a question to another of the same kind.

• Life Medallion: Grants the player an extra life.

• Cursed Medallion: Takes a life from the player. All the artefacts have the same chance of appearing. The player is only able to carry one of each artefact at a time (except lives) and will keep them until used. The artefacts can be seen on the top center of the screen (Fig. 1c).

Minigame Room: This room allows the player to get an artefact of their choice by completing a minigame. Currently the minigame requires the player to tilt their phone in order to help the character move and catch potions falling from the sky. Catching the required number of potions within the time limit completes successfully the minigame. These minigames contribute to highlight the entertaining aspects of the game.

When the player obtains all seven keys, the magic wards are broken, and they'll find and face the wizard in a math duel. Both the player and the wizard must answer a series of multiple-choice questions (Fig. 1d). The player starts the duel with the lives they saved during the game, while the evil wizard always starts with full (five) lives. When the player answers a question, the evil wizard answers it too, and each wrong answer makes them lose a life. The wizard uses a simple algorithm to answer a question, where he can either choose the correct answer, or choose one of the answers at random. The chance of choosing the right answer increases with the difficulty level. Whoever manages to survive the longest wins the game.

User Study

To evaluate the game, two testing sessions were performed. The educational effectiveness of the game (through a math test), and the interest and enjoyment of the children were evaluated while playing (through observation, a small questionnaire and an informal conversation).

The first prototype had only one difficulty level, no minigames, no tilting or clock questions, and the drawing detection was not calibrated. Its evaluation provided us with valuable feedback to validate requirements and guide further developments. With this feedback a second prototype was implemented and evaluated, comprising all major features described in the previous section.

Two different classes of sixth-grade students participated in each of the sessions. Both sessions occurred in a classroom and lasted around 1h30m for each class, with a post-session one week later that lasted for 20 minutes. The same methodology was used in both test sessions:

• First the students answered a math test, about their knowledge prior to playing the game. The test lasted for 20 minutes. • After the test, we let them play the game. The participants installed the game on their phone or tablet and played it for 50 minutes while the research team members moved around the classroom, observing their behaviour and assisting when needed. • In the end, we offered a questionnaire to fill in and had an informal conversation with them. This lasted 20 minutes. • We then let them play the game on their own for a week.

• One week later we met again in the classroom to repeat the same math test they had done the previous week, so we could see if they had improved their math knowledge (they had no information the test would be the same, nor did they have the solutions). This lasted 20 minutes.

The test and questionnaire were individual, but the participants were allowed to join their friends and play together, as we wanted them to feel free and act as naturally as possible. A total of 29 sixth grade students (17 boys and 12 girls) with ages between 10 and 11, from two classes participated in the first evaluation. The test used to evaluate their improvement consisted in 16 open answer questions addressing both fifth and sixth grade subjects (as they were at the end of the sixth grade). The questions were based on subjects in the game. We obtained the following results:

• On average, students increased their scores by 10% and the highest score increased by 12%. • In a total of 29 students, 20 improved their score after playing the game, while 6 maintained it and 3 lowered it. • The student with the biggest improvement had an improvement of 44%.

To evaluate the players enjoyment, we gave them a questionnaire with a few questions, using the Smileyometer [START_REF] Read | Validating the Fun Toolkit: an instrument for measuring children's opinions of technology[END_REF] technique. From the first prototype, we learned that:

• Most students use their phone to play while using their computer to study. This supports our decision of creating a mobile educational game, as they can play on their phone and learn from it.

• When asked about the game, 91% enjoyed playing it, while 78% showed interest in playing it again, which were quite positive results. • When asked if they had trouble understanding the game, 57% answered "never" or "almost never", while 37% answered "sometimes" and 6% answered "very often". This was concerning, as it meant the interface wasn't as simple to understand as we thought. Thus, we decided to improve it according to the observations and the informal conversations.

• When asked about the game questions' difficulty, 77% found it to be appropriate, so there didn't seem to be reason for concern.

During play time, we observed that while the children started by playing alone, as time passed, they begin to interact with each other, helping their friends or challenging them to a speed run of the game. We also observed that the players were having some trouble with parts of the interface. During the whole session the children were very enthusiastic about the game and eager to collaborate. They provided us with plenty of ideas, like the possibility of choosing between difficulty levels, and the minigames.

The second prototype was tested by 30 students (11 boys and 19 girls) from two sixth grade classes, with ages between 10 and 11. This time we gave them a test consisting of 14 questions covering only fifth grade subjects (as the students were only starting the sixth grade). From this second prototype we obtained the following results:

• On average, students increased their scores by 11% and the highest score increased by 22%. • From 30 students, 18 improved their score after playing the game, while 10 maintained it and 2 lowered it. • The students with the biggest improvement had an improvement of 36%. These results, as the ones from the first evaluation, show that the game was effective at helping students obtain and retain math knowledge. We were not able to relate the playing time with their performance in the math test, as we couldn´t collect data regarding the students' usage of the game outside the classroom. We can, however, assume they had their usual study patterns during that week, as they had no exams planned.

From the second prototype's questionnaire we learned that:

• Most students use their phone to play. In this case they reported using the computer to play and study, and most of them also used a tablet to play. • About the game, 96% enjoyed playing it while 92% showed interest in playing it again. These were improvements from the already good results of the first session.

• When asked if they had trouble understanding the game, 84% answered "never" or "almost never" while 12% answered "sometimes" and 4% answered "constantly". This is also an improvement over the first prototype, as now a majority of players seems to understand the game easily.

• When asked about the questions' difficulty, 60% found it to be appropriate. While the percentage of players finding the questions' difficulty level appropriate has gone down, it can be justified as different kinds of questions were added to the game and the increased difficulty that was detected on the clock questions, something we have later improved.

• When asked the participants to sort the various parts of the game in terms of enjoyment, we found out the part that needed more improvement were the clock questions. We also found out that the players seem to enjoy more the parts that don't involve math questions, with the final duel being an exception to this. The favourite interaction was drawing the answer.

Again, the players were very excited, proposing new features to the game, including a labyrinth minigame. We can conclude we were successful in incorporating the educational aspects in a game that is fun while transmitting knowledge.

Conclusions

We created a game with the objective of helping students of the fifth and sixth grades obtain and retain math knowledge, while still having fun. Overall, we obtained good results from the tests we conducted, as a majority of players improved their knowledge after one week of playing the game, and also enjoyed the game. Thus, we can conclude we created a game that shifts the focus to its ludic aspect, while promoting learning. We plan to add new features to engage players as, for example, new unlockable characters to play with, and new kinds of challenges.

Fig. 1 .

 1 Fig. 1. a) Example Corridor. b) Example Tilting Question. c) Example Clock Question. d) Final Duel.

Acknowledgments

This work is funded by FCT/MCTES NOVA LINCS PEst UID/CEC/04516/2019.


Laura A Waters
email: laura.a.waters@uon.edu.au

Karen L Blackmore

Using Game-Based Environments to Measure Cognitive Decision Making

Keywords: 3D environment, cognition, visual search task, decision making

Within the area of serious games research, there is significant potential for researchers and other stakeholders to use serious games to gain more fundamental understanding of the underlying cognitive processes of individual users or participants. In this research, we present the results of an experiment to benchmark a visual search task presented in a 3d game-like environment with a standard, controlled, lab based implementation. Our results show similar trends in performance measures across experimental conditions in the two environments, however, participants were faster and more accurate overall in the 3d game-like environment. There is significant potential for researchers and other stakeholders to utilise serious games platforms as a means of measuring human cognition within environments that are visually more closely related to 'real-life' than those used in cognitive psychology.

Introduction

Serious games (SGs), or games that are used for a purpose other than purely entertainment, are being used more frequently within industries for assessment and training of specific skills and abilities. While some performance measures may be captured, there is great benefit in understanding the types of cognitive processes that underlie abilities. One important research area of cognitive psychology is decision-making; that is, how we process information to make a judgment. This is often done through lab-based experiments that ask participants to make simple cognitive judgments about simple perceptual stimuli.

Many SG models acknowledge that at the crux of any learning activity, there is a cognitive process occurring [START_REF] Mayer | The research and evaluation of serious games: Toward a comprehensive methodology[END_REF][START_REF] Yusoff | A Conceptual Framework for Serious Games[END_REF]. However, the incorporation of robust measures of cognitive performance in game based environments is not currently done, even though the captured data might shine a light on understanding the processes underlying their cognitive abilities [START_REF] Gunter | A case for a formal design paradigm for serious games[END_REF][START_REF] Mayer | The research and evaluation of serious games: Toward a comprehensive methodology[END_REF][START_REF] Shute | Learning Processes and Learning Outcomes[END_REF]. Within the field of computer science, there has been extensive research into 2D environments and evidence has shown that visual search abilities improve with gaming [START_REF] Castel | The effects of action video game experience on the time course of inhibition of return and the efficiency of visual search[END_REF], however these findings were often incidental outcomes and visual search abilities have not been explicitly considered in an immersive game-based environment in a way the relates to the more formal, constrained approach used in cognitive psychology [START_REF] Lee | Characteristics of Internet Use in Relation to Game Genre in Korean Adolescents[END_REF].

In this work we present the results of a pilot study to compare visual search task performance, as measured through reaction time (RT), between a standard 2D constrained lab based task, and a 3D game-like visual environment. The focus here is to assess whether the deviations in RTs that occur when the number of visual stimuli change maps between the 2D lab based task and the 3D counterpart. We begin by providing a brief background to the measurement of human cognition, followed by an outline of our research focus, the experimental methodology and the results of our pilot study. Lastly, we provide some concluding discussion, and outline potential avenues for future work.

Background

Measuring Human Cognition

Cognitive psychology uses robust methodological approaches to test an individuals underlying processing abilities and capacity when performing particular cognitive tasks. There are a number of robust tools and tasks that are used to understand cognitive performance that can provide this broader understanding of capability; for example, surveys, psychometrics, observation, randomised controlled trials (RCTs). One key difficulty in being able to generalise task performance to real world behaviours lies in the highly constrained nature of lab-based environments and artificial stimuli used [START_REF] Ladouce | Understanding minds in real-world environments: toward a mobile cognition approach[END_REF], such as simple shapes. These concerns relate to the overall context of the results, and the potential impact of this context on performance [START_REF] Norman | Generalization and the qualitative-quantitative debate[END_REF]. On the other hand, studies of entertainment games and SGs tend to use quasi-experimental designs and surveys and largely tend to measure post-intervention activity engagement [START_REF] Smith | A Meta-Analysis of Data Collection in Serious Games Research[END_REF]. While studies using quasi-experimental designs and surveys have added to our understanding of the outcomes and impacts of playing games, RCTs provide more rigorous evidence about the impacts of games. Although, laboratory-based tests provide a robust foundation for our understanding of decision making processes, more research is required for conversion of highly constrained lab based tasks and measures into other contexts such as more realistic virtual games-based environments. The rise of SGs and immersive simulation environments provides a platform for a more realistic and variable environment which could be used for cognitive testing and training purposes. Simplistically, performance data can be used to profile players or measure competency [START_REF] Busch | Using Player Type Models for Personalized Game Design-An Empirical Investigation[END_REF], and there is promise for game environments to be used to capture more cognitive level data to assess cognitive processing architecture and capacity.

Research Focus

At the higher level, the purpose of this research is to develop the foundations of a theoretical framework that uses the strengths of two currently separate research areas to address some of their respective limitations. On one hand, cognitive psychology uses robust methodological approaches to design, measure performance, empirically analyse, and make inferences about the underlying cognitive processes and abilities that people use to complete a task or make decisions. We understand that human cognition plays a role in nearly every aspect of every day life, however, due to the highly constrained nature of lab-based tasks, it can be difficult to generalise results to how an individual might approach tasks in their everyday environment. On the other hand, the gaming industry has conducted extensive research into human-computer interaction, engagement and player typing/preferences. By combining aspects of SGs with cognitive labbased approaches, we aim to develop a framework which provides guidelines to both researchers and game designers on a) how to increase engagement and realism when developing experiments in cognitive psychology, and b) how to embed robust cognitive methodology in a SG. This would allow for finer grain data and a greater depth of understanding about how individuals are processing and engaging within the environment. This can help us to adapt or optimally design games for learning to accommodate cognitive styles. This is particularly relevant for SGs, where these games are typically deployed across a cohort of individuals with different player profiles and styles.

One overall end-goal of this research will consider how virtual environments impact users; that is, do people behave and make decisions in a more realistic and virtual environment in the same way they would in standard artificial labbased environments. However, in order to retain the robustness and validity of any cognitive measures used in a game-based environment, we first need to lay the groundwork for ensuring reliable measures of in-game performance by benchmarking performance on simple tasks using the highly constrained approach of cognitive psychology. Although we are restricting participant interaction with the 3D game-like environment, the goal of this pilot study is to ensure that the more visually immersive environment is still capturing valid and reliable data that can be used as a performance baseline for future studies in which we allow greater interactive possibilities for participants. As a means of benchmarking performance on a cognitive task between a standard 2D lab-based cognitive psychology test and a 3D game-like environment, we elect to design and pilot a simple visual search task and replicate this in a restricted game-like environment. Python programming language was used to create the 2D lab-based task whereas we used Virtual Battlespace 3 (VBS3) [START_REF]Bohemia Interactive[END_REF], a visually realistic game environment with scenario creation tools, to create the 3D game-like environment.

We hypothesise that participants will have a preference for the mechanics used in the immersive environment of VBS3, and that this will be reflected in this pilot experiment through faster RTs and greater accuracy even though the cognitive processes involved should remain the same. If we find evidence to support the hypothesis that game-based environments are a valid way to deliver cognitive tasks and assess underlying cognitive processes, then we are developing the capacity to re-deploy this back into the design of SGs and potentially allow for greater flexibility in the realism of environments used for cognitive testing.

Methodology

In this pilot study, we designed a simple visual search task [START_REF] Wolfe | Visual attention[END_REF] within a 3D virtual environment (Virtual Battlespace 3) which replicated the experimental design of a typical 2D laboratory-based task requiring participants to make a simple decision. Further details on the environments and visual stimuli are provided in the materials section below. The research was conducted under the University of Newcastle's Human Research Ethics Committee approval number H-2018-0227.

Participants

Participants were undergraduate students from the University of Newcastle. Of the 41 students who completed the experiment, there were 28 males (M = 22.23 years, SD = 3.71) and 13 females (M = 25.38 years, SD = 9.91) ranging from 18 to 52 years of age. Participants were enrolled in either a second year software engineering course (28 males and 2 females respectively) or a first year introductory psychology course (11 females) and received course credit for their participation.

Materials and Design

The experiment was conducted in one of the University of Newcastle HCI labs using an Alienware 17 R3 laptop. PsychoPy2 (v1.90.3) was used to develop and run the 2D laboratory-based task, while the 3D game-based task was run using VBS 3.7.0.

Design In each of the environments, participants were presented with either 5, 10, 15 or 20 items (stimuli) in a single 4 second trial and required to make a simple decision on whether a target item was present or not. The desired target depended on the presented colour arrangement of an item. Stimuli used in standard cognitive experiments are often highly constrained shapes or letters which are varied visually through the use of colours, location, or rotation [START_REF] Wolfe | Visual attention[END_REF]. Replication of our task in a 3D game-like environment meant that to achieve a higher level of realism, abstract visual stimuli could not be used in both conditions. As such, this experiment used human avatars in VBS3 to address the criteria of perceptual salience between targets and distractor items, relying on colour arrangement of clothing to provide distinction. In each environment, the target (2D: a rectangular, two toned shape, and 3D: a human avatar, see Figure 1 and 2 for example trials) would be tan on top and red on the bottom, while both distractor types would be tan on the bottom half, and either red or yellow on top.

For each task, a practice block with feedback (correct or incorrect response) was completed prior to undertaking 10 experimental blocks, each containing 40 trials (no feedback). We counterbalanced task and response key orders to account for any potential ordering effects. We also asked participants to complete two (2) questionnaires; one pre-test demographic survey containing additional questions regarding gaming preferences and behaviours, and one post-test where we asked participants about the difficulty of the two tasks and whether they were aware of using a particular search strategy to reach their decision.

Results

Preliminary Analysis

Surface level analysis of this pilot experiment revealed a significantly faster mean RT (M = 0.74s) in the 3D environment compared to the 2D environment (M = 1.02s), t = 74.515, p < .001, as well as higher accuracy overall (M = 99% and M = 97% for 3D and 2D respectively), t = -9.191, p < .001. At first glance it could be proposed that in each task participants respond 'yes' as soon as the target item has been identified (self-terminating cognitive architecture) on target present trials, or exhaustively search all items in target absent trials before responding 'no'. [START_REF] Townsend | Serial vs. Parallel Processing: Sometimes They Look like Tweedledum and Tweedledee but they can (and Should) be Distinguished[END_REF]. This is reflected in both 2D and 3D environments by the slower average RTs on trials where no target item was present (see below figure).

Discussion

Two main arguments in favour of using game-like or scenario based platforms for teaching and training purposes are that firstly, they are more engaging and therefore more effective as a training tool [START_REF] Clark N Quinn | Engaging learning: Designing e-learning simulation games[END_REF], and secondly, they can be more closely related to the real world meaning an individuals performance is more likely to reflect actual behaviour [START_REF] Ritterfeld | Serious games: Mechanisms and effects[END_REF]. Unfortunately, the reported benefits and effectiveness of these training approaches are often mostly subjective rather than objectively measured, and assumptions are often made about generalisability to real world behaviours.

The findings of this simple pilot experiment indicate that embedding cognitive tasks within virtual training environments with high levels of 'realism' hold promise as a comparative environment for the capture of robust measures of underlying cognitive processes. The group level trends of slower RTs on trials where target item was absent or when there was a greater number of items to be processed (as seen in figure 3), indicates that participants were engaging similar underlying cognitive decision-making processes despite the different environments. While the design of the task itself was highly constrained, it is worth noting that the faster response times and improved accuracy in the VBS3 environment may be impacted by additional factors such as a discrepancy in the perceived perceptual planes of the two environments. This factor warrants further investigation and as such, future experimentation will focus on adjusting the locations of the avatars as well as the viewing angle of the 'player avatar' so the spatial locations are more closely aligned with the 2D plane presented in the comparative environment. By further researching performance measures within these 2D and 3D comparative environments, we aim to develop a foundation for designing and implementing tasks within SGs that can provide rich, robust and valid measures of a users cognitive processing abilities. This would also provide researchers, educators, or game designers with greater insight into the cognitive abilities, interactions and learning styles of specific SG users.

Fig. 1 .

 1 Fig. 1. An example 15-item set presented in the 2D environment. The target item is absent from this trial.

Fig. 2 .

 2 Fig. 2. An example 20-item set from the VBS3 (3D) environment. The target avatar is present on this trial.

Fig. 3 .

 3 Fig. 3. The absolute RTs averaged across participants can be shown in the two graphs above (Left graph: RTs for the 2D environment, Right graph: RTs for the 3D VBS3 environment). Participants were significantly faster when completing the task in VBS3 compared to the 2D python environment, however in both tasks were participants tended to respond slower to trials without a target present.