Game Levels Research Articles

The REThink Online Therapeutic Game: A Usability Study.

Children and adolescents' help-seeking behaviors are often limited by fear, uncertainty, and stigma, as well as challenges with finding the right intervention, help, and a lack of familiarity with the process. A promising direction for the development of therapeutic interventions targeted at children is embedding them into gamified interventions, known as therapeutic or serious games. The aim of this paper is to describe the protocol of the beta REThink therapeutic game and to evaluate the usability of the game in a sample of children and adolescents. The study was delivered in schools, where 137 children and adolescents aged between 9 and 16 years old played the levels of the REThink game, followed by filling in the usability questionnaire. The results show above average evaluations for all levels of the game, for every dimension evaluated, namely presence/immersion, enjoyment, learning effectiveness, narratives, goal clarity, adequacy of learning material, and motivation. This study contributes to the literature on the usability of cognitive behavioral therapy-based therapeutic games for improvements in the emotion regulation abilities of children and adolescents, which can guide researchers interested in testing the REThink game in different protocols, as well to encourage its use by mental health specialists and parents.

CNN sensor based motion capture system application in basketball training and injury prevention

Basketball is a high-intensity sport, and sports injuries often occur. Therefore, how to monitor the sports status of basketball players in real time, discover and prevent the occurrence of sports injuries in time, has become an urgent problem for athletes and coaches. In this paper, a motion capture system based on CNN sensor is proposed. Through the application of sensor, real-time monitoring of athletes' motion state, the system can collect athletes' movement track, speed, acceleration, stride frequency, heart rate, energy consumption and other parameters in real time. By analyzing the movement data, it can timely warn and deal with the occurrence of sports injuries. In this paper, convolutional neural network (CNN) is used to process and analyze motion data, so that the motion capture system has higher precision and accuracy. Through the training and optimization of CNN, the system can identify and analyze motion data more accurately, and improve the performance and effect of the motion capture system. The motion capture system based on CNN sensor can realize real-time monitoring and prevention of sports injuries for basketball players, provide more comprehensive, scientific and real-time sports data for athletes and coaches, help them better conduct training and adjust tactics, and improve the competitive level and safety of basketball games.

Poster 381: The Current NFL Schedule is not a Risk Factor for Musculoskeletal Injuries; Epidemiology of NFL Injuries From 2017-2022

Objectives: The National Football League, among the most popular team sports in the United States, carries an injury incidence that is higher than that of other collision sports, such as rugby and ice hockey. Considerable research has been undertaken to investigate the etiology of injuries in the NFL and has identified numerous risk factors on the level of individual games, including playing surface and gameday weather conditions; additionally, one study has found that concussion risk is not affected by scheduling variation, such as overseas games and bye week timing. However, there is currently no data that reports the effects of scheduling variation over the course of a season on musculoskeletal injury incidence. The purpose of this retrospective study was to describe the relationship between scheduling factors and injury rates among professional athletes in the NFL. It was hypothesized that higher cumulative travel distance, playing overseas games, an early season bye week, and an expansion of the regular season would be associated with higher injury rates. Methods: The current study was a descriptive epidemiological study that retrospectively reviewed all injuries that occurred in the five NFL seasons from the 2017-2018 season through the 2021-2022 season. Injury data and travel distances were extracted from publicly available sources and injury reports were cross-referenced to verify injury data. Injury rates were calculated per 1,000 athletic exposures (AEs). Results: In 1,216 regular season games in the five NFL seasons, 4,378 injuries were reported for an average injury rate per year of 34.42 injuries per 1,000 AEs. Cumulative team travel distance did not significantly predict injury rates (Figure 1). There was not a statistically significant difference in injury rates between NFL teams that did (19.3 per 1,000 AEs) versus did not play a game overseas (19.3 per 1,000 AEs) (Figure 2). There was not a statistically significant difference in injury rates or the number of players on the injured reserve list between the various bye weeks. There was not a statically significant difference in injury rates between the new 17-game regular season (18.4 per 1,000 AEs) versus the previous four 16-game regular seasons (19.7 per 1,000 AEs) (Figure 3). However, NFL teams that did not qualify for the playoffs had a significantly higher injury rate (19.9 per 1,000 AEs) versus teams that did qualify for the playoffs (18.4 per 1,000 AEs). Also, NFL teams that did not qualify for the playoffs had a significantly higher number of players on the injured reserve list (8.0 per 1,000 AEs) versus teams that qualified for the playoffs (6.8 per 1,000 AEs) (Figure 4). Conclusions: Over five NFL seasons, increased cumulative travel distance, playing a game overseas, and having an early season bye week were not associated with increased injury rates in professional football players. The addition of one regular season game, from 16 to 17 during the 2020 season, was not associated with an increased injury rate. However, not qualifying for post-season play was associated with an increased injury rate and an increase in the number of players on injured reserve. Overall, the analysis suggests that the analyzed parameters in the current NFL schedule were not risk factors for musculoskeletal injuries over the last five seasons; however, lack of regular season success may be associated with higher injury rates and more players on injured reserve.

Gestalt Principles Governed Fitness Function for Genetic Pythagorean Neutrosophic WASPAS Game Scene Generation

The maintenance of visual appeal and coherence in the procedural game scene generation is still a difficult problem. Traditional procedural game scene generation algorithms produce samples that show a noticeable resemblance to each other. The proposed algorithm allows us to add diverse game object compositions and increase creativity value in that way. Result diversity is formed by the proposed genetic algorithm modification and MCDM method based on the fitness function. Video game immersion is reached by aesthetic game element pattern composition, and one of the solutions for this issue is to apply automated aesthetic modelling of the generated game levels. In this research, the construction of fitness function was extended by the modelling of aesthetic principles, which were reverse-engineered from Gestalt principles. All rules were implemented by construction of a focal function with a square zone for each matrix cell of the single game scene. Five types of Gestalt rules were modelled and combined into a Pythagorean neutrosophic WASPAS method and the final score calculation algorithm was proposed. The proposed approach to generating game scenes strikes a balance between functionality and aesthetics to provide players with an engaging and immersive gaming experience.

Persepsi Orang Tua Terhadap Cara Menstimulasi Otak Anak Melalui Permainan Puzzle

Children who receive less brain stimulation, their development will be hampered. This has an impact on his future because brain damage early on is more difficult to repair. Children who get stimulation according to their brain development will be more successful in school. This research methodology is a descriptive research with a quantitative strategy. The questionnaire provided consists of 6 questions with 10 alternative answers. The results showed that 100% of respondents said puzzle games could improve short-term memory skills. 100% of respondents said puzzle games could train children to solve a problem. In the statement the level of difficulty (level) in the puzzle game does not have to be in accordance with the child's age, 62.57% of respondents said it was wrong and 37.5% of respondents said it was correct. 100% of respondents said puzzle games can train children's brain cells to develop their thinking and concentration abilities. As many as 100% of respondents stated that puzzle games were able to develop children's creativity and imagination. As many as 100% of respondents stated that children learn to recognize different shapes, sizes, and colors in each object. It can be concluded that all respondents are good. Parental influence, involvement, and guidance are essential for healthy braindevelopment. So the results of the analysis above as seen from the respondents show that pizzles can encourage children's brain development.

Reinforcement Learning With Dual-Observation for General Video Game Playing

Reinforcement learning algorithms have performed well in playing challenging board and video games. More and more studies focus on improving the generalisation ability of reinforcement learning algorithms. The General Video Game AI Learning Competition aims to develop agents capable of learning to play different game levels that were unseen during training. This paper summarises the five years' General Video Game AI Learning Competition editions. At each edition, three new games were designed. The training and test levels were designed separately in the first three editions. Since 2020, three test levels of each game were generated by perturbing or combining two training levels. Then, we present a novel reinforcement learning technique with dual-observation for general video game playing, assuming that it is more likely to observe similar local information in different levels rather than global information. Instead of directly inputting a single, raw pixel-based screenshot of the current game screen, our proposed general technique takes the encoded, transformed global and local observations of the game screen as two simultaneous inputs, aiming at learning local information for playing new levels. Our proposed technique is implemented with three state-of-the-art reinforcement learning algorithms and tested on the game set of the 2020 General Video Game AI Learning Competition. Ablation studies show the outstanding performance of using encoded, transformed global and local observations as input.

Dikkat eksikliği/hiperaktivite bozukluğu olan ergenlerde çevrimiçi oyun özellikleri

Aim: Leisure activities in adolescents with Attention-deficit/hyperactivity disorder (ADHD) tend to focus on Internet activities, especially online games. Online gaming addiction has been observed in ADHD populations. Game genres and characteristics may affect addiction in ADHD adolescents who play online games. This study aims to contribute to the literature by examining the Internet gaming disorder (IGD) risk effects and game genres of hyperactivity/impulsivity, inattention, and ADHD index separately. Methods: A total of 884 adolescents playing online games were included. Participants answered the online game features form, Conners-Wells’ adolescent self-report scale—long version and digital gaming addiction scale—short form under the observation of their teachers. Game genres and ADHD symptomatology (Inattention, Hyperactivity/impulsivity, ADHD index) were categorized within themselves. Results: IGD was significant in all subcomponents of ADHD symptomatology. In all subcomponents, insulting/threatening speeches were found in the chat tab. In the Hyperactivity/impulsivity component, more hours of online gaming and spending real money on in-game advantages were found. In the Inattention and ADHD index, the amount of gaming in the social environment of the adolescents was high. In ADHD symptomatology, no significant results were found in game genres (except sports in the Inattention component). nclusion: Although levels of online gaming are high in adolescents with ADHD, a bidirectional relationship is likely. We believe that social factors and communicative characteristics are important in relational factors in adolescents. Longitudinal studies should be conducted to answer important empirical questions about the relationship between ADHD-IGD and game genres.

Preschoolers' Mathematics Game Preferences and Learning Performance through Designing a Degree of Freedom for a Tablet Game.

Children always have a natural inclination to play, so the purpose of game-based learning is to establish a way for children to acquire knowledge while playing. This study is to discover the relationship between children's play preferences and learning performances toward a mathematics mobile game we designed. The tablet game we designed, "Lily's Closet", is a mathematics game that guides children aged 3-8 to learn "classification". To explore the preferences and learning performances of the games we designed for the preschool children, we put "Lili's Closet" on a children's tablet named Kizpad with over 200 games. We classify and analyze players' behavior data by Data Mining in our designed game to explore children's play behaviors and preferences. We sampled 6,924 children from Taiwan, aged 3-8 years old. In the results, we found that there is a significant difference in the number of ages and achievements in the game. The maturity of the child's age is positively correlated with the achievement of the game, but it is negatively correlated with the number of times of willing to play. As a result, we suggest that to assist learning, we should give children different levels of games according to their ages. The research hopes to resonate with readers and jointly explore the relationship between mobile games.

(PANDEMIC Covid-19): A Shooter Game for Education - the Impact Measurement of War Games on Virus Eradication Lessons for Students

(PANDEMIC Covid-19) is an educational shooter game inspired by the Covid-19 pandemic which occurred from the end of 2019 until early 2022. There are 2 game modes, namely Third-Person Shooter, or TPS, and First-Person Shooter, or FPS. This study was carried out to highlight the absence of a shooter genre game used in the student learning process. The research methodology in the development of this game applied the pressman method, and the stages include planning, analysis, game development and artificial intelligence, implementation, as well as  evaluation. Furthermore, the testing phase used software testing techniques based on the ISO 9126 standard and involved a total of 100 participants. The age range was between 17 and 20 years, while the participants' gender percentages were 55% male and 45% female. Some of the factors tested include functionality, reliability, portability, usability, efficiency, and maintainability. There were 2 choices only in this test, i.e. agree and disagree. The functionality factor had an agreed rate of 85%; reliability 79%, portability 86%, usability 83%, efficiency 79%, and maintainability 87%. Therefore, it was concluded that this game is suitable for use in student learning in the shooter genre. Furthermore, this research was inspired because shooter games have not been developed for the student learning process. This game genre is currently used for hobbies and for profit by developers and professional players. Further research should develop game levels, enable features to play online together with other users, and should be extended to Android and IOS.Â

Investigating gamification services of university libraries in China

Purpose This study aims to analyze the construction of gamification service in university libraries in China and to help university libraries improve service and user experience. Design/methodology/approach This study took 147 university libraries in China as research object, analyzed the gamification services announced by these libraries on official websites, Weibo, WeChat and other platforms during the period from July 1, 2021 to August 20, 2022, and investigated the gamification services from the four aspects of functions, forms, game elements and mechanisms. Findings The results show that the gamification service of university library in China is often used in the popularization of scientific and cultural knowledge, information literacy education, reading promotion, library service promotion and library entrance education. Questions and answers, points contests, passing the game level and treasure hunting are common forms of games. At the same time, many gamification services combine game elements such as points, leaderboards and grades with the game mechanism to enhance readers’ participation. Originality/value This study investigates gamification services of university libraries in China and provides suggestions for optimizing gamification services, which can provide reference for libraries in China and other countries.

Start small: Training controllable game level generators without training data by learning at multiple sizes

A level generator is a tool that generates game levels from noise. Training a generator without a dataset suffers from feedback sparsity, since it is unlikely to generate a playable level via random exploration. A common solution is shaped rewards, which guides the generator to achieve subgoals towards level playability, but they consume effort to design and require game-specific domain knowledge. This paper proposes a novel approach to train generators without datasets or shaped rewards by learning at multiple level sizes starting from small sizes and up to the desired sizes. The denser feedback at small sizes negates the need for shaped rewards. Additionally, the generators learn to build levels at various sizes, including sizes they were not trained for. We apply our approach to train recurrent autoregressive generative flow networks (GFlowNets) for controllable level generation. We also adapt diversity sampling to be compatible with GFlowNets. The results show that our generators create diverse playable levels at various sizes for Sokoban, Zelda, and Danger Dave. When compared with controllable reinforcement learning level generators for Sokoban, the results show that our generators achieve better controllability and competitive diversity, while being 9× faster at training and level generation.

MODERN CONCEPTS OF GAMIFICATION IMPLEMENTATION IN THE TRAINING SYSTEM FOR TEACHERS OF THE UKRAINIAN AND ENGLISH LANGUAGES

The article clarifies that for the young people of the Zoom generation who have grown up in the age of the Internet, have unlimited access to any information and since childhood have been using digital technologies, classical higher education cannot provide everyone with necessary competencies and soft-skills by traditional methods. The study has defined that gamification is the introduction of computer games into educational activities, adding game elements to increase visualization, motivation training and, accordingly, the level of mastery of theoretical material, i.e. feedback with a student. It has been proved that gamification helps identify a leader and determine the level of training of an education seeker on this or that question (subject); unite students with a common language literary idea and direct all their efforts to its disclosure and representation; reduce the level of conflicts, and increase effectiveness of the educational process. The following components of the gamified process (user; accumulated points; game levels; ranking of participants; badges), forms (competitive, victorious and aesthetic) and services for introducing gamification into the education system (Alice, Scratch, Studio Code; CodeSchool; MotionMathGames; Mathematics; Spongelab; Kahoot!; Zombie-BasedLearning; MinecraftEdu; World of Classcraft (WoC); WildInternetWoods) have been characterized. The prerequisites for the implementation of the gamification method, to which the level of theoretical training of students, educational content, motivation of students of higher education to perform certain tasks, a level development of mental processes (thinking, perception, memory, attention, imagination and etc.) have been analyzed. The system of tasks, a quality control of which is organized by using games developed with the help of online Wordwall service has been developed and presented. The effectiveness of the implementation of the gamification method into the process of training of future teachers of the Ukrainian and English languages, which convincingly is represented by the diagram has been proved. Â

INTEGRATION OF PROCUREMENT VALUE: REVIEW OF ONLINE GAMERS CHARACTER IDENTIFICATION AND INFORMATION SUPPLY BEHAVIOUR

The main intention of the study was to uncover the values that online gamers held during the process of observing, assessing and finally held the procurement process. Those values been interpreted and describing a crystal-clear pattern of behavior of the gamers. Survey was conducted to identify as well as reviewed the character of the respondents whose all player of online game. The research took place at surrounding teens neighborhood with the range of age various but most dominantly by college ages. The method of random interview and quick assessment on questionnaire were held to justify the result. It was confirmed from the final observations and data analysis that character identification, integrated value and the way player feel related to the feature of the game significantly gave impact on how they made procurement pattern and intentions. Further research was suggested to observe more on the variety of respondents occupations as the spending power on game level were various.

Concussion in Canadian Youth Ice Hockey: What Is Needed to Decrease the Burden?

Concussion in Canadian Youth Ice Hockey: What Is Needed to Decrease the Burden?

Cross-Task and Cross-Participant Classification of Cognitive Load in an Emergency Simulation Game

Assessment of cognitive load is a major step towards adaptive interfaces. However, non-invasive assessment is rather subjective as well as task specific and generalizes poorly, mainly due to methodological limitations. Additionally, it heavily relies on performance data like game scores or test results. In this study, we present an eye-tracking approach that circumvents these shortcomings and allows for effective generalizing across participants and tasks. First, we established classifiers for predicting cognitive load individually for a typical working memory task (n-back), which we then applied to an emergency simulation game by considering the similar ones and weighting their predictions. Standardization steps helped achieve high levels of cross-task and cross-participant classification accuracy between 63.78% and 67.25% for the distinction between easy and hard levels of the emergency simulation game. These very promising results could pave the way for novel adaptive computer-human interaction across domains and particularly for gaming and learning environments.

Association between electronic device use and health status among a middle-aged and elderly population: a cross-sectional analysis in the UK Biobank.

Few previous studies have investigated the impact of multiple types of electronic devices on health status, and the moderating effects of gender, age, and BMI. Our aim is to examine the relationships between the use of four types of electronics and three health status indicators in a middle-aged and elderly population, and how these relationships varied by gender, age, and BMI. Using data from 376,806 participants aged 40-69 years in the UK Biobank, we conducted a multivariate linear regression to estimate the association between electronic device use and health status. Electronics use was categorized as TV watching, computer use, computer gaming, and mobile phone use, and health status included self-rated health (SRH), multisite chronic pain (MCP), and total physical activity (TPA). Interaction terms were utilized to assess whether the above associations were modified by BMI, gender, and age. Further stratified analysis was performed to explore the role of gender, age, and BMI. Higher levels of TV watching (BSRH = 0.056, BMCP = 0.044, BTPA= -1.795), computer use (BSRH = 0.007, BTPA= -3.469), and computer gaming (BSRH = 0.055, BMCP = 0.058, BTPA= -6.076) were consistently associated with poorer health status (all P < 0.05). Contrastingly, earlier exposure to mobile phones (BSRH = -0.048, BTPA= 0.933, BMCP = 0.056) was inconsistent with health (all P < 0.05). Additionally, BMI (Bcomputer use-SRH= 0.0026, Bphone-SRH= 0.0049, BTV-MCP= 0.0031, and BTV-TPA= -0.0584) exacerbated the negative effects of electronics use, and male (Bphone-SRH = -0.0414, Bphone-MCP = -0.0537, Bphone-TPA= 2.8873) were healthier with earlier exposure to mobile phones (all P < 0.05). Our findings suggest that the adverse health effects associated with watching TV, computer use, and computer gaming were consistent and were moderated by BMI, gender, and age, which advances a comprehensive understanding of the association between multiple types of electronic devices and health status, and provides new perspectives for future research. The online version contains supplementary material available at 10.1007/s10389-023-01886-5.

Application of Variational AutoEncoder (VAE) Model and Image Processing Approaches in Game Design

In recent decades, the Variational AutoEncoder (VAE) model has shown good potential and capability in image generation and dimensionality reduction. The combination of VAE and various machine learning frameworks has also worked effectively in different daily life applications, however its possible use and effectiveness in modern game design has seldom been explored nor assessed. The use of its feature extractor for data clustering has also been minimally discussed in the literature neither. This study first attempts to explore different mathematical properties of the VAE model, in particular, the theoretical framework of the encoding and decoding processes, the possible achievable lower bound and loss functions of different applications; then applies the established VAE model to generate new game levels based on two well-known game settings; and to validate the effectiveness of its data clustering mechanism with the aid of the Modified National Institute of Standards and Technology (MNIST) database. Respective statistical metrics and assessments are also utilized to evaluate the performance of the proposed VAE model in aforementioned case studies. Based on the statistical and graphical results, several potential deficiencies, for example, difficulties in handling high-dimensional and vast datasets, as well as insufficient clarity of outputs are discussed; then measures of future enhancement, such as tokenization and the combination of VAE and GAN models, are also outlined. Hopefully, this can ultimately maximize the strengths and advantages of VAE for future game design tasks and relevant industrial missions.

Design and Realization of Surakarta Chess Game Program

At present, the research on Surakarta game is relatively less than other games. Aiming at this situation, a Surakarta chess game program is designed. This program implements basic algorithms such as board representation, move generation, game evaluation, and game tree search. The evaluation of the chess game takes into account the number of pieces, moving range, attacking range, attacking power of pieces, board value, arc value, etc. The game tree search algorithm uses the Alpha-Beta pruning algorithm. The whole program is fully functional and has a strong game level, which provides reference significance and reference value for related research. The program won the second prize at the national level in the computer game competition for college students in China, and the second prize at the provincial level in the computer game competition for college students in Liaoning Province, which verified the feasibility of the game program algorithm.

Synthesizing Game Levels for Collaborative Gameplay in a Shared Virtual Environment

We developed a method to synthesize game levels that accounts for the degree of collaboration required by two players to finish a given game level. We first asked a game level designer to create playable game level chunks. Then, two artificial intelligence (AI) virtual agents driven by behavior trees played each game level chunk. We recorded the degree of collaboration required to accomplish each game level chunk by the AI virtual agents and used it to characterize each game level chunk. To synthesize a game level, we assigned to the total cost function cost terms that encode both the degree of collaboration and game level design decisions. Then, we used a Markov-chain Monte Carlo optimization method, called simulated annealing, to solve the total cost function and proposed a design for a game level. We synthesized three game levels (low, medium, and high degrees of collaboration game levels) to evaluate our implementation. We then recruited groups of participants to play the game levels to explore whether they would experience a certain degree of collaboration and validate whether the AI virtual agents provided sufficient data that described the collaborative behavior of players in each game level chunk. By collecting both in-game objective measurements and self-reported subjective ratings, we found that the three game levels indeed impacted the collaboration gameplay behavior of our participants. Moreover, by analyzing our collected data, we found moderate and strong correlations between the participants and the AI virtual agents. These results show that game developers can consider AI virtual agents as an alternative method for evaluating the degree of collaboration required to finish a game level.

Morphological and hand grip strength characteristics and differences between participants of the 2022 world rowing championship.

Rowing is a strength endurance type of sport, and morphology and mass are undoubtedly performance-related factors. Precisely identifying these morphological factors associated with performance, can assist the exercise scientists and coaches in selecting and developing talented athletes. There is however, a lack of anthropometric data collected at either World Championship or Olympic Games level. The aims of this study were to describe and compare the morphology and basic strength characteristic of male and female heavyweight and lightweight rowers competing at 2022 World Championship (18.-25. September, Račice, Czech Republic). A total of 68 athletes (of 46 male competitors: 15 competed in the lightweight category and 31 in the heavyweight category; of 22 female athletes: 6 competed in the lightweight category and 16 in the heavyweight category) were assessed using anthropometric methods, bioimpedance analysis and performed a hand-grip test. Between heavyweight and lightweight male rowers there were a statistically and practically significant differences in all monitored aspects except the sport age, sitting height/body height ratio and arm span/body height ratio. Between heavyweight and lightweight female rowers there were also statistically and practically significant differences in all monitored aspects except the identical indicators as in male. Within this research it can be argued that female rowers are in many anthropometric aspects more similar to their male counterparts than to female rowers in the lightweight category. In some anthropometric aspects (BMI, thigh girth, calf girth), female rowers are even more similar to male heavyweight than to male lightweight rowers. The physical characteristics of elite male and female lightweight rowers differ radically from those of heavyweight. From a practical point of view, this research can be used to determine what type of athletes should be recruited or selected for heavy category and what type for lightweight category in male and female rowing based on the somatotype.