Entertainment Computing最新文献

The present study describes the evaluation of Regulatia, an immersive web-based educational game for pre-service teachers to promote self-regulated learning (SRL). Based on Zimmerman’s model of SRL, learners immerse themselves in the underwater kingdom Regulatia and must find a way back home. Regulatia fosters the use of SRL-specific strategies and combines game elements with learning content. In this paper, the goal is to evaluate the first functional prototype of the game, examining its usability as well as users’ game experience to create a basis for an effective game in the future. The findings based on a sample of N = 31 pre-service teachers from a Southwestern German university indicate great usability and a good feedback system, high perceived knowledge improvement, and pleasant visual aesthetics. Potential for optimization was revealed for the scope and the level progression of the game.

Context:

In recent years, there has been a significant surge in global Social Engineering (phishing) attacks. This upsurge has prompted governmental bodies, organizations, and educational institutions to formulate strategies aimed at mitigating this threat.

Objective:

The primary objective of this research is to create a game-based solution that educates participants about URLs and evaluates their understanding through multiple-choice questions.

Methodology:

To attain the aforementioned objectives, a multifaceted approach has been adopted in this study. Firstly, an extensive literature review was conducted to gain insights into the problem and prior research on the subject. This review was instrumental in comprehending the game development framework, developmental tools, and various design models for game design. A digital adaptation of the game has been created utilizing the CONSTRUCT 3 platform. Secondly, an empirical evaluation was executed, involving participants engaging with the game and their learning assessed through survey. A survey method was employed to further gauge participants’ knowledge and to solicit feedback on the game’s design.

Results and Conclusion:

The survey results indicate a lack of significant outcomes or dependencies on the dependent variable. fun to play, ease to play, and game-based learning did not significantly predict avoidance behavior while the intention to play and phishing knowledge were the significant positive predictors of avoidance behavior, with intention to play showing the biggest contribution in the models 2 and 3. Correspondingly, the negligible difference between the R2 value and $△$ R2 in models 2 and 3 also confirmed the small variance of model 2 (explained in the paper). Consequently, the research asserts that the assessment of the gaming method has not yielded success and underscores the necessity for enhancements and further evaluation.

Game engines are tools to facilitate video game development. They provide graphics, sound, and physics simulation features, which would have to be otherwise implemented by developers. Even though essential for modern commercial video game development, game engines are complex and developers often struggle to understand their architecture, leading to maintainability and evolution issues that negatively affect video game productions. In this paper, we present the Subsystem-Dependency Recovery Approach (SyDRA), which helps game engine developers understand game engine architecture and therefore make informed game engine development choices. By applying this approach to 10 open-source game engines, we obtain architectural models that can be used to compare game engine architectures and identify and solve issues of excessive coupling and folder nesting. Through a controlled experiment, we show that the inspection of the architectural models derived from SyDRA enables developers to complete tasks related to architectural understanding and impact analysis in less time and with higher correctness than without these models.

Theme parks should provide attractive green design and digital interactive experiences to attract tourists and improve their satisfaction. Therefore, this study aims to achieve a digital interactive experience in theme park greening design through the use of entertainment robots and genetic algorithm optimization. The study analyzed the characteristics of experiential theme parks and introduced landscape 3D modeling techniques for creating interactive 3D models of green spaces in theme parks. By using technologies such as sensors and cameras, entertainment robots can perceive the presence and behavior of tourists. Entertainment robots can interact with tourists in real-time, customize them according to their interests and needs, and provide unique and enjoyable experiences for tourists. By collecting and analyzing feedback and behavioral data from tourists, the advantages and improvement points of digital interactive experiences can be identified. Through comparative experimental analysis with traditional experience methods, it was found that digital interactive experience can significantly improve tourist participation and satisfaction. Through user data analysis of interactive experience, this design can effectively improve the greening design of theme parks, enhance tourist participation and entertainment experience.

The application of digital media in fitness environments can improve the entertainment of the fitness process, allowing users to watch entertainment videos while engaging in fitness activities. At the same time, we can add relevant game elements to the fitness process, such as playing tennis through gesture movements, thereby increasing user interest in fitness participation. Therefore, this article has developed a personalized fitness training system using optical sensors and intelligent algorithms, and conducted testing experiments on blood oxygen saturation under two sports states: running and cycling. This article analyzes the intelligent optimization algorithm for optical signals. This algorithm has strong adaptability and intelligence level, and can directly learn and adjust the model based on the characteristics of the data. The automation performance of the intelligent algorithm can effectively reduce labor costs and improve efficiency. Finally, this article provides a basic analysis and testing of the personalized fitness training system. Through the obtained data, personalized guidance is provided to users, helping people make fitness more scientific and intelligent, and improving fitness efficiency.

In recent years, due to the increasing number of basketball games and training activities, basketball players often face the problem of fatigue and injury. This study aims to develop an interactive digital entertainment and gamification training method to provide basketball players with more efficient training methods and enhance their training fun. Sensors can be installed in various key parts of the athlete’s body, through the use of high-precision sensor equipment, real-time acquisition of athlete action data, the use of machine learning and data mining technology, real-time analysis and modeling of sensor data to extract key information and movement patterns. Using gamified training theory to design interactive digital entertainment system, develop a series of training scenarios and gamified tasks for different technical elements, combine sensor data and motion analysis results, and provide athletes with personalized training plans and feedback mechanisms to help them improve their technical level. Through an interactive digital entertainment system, basketball players can train and compete in a virtual environment. They can complete various training tasks by interacting with the system, which will give real-time evaluation and guidance based on the athlete’s performance to help them correct mistakes, improve technique and improve training results.

This study aims to explore the application of sports data analysis of recreational fitness robots in sports training. By introducing interactive elements of games, recreational fitness robots can provide more interesting and challenging fitness experience, thus attracting more people to participate in sports training. Research and design and develop a game interactive entertainment fitness robot, with action recognition and user interaction functions, can perceive the user’s actions and make corresponding responses. When the user interacts with the robot, the sensor continuously collects and records data, establishes a data acquisition and storage system, analyzes and processes the collected interactive data, identifies the user’s movement pattern, assesses the user’s physical fitness level, and identifies potential improvement points. According to the user’s physical condition and training objectives, the appropriate exercise plan and game interaction mode are designed to provide better training results and entertainment experience. It is found that the game-based interactive entertainment fitness robot can effectively improve the user’s exercise motivation and participation in sports training. Through the analysis of sports data, we can develop personalized training plans according to the specific needs and goals of users, so as to achieve better training results.

At present, the mental health problems of college students are becoming more and more prominent, but the existing mental health detection methods have certain limitations. Therefore, the combination of entertainment and game experience with artificial intelligence technology in this paper can provide a more comprehensive and accurate mental health detection scheme. The research designed an immersive entertainment game experience platform, in which artificial intelligence technology was used to detect mental health. The platform is based on emotion recognition algorithms and artificial intelligence interaction technology to assess the level of mental health of users by analyzing their behavior and emotional expression in the game. The results show that immersive artificial intelligence technology based on entertainment and game experience can effectively simulate the mental health state of college students. Compared with traditional mental health assessment methods, this technique has obvious advantages in terms of accuracy and comprehensiveness. Users also showed a high degree of acceptance and participation for this entertaining mental health detection method. This technology can provide students with a more relaxed and interesting mental health assessment experience, and can also provide accurate and comprehensive assessment results, and provide scientific and effective guidance for mental health management and intervention of college students.

The success of baby monitoring systems is heavily dependent on their design. Well-designed systems can enhance user experience, improve accuracy, and increase trust in the technology. As a collective virtual shared space, the Metaverse technology has the potential to benefit both designers and companies involved in designing baby monitoring systems. The usefulness of the Metaverse is widely investigated in different domains. However, the adoption of the Metaverse in the design of baby monitoring systems has rarely been explored in the previous research. In addition, the integration of the Metaverse in intelligent baby monitoring systems’ design poses significant privacy challenges that need to be addressed. This study, therefore, explores the factors that impact the adoption of the Metaverse in the design of baby monitoring systems. A model is developed and tested using the Stimulus-Organism-Response (SOR) theory. The findings of the study reveal that real-time monitoring, perceived security, ease of use, and personalization positively influence the technology trust in baby monitoring systems’ design. Besides, perceived privacy positively influences the technology trust and Metaverse adoption in baby monitoring systems. On the other hand, safety concerns do not impact the technology trust or the Metaverse adoption in baby monitoring systems’ design.

Teaching games are an effective teaching organization activity. In response to the evaluation and prediction problem of teaching games, a teaching game evaluation model based on improved sparrow search algorithm and back propagation neural network was studied and constructed. Firstly, a situational teaching game was designed and an evaluation index system was constructed. Then, a teaching game evaluation prediction model based on the improved method was established. Finally, the expert consultation method is adopted to collect opinions from experts in the field of education and construct an evaluation index system for teaching games. And based on the evaluation index system of teaching games, evaluate students’ mathematical thinking ability before and after experiencing teaching games to verify the application effect of teaching games. The scenario based teaching game designed in this study has a certain effect on improving students’ mathematical thinking ability. Students’ mathematical thinking has significantly improved (P<0.05), and the teaching effect is the same for students of different genders (P>0.1). The improved sparrow search algorithm has a faster convergence rate than other algorithms, and tends to be stable when iteration is about 100 when solving the single peak benchmark function. When solving the multimodal benchmark test function, it tends to stabilize when iteration is around 20. The teaching game evaluation prediction price model based on the improved method shows a trend of first increasing and then decreasing with hidden units increasing. When the hidden unit is 16, the area index under model curve is the highest, around 0.962, and its prediction accuracy is relatively high. In summary, the model constructed in this study is applicating good in teaching game evaluation prediction, and can promote education industry developing.