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The increasingly widespread use of large-scale 3D virtual environments has translated into an increasing effort required from designers, developers and testers. While considerable research has been conducted into assisting the design of virtual world content and mechanics, to date, only limited contributions have been made regarding the automatic testing of the underpinning graphics software and hardware. In the work presented in this paper, two novel neural network-based approaches are presented to predict the correct visualization of 3D content. Multilayer perceptrons and self-organizing maps are trained to learn the normal geometric and color appearance of objects from validated frames and then used to detect novel or anomalous renderings in new images. Our approach is general, for the appearance of the object is learned rather than explicitly represented. Experiments were conducted on a game engine to determine the applicability and effectiveness of our algorithms. The results show that the neural network technology can be effectively used to address the problem of automatic and reliable visual testing of 3D virtual environments.

This book discusses two questions in Complexity Theory: the Monotonicity Testing problem and the 2-to-2 Games Conjecture. Monotonicity testing is a problem from the field of property testing, first considered by Goldreich et al. in 2000. The input of the algorithm is a function, and the goal is to design a tester that makes as few queries to the function as possible, accepts monotone functions and rejects far-from monotone functions with a probability close to 1. The first result of this book is an essentially optimal algorithm for this problem. The analysis of the algorithm heavily relies on a novel, directed, and robust analogue of a Boolean isoperimetric inequality of Talagrand from 1993. The probabilistically checkable proofs (PCP) theorem is one of the cornerstones of modern theoretical computer science. One area in which PCPs are essential is the area of hardness of approximation. Therein, the goal is to prove that some optimization problems are hard to solve, even approximately. Many hardness of approximation results were proved using the PCP theorem; however, for some problems optimal results were not obtained. This book touches on some of these problems, and in particular the 2-to-2 games problem and the vertex cover problem. The second result of this book is a proof of the 2-to-2 games conjecture (with imperfect completeness), which implies new hardness of approximation results for problems such as vertex cover and independent set. It also serves as strong evidence towards the unique games conjecture, a notorious related open problem in theoretical computer science. At the core of the proof is a characterization of small sets of vertices in Grassmann graphs whose edge expansion is bounded away from 1.

In this meta-analysis, we systematically reviewed research on digital games and learning for K–16 students. We synthesized comparisons of game versus nongame conditions (i.e., media comparisons) and comparisons of augmented games versus standard game designs (i.e., value-added comparisons). We used random-effects meta-regression models with robust variance estimates to summarize overall effects and explore potential moderator effects. Results from media comparisons indicated that digital games significantly enhanced student learning relative to nongame conditions (ḡ = 0.33, 95% confidence interval [0.19, 0.48], k = 57, n = 209). Results from value-added comparisons indicated significant learning benefits associated with augmented game designs (ḡ = 0.34, 95% confidence interval [0.17, 0.51], k = 20, n = 40). Moderator analyses demonstrated that effects varied across various game mechanics characteristics, visual and narrative characteristics, and research quality characteristics. Taken together, the results highlight the affordances of games for learning as well as the key role of design beyond medium.

Purpose The purpose of this paper is to investigate the mechanism by which uses and gratification (U&G) constructs predict continuance intention to play (ContInt) the augmented reality game Pokémon Go (PG), through multiple serial mediation technique, with enjoyment and flow as mediators. The model also integrates other motivational factors specific to PG, namely, network externality and nostalgia and investigates the process by which they influence ContInt through players’ inherent need-to-collect animated monsters and online community involvement, respectively. Design/methodology/approach The model was tested using 362 validated responses from an online survey of PG players in Malaysia. Partial least squares structural equation modeling was used to analyse the data. The predictive relevance of the model was tested via partial least squares-Predict. Findings ContInt is influenced through various mechanisms. Enjoyment is the most important mediator, mediating three U&G predictor constructs (achievement, escapism, challenge and social interaction) and the outcome ContInt. Flow did not have any influence on ContInt unless coupled with enjoyment as a serial mediator. Network externality and nostalgia were found to only influence ContInt through mediators, online community involvement and need-to-collect Pokémon Monsters, respectively. Overall, the results show evidence of four indirect-only mediation paths and one complementary partial mediation path. Originality/value Provides support for an integrated model incorporating psychological, social and gaming motivational factors. While most other studies focus on direct relationships, we focus on indirect relationships through multiple sequential mediation analysis, following the recent modern mediation analysis guidelines. Contrary to previous findings, flow was not an important factor in predicting ContInt for gaming and nostalgia does not link directly to ContInt.

Software testing activities scrutinize the artifacts and the behavior of a software product to find possible defects and ensure that the product meets its expected requirements. Although various approaches of software testing have shown to be very promising in revealing defects in software, some of them lack automation or are partly automated which increases the testing time, the manpower needed, and overall software testing costs. Recently, Deep Reinforcement Learning (DRL) has been successfully employed in complex testing tasks such as game testing, regression testing, and test case prioritization to automate the process and provide continuous adaptation. Practitioners can employ DRL by implementing from scratch a DRL algorithm or using a DRL framework. DRL frameworks offer well-maintained implemented state-of-the-art DRL algorithms to facilitate and speed up the development of DRL applications. Developers have widely used these frameworks to solve problems in various domains including software testing. However, to the best of our knowledge, there is no study that empirically evaluates the effectiveness and performance of implemented algorithms in DRL frameworks. Moreover, some guidelines are lacking from the literature that would help practitioners choose one DRL framework over another. In this paper, therefore, we empirically investigate the applications of carefully selected DRL algorithms (based on the characteristics of algorithms and environments) on two important software testing tasks: test case prioritization in the context of Continuous Integration (CI) and game testing. For the game testing task, we conduct experiments on a simple game and use DRL algorithms to explore the game to detect bugs. Results show that some of the selected DRL frameworks such as Tensorforce outperform recent approaches in the literature. To prioritize test cases, we run extensive experiments on a CI environment where DRL algorithms from different frameworks are used to rank the test cases. We find some cases where our DRL configurations outperform the implementation of the baseline. Our results show that the performance difference between implemented algorithms in some cases is considerable, motivating further investigation. Moreover, empirical evaluations on some benchmark problems are recommended for researchers looking to select DRL frameworks, to make sure that DRL algorithms perform as intended.

Recognition of human actions from visual contents is a budding field of computer vision and image understanding. The problem with such a recognition system is the huge dimensions of the feature vectors. Many of these features are irrelevant to the classification mechanism. For this reason, in this paper, we propose a novel feature selection (FS) model called cooperative genetic algorithm (CGA) to select some of the most important and discriminating features from the entire feature set to improve the classification accuracy as well as the time requirement of the activity recognition mechanism. In CGA, we have made an effort to embed the concepts of cooperative game theory in GA to create a both-way reinforcement mechanism to improve the solution of the FS model. The proposed FS model is tested on four benchmark video datasets named Weizmann, KTH, UCF11, HMDB51, and two sensor-based UCI HAR datasets. The experiments are conducted using four state-of-the-art feature descriptors, namely HOG, GLCM, SURF, and GIST. It is found that there is a significant improvement in the overall classification accuracy while considering very small fraction of the original feature vector.

Online educational games have been widely used to support students’ mathematics learning. However, their effects largely depend on student-related factors, the most prominent being their behavioral characteristics as they play the games. In this study, we applied a set of learning analytics methods (k-means clustering, data visualization) to clickstream data from an interactive online algebra game to unpack how middle-school students’ (N = 227) behavioral patterns (i.e., the number of problems completed, resetting problems, reattempting problems, pause time before first actions) correlated with their understanding of mathematical equivalence. The k-means cluster analysis identified four groups of students based on their behavioral patterns in the game: fast progressors, intermediate progressors, slow progressors, and slow-steady progressors. The results indicated that students in these clusters, with the exception of slow progressors, showed significant increases in their understanding of mathematical equivalence. In particular, slow-steady progressors, who reattempted the same problem more often than other students, showed the largest absolute learning gains, suggesting that behavioral engagement played a significant role in learning. With data visualizations, we presented evidence of variability in students’ approaches to problem solving in the game, providing future directions for investigating how differences in student behaviors impact learning.

Because of its benefits in providing an engaging and mobile environment, virtual reality (VR) has recently been rapidly adopted and integrated in education and professional training. Augmented reality (AR) is the integration of VR with the real world, where the real world provides context and the virtual world provides or reconstructs missing information. Mixed reality (MR) is the blending of virtual and physical reality environments allowing users to interact with both digital and physical objects at the same time. In recent years, technology for creating reality-based 3D models has advanced and spread across a diverse range of applications and research fields. The purpose of this paper is to design, develop, and test VR for kinaesthetic distance learning in a museum setting. A VR training program has been developed in which learners can select and perform pre-made scenarios in a virtual environment. The interaction in the program is based on kinaesthetic learning characteristics. Scenarios with VR controls simulate physical interaction with objects in a virtual environment for learners. Learners can grasp and lift objects to complete scenario tasks. There are also simulated devices in the virtual environment that learners can use to perform various actions. The study’s goal was to compare the effectiveness of the developed VR educational program to that of other types of educational material. Our innovation is the development of a system for combining their 3D visuals with rendering capable of providing a mobile VR experience for effective heritage enhancement.

Many studies have demonstrated that vocabulary size plays a key role in learning English as a foreign language (EFL). In recent years, mobile game-based learning (MGBL) has been considered a promising scheme for successful acquisition and retention of knowledge. Thus, this study applies a mixed methodology that combines quantitative and qualitative approaches to assess the effects of PHONE Words, a novel mobile English vocabulary learning app (application) designed with game-related functions (MEVLA-GF) and without game-related functions (MEVLA-NGF), on learners’ perceptions and learning performance. During a four-week experiment, 20 sophomore students were randomly assigned to the experimental group with MEVLA-GF support or the control group with MEVLA-NGF support for English vocabulary learning. Analytical results show that performance in vocabulary acquisition and retention by the experimental group was significantly higher than that of the control group. Moreover, questionnaire results confirm that MEVLA-GF is more effective and satisfying for English vocabulary learning than MEVLA-NGF. Spearman rank correlation results show that involvement and dependence on gamified functions were positively correlated with vocabulary learning performance.