Active video game head movement inputs

Virtual reality has been extensively studied for applications in rehabilitation. With the development of active video games, these commercial products can also be considered for inclusion in a patient’s rehabilitation program. In this study, the Sony EyeToy ^® and PlayStation 2 ^® were used with the AntiGrav? game to evaluate the user’s head movement actions. The game required lateral head, body, and arm movements. Over the course of 9 sessions of game play, average and maximum head excursions remained constant. However, the frequency of head movement increased over the sessions. The results suggest that the video game could be used for postural balance rehabilitation through head movements, and their effect on the vestibular system. Future work will evaluate how such vestibular exercise, and an increase in head movement frequency over training sessions can support postural balance improvements.     

Serious games and learning effectiveness: The case of It’s a Deal!

Although the value of serious games in education is undeniable and the potential benefits of using video games as ideal companions to classroom instruction is unquestionable, there is still little consensus on the game features supporting learning effectiveness, the process by which games engage learners, and the types of learning outcomes that can be achieved through game play. Our aim in this discussion is precisely to advance in this direction by providing evidence of some of the factors influencing the learning effectiveness of a serious game called It’s a Deal! This serious game was created for the purpose of teaching intercultural business communication between Spaniards and Britons in business settings in which English is used as the lingua franca. This paper hypothesizes that the immersive, all-embracing and interactive learning environment provided by the video game to its users may contribute to develop and enhance their intercultural communicative competence. The study attempts to answer three main research questions: (a) after playing It’s a Deal!, did the students sampled improve their intercultural awareness, intercultural knowledge and intercultural communicative competence in business English? (b) If they improved their intercultural learning, what are the factors influencing such improvement? And (c) if they did not improve their intercultural learning, what are the factors influencing such failure? The game participants who volunteered to take part in the study were all students of English Studies at the University of Alicante in the academic year 2010-2011. One hundred and six students completed both the pre-test and the post-test questionnaires, and played It’s a Deal! A sample of fifty students was selected randomly for the empirical study. The results obtained in the tests performed were compared and contrasted intra-group, both qualitatively and quantitatively, for the purpose of finding any statistically significant difference that may confirm whether or not there was an improvement in the students’ intercultural communicative competence in business English as a result of the implementation of the It’s a Deal! serious game. Findings of this study demonstrate that the video game is an effective learning tool for the teaching of intercultural communication between Spaniards and Britons in business settings in which English is used as the lingua franca. In particular, whereas the game had a small learning effect on intercultural awareness and a medium learning effect on intercultural knowledge, it had a large learning effect on intercultural communicative competence. The study also documents correlating factors that make serious games effective, since it shows that the learning effectiveness of It’s a Deal! stems from the correct balance of the different dimensions involved in the creation of serious games, specifically instructional content, game dimensions, game cycle, debriefing, perceived educational value, transfer of learnt skills and intrinsic motivation.     

Acceptability of a custom-designed game,CityQuest, aimed at improving balance confidence and spatial cognition in fall-prone and healthy older adults

Virtual reality or video games show great potential as low-cost and effective interventions for improving balance and cognitive function in older adults. This research describes the design and acceptability of a serious game (CityQuest) aimed at improving balance confidence, spatial navigation, and perceptual function in older adults with the use of a virtual environment and a balance board. Community-dwelling healthy (N?=?28) and fall-prone (N?=?28) older adults were pseudo-randomly assigned to train with CityQuest or one of two control games developed to evaluate the specific effects of the CityQuest game. Following completion of 10 training sessions, participants completed questionnaires measuring their acceptability of the game as a falls-related intervention, game experience, and subjective cognitive or balance confidence changes associated with the game. The results revealed high acceptance scores of the game and positive game experiences for all three game conditions. Older adults prone to falls reported a greater reduction in fear of falling and greater improvement in vigilance following training, compared to healthy older adults. These findings suggest that a serious game based on VR technology that trains both motor and cognitive processes is perceived to be beneficial and acceptable to healthy and fall-prone older adults.     

Children’s choices and strategies in video games

It is important to develop an understanding of children’s engagement and choices in learning experiences outside of school as this has implications for their development and orientations to other learning environments. This mixed-methods study examines relationships between the genres of video games children choose to play and the learning strategies they employ to improve at these games. It also explores students’ motivations for playing the games they choose to play. One hundred eighteen fourth- and fifth-grade students participated in this study. Qualitative analyses of student responses resulted in a model for classifying motivation for game choices. Children primarily cite reasons that can be classified as psychological or cognitive reasons for choosing to play certain video games, and are motivated by the challenge and thinking required in the games. Analyses using Chi-square tests of association demonstrated significant relationships between video game genre and learning strategy used for two of the six learning strategies (p < .05). Children playing action games are more likely to use repetition to learn the game and children playing adventure games are more likely to use their imaginations to take on the role of the character in the game and think the way the character would to make decisions in the game. There were also several gender differences in learning preferences.     

A case study of the in-class use of a video game for teaching high school history

This study examines the case of a sophomore high school history class where Making History, a video game designed with educational purposes in mind, is used in the classroom to teach about World War II. Data was gathered using observation, focus group and individual interviews, and document analysis. The high school was a rural school located in a small town in the Midwestern United States. The teacher had been teaching with the game for several years and spent one school week teaching World War II, with students playing the game in class for three days of that week. The purpose of this study was to understand teacher and student experiences with and perspectives on the in-class use of an educational video game. Results showed that the use of the video game resulted in a shift from a traditional teacher-centered learning environment to a student-centered environment where the students were much more active and engaged. Also, the teacher had evolved implementation strategies based on his past experiences using the game to maximize the focus on learning.     

Generative adversarial network based novelty detection usingminimized reconstruction error

Generative adversarial network (GAN) is the most exciting machine learning breakthrough in recent years, and it trains the learning model by finding the Nash equilibrium of a two-player zero-sum game. GAN is composed of a generator and a discriminator, both trained with the adversarial learning mechanism. In this paper, we introduce and investigate the use of GAN for novelty detection. In training, GAN learns from ordinary data. Then, using previously unknown data, the generator and the discriminator with the designed decision boundaries can both be used to separate novel patterns from ordinary patterns. The proposed GAN-based novelty detection method demonstrates a competitive performance on the MNIST digit database and the Tennessee Eastman (TE) benchmark process compared with the PCA-based novelty detection methods using Hotelling’s T² and squared prediction error statistics.     

A personalized e-course composition based on a genetic algorithm with forcing legality in an adaptive learning system

This paper proposes a personalized e-course composition based on a genetic algorithm with forcing legality (called GA^?) in adaptive learning systems, which efficiently and accurately finds appropriate e-learning materials in the database for individual learners. The forcing legality operation not only reduces the search space size and increases search efficiency but also is more explicit in finding the best e-course composition in a legal solution space. In serial experiments, the forcing legality operation is applied in Chu et al.'s the particle swarm optimization (called PSO^?) and Dheeban et al.'s the improved particle swarm optimization (called RPSO^?) to show the forcing legality can speed up the computational time and reduce the computational complexity of algorithm. Furthermore, GA^? regardless of the number of students or the number of materials in the database, to compose a personalized e-course within a limited time is much more efficient and accurate than PSO^? and RPSO^?. For the experiment increasing the number of students to 1200, the average improvement ratios of errors (learning concept error, materials difficulty error, learning time error), fitness value, stability, and execution time are above 96%, 79%, 90%, and 10%, respectively. For the experiment increasing the number of materials to 500 and the execution time set to the shortest execution time of RPSO^?, the average improvement ratios of errors (learning concept error, materials difficulty error, learning time error), fitness value, and stability are above 97%, 51%, and 80%, respectively. Therefore, GA^? is able to enhance the quality of personalized e-course compositions in adaptive learning environments.     

Ergonomic factors on task performance in laparoscopic surgery training

This paper evaluates the effect of ergonomic factors on task performance and trainee posture during laparoscopic surgery training. Twenty subjects without laparoscopic experience were allotted into 2 groups. Group 1 was trained under the optimal ergonomic simulation setting according to current ergonomic guidelines (Condition A). Group 2 was trained under non-optimal ergonomic simulation setting that can often be observed during training in a skills lab (Condition B). Posture analysis showed that the subjects held a much more neutral posture under Condition A than under Condition B (p < 0.001). The subjects had less joint excursion and experienced less discomfort in their neck, shoulders, and arms under Condition A. Significant difference in task performance between Conditions A and B (p < 0.05) was found. This study shows that the optimal ergonomic simulation setting leads to better task performance. In addition, no significant differences of task performance, for Groups 1 and 2 using the same test setting were found. However, better performance was observed for Group 1. It can be concluded that the optimal and non-optimal training setting have different learning effects on trainees’ skill learning.     

When simulated environments make the difference: the effectiveness of different types of training of car service procedures

An empirical analysis was performed to compare the effectiveness of different approaches to training a set of procedural skills to a sample of novice trainees. Sixty-five participants were randomly assigned to one of the following three training groups: (1) learning-by-doing in a 3D desktop virtual environment, (2) learning-by-observing a video (show-and-tell) explanation of the procedures, and (3) trial-and-error. In each group, participants were trained on two car service procedures. Participants were recalled to perform a procedure either 2 or 4 weeks after the training. The results showed that: (1) participants trained through the virtual approach of learning-by-doing performed both procedures significantly better (i.e. p < .05 in terms of errors and time) than people of non-virtual groups, (2) the virtual training group, after a period of non-use, were more effective than non-virtual training (i.e. p < .05) in their ability to recover their skills, (3) after a (simulated) long period from the training—i.e. up to 12 weeks—people who experienced 3D environments consistently performed better than people who received other kinds of training. The results also suggested that independently from the training group, trainees’ visuospatial abilities were a predictor of performance, at least for the complex service procedure, adj R ² = .460, and that post-training performances of people trained through virtual learning-by-doing are not affected by learning styles. Finally, a strong relationship (p < .001, R ² = .441) was identified between usability and trust in the use of the virtual training tool—i.e. the more the system was perceived as usable, the more it was perceived as trustable to acquire the competences.     

Implicit and explicit training in the mitigation of cognitive bias through the use of a serious game

Heuristics can interfere with information processing and hinder decision-making when more systematic processes that might lead to better decisions are ignored. Based on the heuristic-systematic model (HSM) of information processing, a serious training game (called MACBETH) was designed to address and mitigate cognitive biases that interfere with the analysis of evidence and the generation of hypotheses. Two biases are the focus of this paper—fundamental attribution error and confirmation bias. The efficacy of the serious game on knowledge and mitigation of biases was examined using an experiment in which participants (N = 703) either played the MACBETH game or watched an instructional video about the biases. Results demonstrate the game to be more effective than the video at mitigating cognitive biases when explicit training methods are combined with repetitive play. Moreover, explicit instruction within the game provided greater familiarity and knowledge of the biases relative to implicit instruction. Suggestions for game development for purposes of enhancing cognitive processing and bias mitigation based on the MACBETH game design are discussed.     

Learning rules of a card game from video

This paper presents a framework for automatically learning rules of a simple game of cards using data from a vision system observing the game being played. Incremental learning of object and protocol models from video, for use by an artificial cognitive agent, is presented. iLearn??a novel algorithm for inducing univariate decision trees for symbolic datasets is introduced. iLearn builds the decision tree in an incremental way allowing automatic learning of rules of the game.     

Bounded budget betweenness centrality game for strategic network formations

In computer networks and social networks, the betweenness centrality of a node measures the amount of information passing through the node when all pairs are conducting shortest path exchanges. In this paper, we introduce a strategic network formation game in which nodes build connections subject to a budget constraint in order to maximize their betweenness in the network. To reflect real world scenarios where short paths are more important in information exchange in the network, we generalize the betweenness definition to only count shortest paths with a length limit ? in betweenness calculation. We refer to this game as the bounded budget betweenness centrality game and denote it as ?- B³C game, where ? is the path length constraint parameter.We present both complexity and constructive existence results about Nash equilibria of the game. For the nonuniform version of the game where node budgets, link costs, and pairwise communication weights may vary, we show that Nash equilibria may not exist and it is NP-hard to decide whether Nash equilibria exist in a game instance. For the uniform version of the game where link costs and pairwise communication weights are one and each node can build k links, we construct two families of Nash equilibria based on shift graphs, and study the properties of Nash equilibria. Moreover, we study the complexity of computing best responses and show that the task is polynomial for uniform 2- B³C games and NP-hard for other games (i.e. uniform ?- B³C games with ?≥3 and nonuniform ?- B³C games with ?≥2).     

Assessing implicit science learning in digital games

Building on the promise shown in game-based learning research, this paper explores methods for Game-Based Learning Assessments (GBLA) using a variety of educational data mining techniques (EDM). GBLA research examines patterns of behaviors evident in game data logs for the measurement of implicit learning—the development of unarticulated knowledge that is not yet expressible on a test or formal assessment. This paper reports on the study of two digital games showing how the combination of human coding with EDM has enabled researchers to measure implicit learning of Physics. In the game Impulse, researchers combined human coding of video with educational data mining to create a set of automated detectors of students' implicit understanding of Newtonian mechanics. For Quantum Spectre, an optics puzzle game, human coding of Interaction Networks was used to identify common student errors. Findings show that several of our measures of student implicit learning within these games were significantly correlated with improvements in external postassessments. Methods and detailed findings were different for each type of game. These results suggest GBLA shows promise for future work such as adaptive games and in-class, data-driven formative assessments, but design of the assessment mechanics must be carefully crafted for each game.     

Learning automatic concept detectors from online video

Concept detection is targeted at automatically labeling video content with semantic concepts appearing in it, like objects, locations, or activities. While concept detectors have become key components in many research prototypes for content-based video retrieval, their practical use is limited by the need for large-scale annotated training sets. To overcome this problem, we propose to train concept detectors on material downloaded from web-based video sharing portals like YouTube, such that training is based on tags given by users during upload, no manual annotation is required, and concept detection can scale up to thousands of concepts. On the downside, web video as training material is a complex domain, and the tags associated with it are weak and unreliable. Consequently, performance loss is to be expected when replacing high-quality state-of-the-art training sets with web video content.This paper presents a concept detection prototype named TubeTagger that utilizes YouTube content for an autonomous training. In quantitative experiments, we compare the performance when training on web video and on standard datasets from the literature. It is demonstrated that concept detection in web video is feasible, and that – when testing on YouTube videos – the YouTube-based detector outperforms the ones trained on standard training sets. By applying the YouTube-based prototype to datasets from the literature, we further demonstrate that: (1) If training annotations on the target domain are available, the resulting detectors significantly outperform the YouTube-based tagger. (2) If no annotations are available, the YouTube-based detector achieves comparable performance to the ones trained on standard datasets (moderate relative performance losses of 11.4% is measured) while offering the advantage of a fully automatic, scalable learning. (3) By enriching conventional training sets with online video material, performance improvements of 11.7% can be achieved when generalizing to domains unseen in training.     

Video game training to improve selective visual attention in older adults

The current study investigated the effect of video game training on older adult’s useful field of view performance (the UFOV® test). Fifty-eight older adult participants were randomized to receive practice with the target action game (Medal of Honor), a placebo control arcade game (Tetris), a clinically validated UFOV training program, or into a no contact control group. Examining pretest–posttest change in selective visual attention, the UFOV improved significantly more than the game groups; all three intervention groups improved significantly more than no-contact controls. There was a lack of difference between the two game conditions, differing from findings with younger adults. Discussion considers whether games posing less challenge might still be effective interventions for elders, and whether optimal training dosages should be higher.     

Fast Algorithms for max independent set

We first propose a method, called “bottom-up method” that, informally, “propagates” improvement of the worst-case complexity for “sparse” instances to “denser” ones and we show an easy though non-trivial application of it to the min set cover problem. We then tackle max independent set. Here, we propagate improvements of worst-case complexity from graphs of average degree?d to graphs of average degree greater than?d. Indeed, using algorithms for max independent set in graphs of average degree 3, we successively solve max independent set in graphs of average degree 4, 5 and?6. Then, we combine the bottom-up technique with measure and conquer techniques to get improved running times for graphs of maximum degree?5 and?6 but also for general graphs. The computation bounds obtained for max independent set are?O ^?(1.1571 ⁿ ), O ^?(1.1895 ⁿ ) and?O ^?(1.2050 ⁿ ), for graphs of maximum (or more generally average) degree?4, 5 and?6 respectively, and?O ^?(1.2114 ⁿ ) for general graphs. These results improve upon the best known results for these cases for polynomial space algorithms.     

Investigating the impact of video games on high school students’ engagement and learning about genetics

The popularity of video games has transcended entertainment crossing into the world of education. While the literature base on educational gaming is growing, there is still a lack of systematic study of this emerging technology’s efficacy. This quasi-experimental study evaluated a teacher created video game on genetics in terms of its affective and cognitive impact on student users. While statistical results indicated no differences (p > .05) in student learning as measured by our instrument, there were significant differences (p < .05) found in the participants’ level of engagement while interfacing with the video game. Implications on this emerging line of inquiry are discussed.     

Improving surgical pattern recognition through repetitive viewing of video clips

Previous field studies show that surgery residents and medical students have difficulty recognizing appropriate anatomic cues during laparoscopic surgery, causing delays in procedures and errors. Such observations led to the development of an anatomy recognition training intervention, specifically the use of an ordered set of video clips that show the main steps of a laparoscopic procedure. Each procedural step is shown several times in succession, with each repetition coming from a different surgery, thus exposing the learner to varied anatomy and crucial maneuvers. A factorial experiment with 30 medical students showed that the group using these ordered, perceptual learning modules had a significant increase in scores on questions assessing perceptual knowledge and procedural knowledge, with no corresponding increase for the control group who watched the videos from the same cases but in an unstructured format for the same amount of time (p< 0.05). Neither group showed improvement on strategic or declarative knowledge tests. The study suggests that ordered perceptual learning modules are a potential means for training perceptual and procedural knowledge in an effective, safe, and efficient manner, serving as a complement to other types of training methods that teach physical dexterity, strategic and declarative knowledge.     

Video-games: Do they require general intelligence?

Here we test if playing video-games require intelligence. Twenty-seven university undergraduate students were trained on three games from Big Brain Academy (Wii): Calculus, Backward Memory and Train. Participants did not have any previous experience with these games. General intelligence was measured by five ability tests before the training session. Training comprised 10 blocks of trials (10 trials per block). Ackerman’s (Ackerman, P. L. (1988). Individual differences and skill acquisition. In P. L. Ackerman, R. J. Sernberg, & R. Glaser (Eds.), Learning and individual differences: Advances in theory and practice (pp. 165–217). New York: W.H. Freeman and Company) theory of skill learning was used as a framework for the present study. Results show that playing the Train game increases the correlation with general intelligence across blocks of trials. This is not the case for Calculus and Backward Memory. These findings suggest strategies for designing video-games presumably appropriate to stimulate our core cognitive abilities.     

Bandwidth and pebbling

The main results of this paper establish relationships between the bandwidth of a graphG — which is the minimum over all layouts ofG in a line of the maximum distance between images of adjacent vertices ofG — and the ease of playing various pebble games onG. Three pebble games on graphs are considered: the well-known computational pebble game, the “progressive” (i.e., no recomputation allowed) version of the computational pebble game, both of which are played on directed acyclic graphs, and the quite different “breadth-first” pebble game, that is played on undirected graphs. We consider two costs of a play of a pebble game: the minimum number of pebbles needed to play the game on the graphG, and the maximumlifetime of any pebble in the game, i.e., the maximum number of moves that any pebble spends on the graph. The first set of results of the paper prove that the minimum lifetime cost of a play of either of the second two pebble games on a graphG is precisely the bandwidth ofG. The second set of results establish bounds on the pebble demand of all three pebble games in terms of the bandwidth of the graph being pebbled; for instance, the number of pebbles needed to pebble a graphG of bandwidthk is at most min (2k ²+k+1, 2k log₂|G|); and, in addition, there are bandwidth-k graphs that require 3k?1 pebbles. The third set of results relate the difficulty of deciding the cost of playing a pebble game on a given input graphG to the bandwidth ofG; for instance, the Pebble Demand problem forn-vertex graphs of bandwidthf(n) is in the class NSPACE (f(n) log² n); and the Optimal Lifetime Problem for either of the second two pebble games is NP-complete.