Search Results (14)

This study explores the effects of interacting with AI vs. human interlocutors on English language learners’ speaking performance in a game-based learning context. We developed Taboo Talks, a word-guessing game in which learners alternated between giving and guessing clues with either an AI or a human partner. To evaluate the impact of interaction mode on oral proficiency, participants completed a story retelling task, assessed using complexity, accuracy, and fluency (CAF) metrics. Each participant engaged in both partner conditions, with group order counterbalanced. The results from the retelling task indicated modest improvements in fluency and complexity, particularly following interaction with the AI partner. Accuracy scores remained largely stable across conditions. Post-task reflections revealed that learners perceived AI partners as less intimidating, facilitating more relaxed language production, though concerns were noted regarding limited responsiveness. Qualitative analysis of the gameplay transcripts further revealed contrasting interactional patterns: AI partners elicited more structured interactions whereas human partners prompted more spontaneous and variable interactions. These findings suggest that AI-mediated gameplay can enhance specific dimensions of spoken language development and may serve as a complementary resource alongside human interaction. Full article

The influence of gameplay on autonomic nervous system activity was investigated by comparing electrocardiogram (ECG) data during seated rest and gameplay. A total of 13 participants (6 in the gameplay group and 7 in the control group) were analyzed. RR interval time series (2 Hz) and heart-rate variability (HRV) indices, including mean RR, SDRR, VLF, LF, HF, LF/HF, and HF peak frequency, were extracted from ECG signals over 5 min and 10 min segments. HRV indices were calculated using fast Fourier transform (FFT). The classification was performed using Logistic Regression (LGR), Random Forest (RF), XGBoost (XGB, v2.9.2), One-Class SVM (OCS), Isolation Forest (ILF), and Local Outlier Factor (LOF). A balanced dataset of 5 min and 10 min segments was evaluated using k-fold cross-validation (k = 3, 4, 5). Performance metrics, including recall, F-score, and PR-AUC, were computed for each classifier. Grid search was applied to optimize parameters for LGR, RF, and XGB, while default settings were used for the other classifiers. Among all models, OCS with k = 3 achieved the highest classification accuracy for both 5 min and 10 min data. These findings suggest that machine learning-based classification can effectively distinguish ECG patterns between gameplay and rest. Full article

As an emerging and lively research field, game designers are employing Dynamic Difficulty Adjustment (DDA) in Game Artificial Intelligence (Game AI) to improve player experience. Traditional DDA methods focus little on player churn, which cannot always lead to enhanced player engagement. Hence, we propose the Engagement-oriented Dynamic Difficulty Adjustment (EDDA) to meet the urgent need for a highly general and customizable solution in the game industry. EDDA directly considers players’ churn trend to ensure player engagement during gameplay. Its real-time monitoring algorithm and common parameter set are effective in quantifying and preventing player churn. We developed a prototype system integrating seven major game genres to verify the difficulty, gameplay time, and scores of the Game Engagement Questionnaire (GEQ) in multiple dimensions. EDDA has the largest mean and median of all genres in the above metrics with the highest confidence level and effect size, which demonstrates its generality and availability in improving player experience. It is fair to say that EDDA not only provides game designers with targeted player churn monitoring and intervention means, but also offers a deeper level of thinking for the generalized application of DDA and other Game AI technologies. Full article

In research focusing on esports, studies have been conducted on designs that attract competitors, performance estimation, training methods, and motivational factors. However, quantitative and convenient methods for performance evaluation are still in the development stage among the numerous performance evaluation methods. In particular, few method has been developed to objectively measure an individual’s mental state utilizing limited equipment. It has been observed that when players’ performance deteriorates or they are under pressure, they occasionally operate the controller in accordance with their state, resulting in the sound of the controller increasing. Therefore, this study aimed to clarify the relationship between the sound of esports players’ controller operations and their objective as well as subjective metrics, including their emotional state and performance during the game. Initially, the controller sounds of players of various ranks in Super Smash Bros. Ultimate (SSBU) by Nintendo were explored, aiming to elucidate the connection between the operation sounds of adept and intermediate esports competitors and their day-to-day fluctuations in game performance and emotional well-being. The findings revealed a discernible pattern: the more proficient the player, the more resonant the sounds emanating from their controller during gameplay. Furthermore, the operational sounds of skilled players exhibited an escalation when their performance faltered. Full article

As virtual reality (VR) sports training apps start to become more mainstream, it is important that human performance is measured from VR gameplay interaction data in a more meaningful way. CleanSheet is a VR training app that is played by over 100,000 users around the world. Many of those players are aspiring goalkeepers who want to use the app as a new way to train and improve their general goalkeeping performance. Whilst the leaderboards display how many shots players saved, these data do not take into account the difficulty of the shot faced. This study presents a regression model developed from a combination of existing expected goals (xG) models, goalkeeper performance metrics, and psychological research to produce a new shot difficulty metric called CSxG. Utilizing user save rate data as the target variable, a model was developed that incorporated three input variables relating to ball flight and in-goal positioning. Our analysis showed that the required rate of closure (RROC), adapted from Tau theory, was the most significant predictor of the proportion of goals conceded. A validation process evaluated the new xG model for CleanSheet by comparing its difficulty predictions against user performance data across players of varying skill levels. CSxG effectively predicted shot difficulty at the extremes but showed less accuracy for mid-range scores (0.4 to 0.8). Additional variables influencing shot difficulty, such as build-up play and goalpost size, were identified for future model enhancements. This research contributes to the advancement of predictive modeling in sports performance analysis, highlighting the potential for improved goalkeeper training and strategy development using VR technology. Full article

A critical element of neurological function is eye–hand coordination: the ability of our vision system to coordinate the information received through the eyes to control, guide, and direct the hands to accomplish a task. Recent evidence shows that this ability can be disturbed by strokes or other neurological disorders, with critical consequences for motor behaviour. This paper presents a system based on serious games and multimodal devices aimed at improving the assessment of eye–hand coordination. The system implements gameplay that involves drawing specific patterns (labyrinths) to capture hand trajectories. The user can draw the path using multimodal devices such as a mouse, a stylus with a tablet, or robotic devices. Multimodal input devices can allow for the evaluation of complex coordinated movements of the upper limb that involve the synergistic motion of arm joints, depending on the device. A preliminary test of technological validation with healthy volunteers was conducted in the laboratory. The Dynamic Time Warping (DTW) index was used to compare hand trajectories without considering time-series lag. The results suggest that this multimodal framework allows for measuring differences between fine and gross motor skills. Moreover, the results support the viability of this system for developing a high-resolution metric for measuring eye–hand coordination in neurorehabilitation. Full article

This paper addresses the challenge of assessing relatedness and functional interdependence through connecting passes within invasion games, which may offer valuable pedagogical insights into gameplay for accessibility and inclusiveness. Hence, the purpose of this paper is twofold. Firstly, it presents preliminary work on the methodology for computing open passing lanes and derived metrics, integrating spatiotemporal data analysis with event data. Secondly, using a within-subject design, it investigates how modified handball games influence game play opportunities. Data were collected during handball matches in a pre-teens Physical Education (PE) class with mixed-skill-level teams. Game actions (e.g., passes, receptions, and shots) were manually recorded through systematic observation of video footage, while players’ positional data were captured with ultra-wideband technology. Findings provide evidence that employing a numerical advantage (one player up) enhances overall opportunities for individual attacking actions (i.e., more passing, catching actions, and goal-scoring opportunities) and relational actions (i.e., more open passing lanes) compared to equal numbers. Conversely, equal numbers with individual marking appeared more challenging, as fewer secure passing lanes were observed, and the ball possessor spent more time with the ball before releasing it. The developed approach holds promise for studying designed games to enhance inclusion and learning opportunities for all. Full article

Equity and inclusion are requisites of high-quality Physical Education (PE). However, there is a substantial gap in understanding PE Teacher Education’s (PETE) effectiveness in preparing Preservice Teachers (PSTs) to implement equity-driven pedagogies. Moreover, focused on individual retrospective gameplay engagement rates (participation time), current research fails to provide a holistic perspective of the practical manifestations of equity and inclusion in PE. This study fills this void with novel insights offered by a mixed-methods case study examining the following: (i) the process-oriented teaching strategies employed by a PST trained to deliver inclusive pedagogies, alongside student voices on lived experiences; and (ii) the outcome-oriented gameplay patterns across two teaching units (Basketball and Volleyball). Participants included one PST and 26 students. Extensive observations and focus groups mapped the applied teaching strategies and student responses. Video-based social network analysis captured equity and inclusion in students’ gameplay patterns, using metrics such as degree prestige. Findings indicate the PETE impact in inducing PSTs’ inclusive manipulation of learning activities and the fostering of inclusive team membership and positive collaboration. SNA metrics evidenced equitable and inclusive gameplay patterns. Despite pedagogical efforts, content-specific factors may influence students’ gameplay inclusion. Hence, a reflection on the multifaceted and non-linear nature of promoting inclusive participation is prompted. Full article

Maintaining player engagement is pivotal for video game success, yet achieving the optimal difficulty level that adapts to diverse player skills remains a significant challenge. Initial difficulty settings in games often fail to accommodate the evolving abilities of players, necessitating adaptive difficulty mechanisms to keep the gaming experience engaging. This study introduces a custom first-person-shooter (FPS) game to explore Dynamic Difficulty Adjustment (DDA) techniques, leveraging both performance metrics and emotional responses gathered from physiological sensors. Through a within-subjects experiment involving casual and experienced gamers, we scrutinized the effects of various DDA methods on player performance and self-reported game perceptions. Contrary to expectations, our research did not identify a singular, most effective DDA strategy. Instead, findings suggest a complex landscape where no one approach—be it performance-based, emotion-based, or a hybrid—demonstrably surpasses static difficulty settings in enhancing player engagement or game experience. Noteworthy is the data’s alignment with Flow Theory, suggesting potential for the Emotion DDA technique to foster engagement by matching challenges to player skill levels. However, the overall modest impact of DDA on performance metrics and emotional responses highlights the intricate challenge of designing adaptive difficulty that resonates with both the mechanical and emotional facets of gameplay. Our investigation contributes to the broader dialogue on adaptive game design, emphasizing the need for further research to refine DDA approaches. By advancing our understanding and methodologies, especially in emotion recognition, we aim to develop more sophisticated DDA strategies. These strategies aspire to dynamically align game challenges with individual player states, making games more accessible, engaging, and enjoyable for a wider audience. Full article

This paper investigates the cognitive stimulation experienced by players engaging in virtual reality (VR) puzzle games through the analysis of electroencephalography (EEG) data. The study employs magnitude-square coherence (MSC) and spectral entropy (SpEn) metrics to quantify neural activity patterns associated with problem-solving processes during gameplay. Results reveal unique coherence and entropy profiles across different VR gaming tasks, with Tetris gameplay eliciting heightened coherence and entropy values compared to other games. Specifically, Tetris demonstrates increased coherence between frontal and temporal brain regions, indicative of enhanced visuospatial processing and decision making. These findings underscore the importance of considering both spectral coherence and entropy when assessing the cognitive effects of video game tasks on brain activity. Insights from this study may inform the design of serious VR games aimed at promoting cognitive development and problem-solving skills in players. Full article

Research into the design of serious games still lacks metrics to evaluate engagement with the experience so that users can achieve the learning aims. This study presents the new EPUX metric, based on playability and User eXperience (UX) elements, to measure the capability of any serious game to maintain the attention of players. The metric includes (1) playability aspects: game items that affect the emotions of users and that constitute the different layers of the game, i.e., mechanics, dynamics and aesthetics; and (2) UX features: motivation, meaningful choices, usability, aesthetics and balance both in the short and in the long term. The metric is also adapted to evaluate virtual reality serious games (VR-SGs), so that changes may be considered to features linked to playability and UX. The case study for the assessment of the EPUX metric is Hellblade, developed in two versions: one for 2D-screens and the other for VR devices. The comparison of the EPUX metric scores for both versions showed that (1) some VR dynamics augmented the impact of gameplay and, in consequence, engagement capacity; and (2) some game design flaws were linked to much lower scores. Among those flaws were low numbers of levels, missions, and items; no tutorial to enhance usability; and lack of strategies and rewards to increase motivation in the long term. Full article

Serious games have to meet certain characteristics relating to gameplay and educational content to be effective as educational tools. There are some models that evaluate these aspects, but they usually lack a good balance between both ludic and learning requirements, and provide no guide for the design of new games. This study develops the Gaming Educational Balanced (GEB) Model which addresses these two limitations. GEB is based on the Mechanics, Dynamics and Aesthetics framework and the Four Pillars of Educational Games theory. This model defines a metric to evaluate serious games, which can also be followed to guide their subsequent development. This rubric is tested with three indie serious games developed using different genres to raise awareness of mental illnesses. This evaluation revealed two main issues: the three games returned good results for gameplay, but the application of educational content was deficient, due in all likelihood to the lack of expert educators participating in their development. A statistical and machine learning validation of the results is also performed to ensure that the GEB metric features are clearly explained and the players are able to evaluate them correctly. These results underline the usefulness of the new metric tool for identifying game design strengths and weaknesses. Future works will apply this metric to more serious games to further test its effectiveness and to guide the design of new serious games. Full article

Games have become one of the most popular activities across cultures and ages. There is ample evidence that supports the benefits of using games for learning and assessment. However, incorporating game activities as part of the curriculum in schools remains limited. Some of the barriers for broader adoption in classrooms is the lack of actionable assessment data, the fact that teachers often do not have a clear sense of how students are interacting with the game, and it is unclear if the gameplay is leading to productive learning. To address this gap, we seek to provide sequence and process mining metrics to teachers that are easily interpretable and actionable. More specifically, we build our work on top of Shadowspect, a three-dimensional geometry game that has been developed to measure geometry skills as well other cognitive and noncognitive skills. We use data from its implementation across schools in the U.S. to implement two sequence and process mining metrics in an interactive dashboard for teachers. The final objective is to facilitate that teachers can understand the sequence of actions and common errors of students using Shadowspect so they can better understand the process, make proper assessment, and conduct personalized interventions when appropriate. Full article

Metrics are important and well-known tools to measure users’ behavior in games, and gameplay in general. Particularities of location-aware games—a class of games where the player’s location plays a central role-demand specific support in metrics to adequately address the spatio-temporal features such games exhibit. In this article, we analyse and discuss how existing game analytics platforms address the spatio-temporal features of location-aware games. Our analysis reveals that little support is available. Next, based on the analysis, we propose a classification of spatial metrics, embedded in existing literature, and discuss three types of spatial metrics-point-, trajectory- and area-based metrics-, and elaborate examples and difficulties. Finally, we discuss how spatial metrics may be deployed to improve gameplay in location-aware games. Full article