Gameplay Sessions Research Articles

Advancing Mixed Reality Game Development: An Evaluation of a Visual Game Analytics Tool in Action-Adventure and FPS Genres

In response to the unique challenges of Mixed Reality (MR) game development, we developed GAMR , an analytics tool specifically designed for MR games. GAMR aims to assist developers in identifying and resolving gameplay issues effectively. It features reconstructed gameplay sessions, heatmaps for data visualization, a comprehensive annotation system, and advanced tracking for hands, camera, input, and audio, providing in-depth insights for nuanced game analysis. To evaluate GAMR 's effectiveness, we conducted an experimental study with game development students across two game genres: action-adventure and first-person shooter (FPS). The participants used GAMR and provided feedback on its utility. The results showed a significant positive impact of GAMR in both genres, particularly in action-adventure games. This study demonstrates GAMR 's effectiveness in MR game development and suggests its potential to influence future MR game analytics, addressing the specific needs of developers in this evolving area.

Evaluating ExpandED: Evaluating the effectiveness of a serious game expansion pack in teaching health professional students about interprofessional care.

The emergency department (ED) is a challenging fast-paced environment with high-acuity, undifferentiated patients who often require extensive interdisciplinary care. This paper introduces ExpandED, an expansion pack to the serious board game GridlockED, designed to enhance players' understanding of interprofessional collaboration in the ED and the diverse scope of practice of different ED professionals including physicians, residents, registered nurses, registered practical nurses, social workers, occupational therapists, and physiotherapists. This investigation evaluates the effectiveness of ExpandED as a teaching tool for medical and allied health professions students about interprofessional collaboration in the ED. A program evaluation harnessing a playtest framework was employed. Participants completed pre- and postgame surveys including quantitative measures (e.g., Likert scales) and qualitative free-text feedback that focused on participant familiarity with ED functioning, valuation of interprofessional collaboration before and after playing, and feedback on game usability and effectiveness. Recruitment was open to students in all health care and allied health professional programs at the institution. Forty-five participants were recruited from medical doctor, nursing, physiotherapy, and speech language pathology programs. ExpandED enhances participants' understanding of ED workflow (p < 0.001) and provides an enjoyable playing experience. However, participants' valuation of interdisciplinary teamwork did not change significantly before and after game play (p = 0.17). Participants expressed satisfaction with the game's accuracy in simulating the ED environment and appreciated the opportunity to collaborate with peers from different disciplines. Challenges reported included some tension among players, potential biases, and limitations of fidelity to a real-life ED. While this study has limitations regarding participant sampling and duration of gameplay sessions, it highlights the potential of ExpandED for teaching interprofessional collaboration in the ED. These findings will guide further development to optimize the expansion pack's effectiveness and its implementation into health care curricula.

Education in the open: Building a network for social action

This paper introduces Fast Food da Politica (FFDP) as a case study of a learning network designed to promote social action in a developing country. Our focus is on exploring FFDP design elements, such as those related to tools, tasks and social organization, and the connections between these elements and valued learning activity. FFDP cleverly (re)purposes popular (board) games as pedagogical tools, which are then customised for the teaching and learning of the mechanisms and functioning of Brazilian political structures. FFDP has taken their political games to game-playing sessions across the country in varied venues – including schools, government organisations, and open sessions at market-street events and public protests. Their games are shared as open learning resources through blueprints and manuals that explain the many ways a game can be played, and which are easily downloadable through their website. FFDP also encourages game users (educators and learners) to come up with and share their own game-playing ideas for reuse. As a result, FFDP has built a repository of games that is constantly evolving, as new ways of using their open resources are captured and packaged for sharing and reuse by others. As a not-for-profit organisation, FFDP has been successfully relying on social media and crowdsourced funding to survive. In this paper, we draw on the ACAD Wireframe to explore the alignment of this network’s design elements at the micro and meso levels, focusing on the ways FFDP combines a strategic educational vision deeply grounded on action for social change, with a curriculum that emphasizes gaming elements and promotes the physicality of materials in learning. At the micro level, the case study examines how the quality of materials support the development of educational innovation, while at the meso level this organization, driven by young women, is building-up a learning network for social action, empowering children, youth and adults to learn about the mechanisms of politics and their civil rights, within the Brazilian context. Overall, this paper offers an inspiring example of a productive learning network in action, where participation and co-creation are fostered through connections between a network of people, ideas, digital and material elements.

Teaching in a collaborative mathematic learning activity with and without a social robot

AbstractThere is a growing interest in whether social robots, which are embodied and exhibit human-like behaviour, can be used for teaching and learning. Still, very few studies focus on the teacher’s role. This study focuses on how a teacher acted in a learning-by-teaching activity with 20 children. In this small-scale field experiment, the teacher’s interactions and teaching actions were observed when the teacher scaffolded a learning activity where children played a collaborative digital mathematics game to strengthen their mathematical reasoning and conceptual understanding of arithmetic. When playing, the children were acting as tutors for a tutee, according to the learning-by-teaching principle. In one scenario, the tutee was a younger child; in the other, the tutee was a social robot. Twenty 30-minute game-playing sessions are observed, video-recorded, and transcribed. The study explores the teacher’s interactions and teaching actions in the two scenarios and discusses the results from the perspective of the teacher’s role, social norms, and teacher digital competence. The interaction and thematic analyses show similarities and characteristic differences in the teacher’s interaction patterns in the two scenarios. The teaching actions are similar on a structural level and differ regarding the types and distribution of teaching actions. In the child-child scenario, the teacher directs most teaching actions to both players, and the actions are didactic (mathematical) scaffolding. In contrast, in the child-robot scenario, the teacher only addresses the tutor, and the scaffolding is socially oriented. Implications for a teaching practice involving social robots as learning companions are discussed regarding teachers’ presence and participation, types of social robot knowledge that go beyond digital competence, and new challenges introduced by using social robots as learning companions in the classroom. The study contributes new insights into the teacher’s role and actions when teaching with a social robot in a collaborative learning situation, which is relevant for educational research and teaching practice.

Using provenance and replay for qualitative analysis of gameplay sessions

There is an increasing interest to use game telemetry for analyzing gameplay sessions, with numerous techniques created to help game developers analyze different game aspects like game balancing and behavioral analysis. Among different gameplay session analysis techniques, the collection of provenance data has stood out due to a crucial advantage of this approach: the possibility to identify cause–effect relationships between game events. In previous work, we presented our conceptual framework called Prov-Replay, which provides a replay synchronized with an interactive provenance graph visualization. We validate Prov-Replay by creating PinGU Replay, a tool that implements our conceptual framework and applied it in a commercial game. Due to the promising results, this paper extends our previous work by presenting a detailed overview about Prov-Replay implementation, introducing a new feature that provides an analysis dashboard, and applying our experiment methodology to a new commercial game. We also enhance the concept of analytics related to provenance through the replay pipeline. Finally, we made PinGU Replay available as open-source software. Our new experiment results reinforce that, when using our conceptual framework fundamentals, it is possible to improve the efficiency and effectiveness of qualitative analysis process.

ECEC educators’ Attitudes and Perceptions Toward and Supportive Role in children’s Digital Gameplay: The Emotion Detectives Game as an example

ABSTRACT Today, digital games are considered important tools for learning, but using them in early childhood education and care (ECEC) has raised the question of educators’ roles when children play an educational digital game. The aim of this study was to explore how ECEC educators differed in their attitudes and perceptions toward and ways of supporting children’s digital gameplay and how these differences were associated with children’s gameplay. The data were collected by interviewing seven ECEC educators in four ECEC groups in which 30 children aged 5–6 years played an educational Emotion Detectives game. The log data were also analyzed. Research Findings: Based on the differences in support discovered, the educators were found to represent two groups: a non-supportive group (NSG) and a supportive group (SG). Compared to the NSG, the children in the SG played more and were more successful in their gameplay. Practice or Policy: The findings of this study emphasize the importance of organizing regular gameplay sessions when children’s development is supported using educational games and of monitoring that all children have opportunities to get enough practice by playing. In addition, educators need to familiarize themselves with the game to be able to discuss it with children, motivate them to play, and help on demand.

Integrating Smartphone Sensor Technology to Enhance Fine Motor and Working Memory Skills in Pediatric Obesity: A Gamified Approach

Childhood obesity remains a pervasive global challenge, often accompanied by deficits in working memory and fine motor skills among affected children. These deficits detrimentally impact academic performance. Despite limited evidence, home-based interventions targeting both fine motor skills and working memory remain underexplored. Leveraging game-based approaches holds promise in behavior modification, self-management of chronic conditions, therapy adherence, and patient monitoring. In this study, a novel smartphone-based game was meticulously developed to target the enhancement of working memory and fine motor skills in a cohort of thirty-two obese or overweight children. Over two weeks, participants engaged in regular gameplay sessions within the comfort of their homes. Pretest and post-test assessments yielded compelling evidence of significant improvements, with statistical significance established at a robust 95% confidence level. Notably, participants exhibited a progressive trend of improvement in their gameplay performance. Recognizing the profound impact of academic achievement on future socioeconomic trajectories, regardless of weight management outcomes, the importance of bolstering cognitive skills cannot be overstated. This innovative intervention offers a pragmatic and cost-effective solution to empower children to cultivate essential cognitive abilities within their home environment. By fostering the development of working memory and fine motor skills, this intervention holds promise in facilitating improved academic performance and, consequently, enhancing long-term prospects for these children.

Addressing Data Absenteeism and Technology Chauvinism in the Use of Gamified Wearable Gloves Among Older Adults: Moderated Usability Study.

Digital health technologies have the potential to improve health outcomes for older adults, especially for those recovering from stroke. However, there are challenges to developing these technologies, such as data absenteeism (where older adults' views are often underrepresented in research and development) and technology chauvinism (the belief that sophisticated technology alone is the panacea to addressing health problems), which hinder their effectiveness. In this study, we aimed to address these challenges by developing a wearable glove integrated with culturally relevant exergames to motivate older adults to exercise and, for those recovering from stroke, to adhere to rehabilitation. We conducted a moderated usability study with 19 older adults, of which 11 (58%) had a history of stroke. Our participants engaged in a 30-minute gameplay session with the wearable glove integrated with exergames, followed by a quantitative survey and an in-depth interview. We used descriptive analysis to compare responses to the System Usability Scale between those who had a history of stroke and those who did not. In addition, we analyzed the qualitative interviews using a bottom-up thematic analysis to identify key themes related to the motivations and barriers regarding the use of wearable gloves for rehabilitation and exercise. Our study generated several key insights. First, making the exergames exciting and challenging could improve exercise and rehabilitation motivation, but it could also have a boomerang effect, where participants may become demotivated if the games were very challenging. Second, the comfort and ease of use of the wearable gloves were important for older adults, regardless of their stroke history. Third, for older adults with a history of stroke, the functionality and purpose of the wearable glove were important in helping them with specific exercise movements. Our findings highlight the importance of providing contextual support for the effective use of digital technologies, particularly for older adults recovering from stroke. In addition to technology and usability factors, other contextual factors such as gamification and social support (from occupational therapists or caregivers) should be considered to provide a comprehensive approach to addressing health problems. To overcome data absenteeism and technology chauvinism, it is important to develop digital health technologies that are tailored to the needs of underserved communities. Our study provides valuable insights for the development of digital health technologies that can motivate older adults recovering from stroke to exercise and adhere to rehabilitation.

Using In-game Analytics to Explore Learning Dynamics of Information Literacy in a Social Media Simulator

The emergence of Game-Based Learning (GBL) strategies to promote critical thinking has generated a growing need for analytical tools able to assess their effectiveness. Current methods typically apply qualitative approaches such as focus groups and survey-based questionnaires evaluating players’ knowledge, skills, and attitudes before and after playing the game; these methods are flexible, and they provide valuable information, yet new methods are needed to improve our understanding of what is happening during a gameplay session.&#x0D; The challenge of understanding the internal dynamics of GBL are illustrated by the open debate on gamified disinformation inoculators. These educational tools teach players how to identify disinformation by training them with a small set of fake news displayed within a gaming experience. There is an ongoing debate on what exactly is being learnt with these inoculators as some studies suggest a positive effect while other ones reveal that they promote scepticism instead of resistance against fake news. It is argued that new analytical methods are required to capture the learning process: detailed data collection on gameplay would be extremely useful to identify the most useful traits of Game-Based Learning, while detecting potential limitations or negative effects of this valuable learning resource.&#x0D; This work presents a novel framework to assess GBL dynamics grounded on data analytics. The approach uses the potential of the Unity in-game analytics platform to collect detailed data on how players tackle the challenges posed by the game mechanics; this diverse information may include the full set of decisions and interactions of the player as well as additional information such as the number of tries or time lapse between interactions. The behavioural data is then combined with content metadata information to infer general learning dynamics amongst players.&#x0D; This analytical framework is applied to a social media simulator included in the video game Julia: A Science Journey. Results suggest that the framework can reveal novel insights including aspects such as the level of engagement of the players, the impact of the type of content on the correct assessment of fake news, and the relation between reading speed and performance.

Children and Young People’s Experiences and Understandings of Gambling-Style Systems in Digital Games: Loot Boxes, Popular Culture, and Changing Childhoods

Developing current geographical debates on children’s digital geographies and popular culture, this article examines children and young people’s experiences and understandings of gambling-style systems in digital games. Chance-based mechanisms such as loot boxes are a growing feature of the global gaming industry. This article examines the space between gaming and gambling and provides new perspectives to this emerging field, drawing on empirical research from video ethnography game-play sessions with children and young people. This article uniquely foregrounds these accounts, giving room for their voices in a debate dominated by adults. We argue gambling-style systems must be understood within children’s everyday sociospatial experiences, including friendship, family, and curating collections. We provide a fuller picture of children and young people’s situatedness and negotiations around digital gaming through interviews with parents and game designers. We demonstrate the conceptually striking ways they narrate generational change, mobilizing powerful social constructions of childhood. We advance understandings of children’s popular culture and nostalgia in academic debates on digital childhoods, arguing that loot boxes are a new and important lens through which to view wider anxieties. Furthermore, we reveal potential risks associated with these systems and offer recommendations for a timely international policy debate.

Communication Challenges in Social Board Games

Background Discussion-based communication scenarios are present in many aspects of life. These can range from conversations with friends in a social setting to formal consultation processes and focus groups used by industry and government. However, reliance on speech does not easily permit the fair and equitable involvement of people who face communication-based accessibility challenges. Aim This work aimed to understand the communication challenges present within social board games, how these challenges arise, and participants’ perceptions of the difficulties these challenges may cause. Method We conducted four social gameplay sessions to understand what parts of discussion may cause communication challenges and what techniques are commonplace in overcoming these. Results Our results highlight how group facilitation and conversation pacing are essential in promoting accessibility within discussion-type situations. Our analysis identified four themes that focused on speech and delivery, access strategies, viewing and position, balance of power, and awareness of others. Conclusions Communication within board game scenarios is a complex area that creates several intersectional accessibility challenges. These challenges can impact how group communication is facilitated, how pacing and delivery relate to overall group understanding, and how an awareness of accessibility is critical in developing inclusive environments.

Leveraging machine learning to examine engagement with a digital therapeutic.

Digital Therapeutics (DTx) are evidence-based software-driven interventions for the prevention, management, and treatment of medical disorders or diseases. DTx offer the unique ability to capture rich objective data about when and how a patient engages with a treatment. Not only can one measure the quantity of patient interactions with a digital treatment with high temporal precision, but one can also assess the quality of these interactions. This is particularly useful for treatments such as cognitive interventions, where the specific manner in which a patient engages may impact likelihood of treatment success. Here, we present a technique for measuring the quality of user interactions with a digital treatment in near-real time. This approach produces evaluations at the level of a roughly four-minute gameplay session (mission). Each mission required users to engage in adaptive and personalized multitasking training. The training included simultaneous presentation of a sensory-motor navigation task and a perceptual discrimination task. We trained a machine learning model to classify user interactions with the digital treatment to determine if they were "using it as intended" or "not using it as intended" based on labeled data created by subject matter experts (SME). On a held-out test set, the classifier was able to reliably predict the SME-derived labels (Accuracy = .94; F1 Score = .94). We discuss the value of this approach and highlight exciting future directions for shared decision-making and communication between caregivers, patients and healthcare providers. Additionally, the output of this technique can be useful for clinical trials and personalized intervention.

Multimodal Data Fusion to Track Students’ Distress during Educational Gameplay

Using multimodal data fusion techniques, we built and tested prediction models to track middle-school student distress states during educational gameplay. We collected and analyzed 1,145 data instances, sampled from a total of 31 middle-school students’ audio- and video-recorded gameplay sessions. We conducted data wrangling with student gameplay data from multiple data sources, such as individual facial expression recordings and gameplay logs. Using supervised machine learning, we built and tested candidate classifiers that yielded an estimated probability of distress states. We then conducted confidence-based data fusion that averaged the estimated probability scores from the unimodal classifiers with a single data source. The results of this study suggest that the classifier with multimodal data fusion improves the performance of tracking distress states during educational gameplay, compared to the performance of unimodal classifiers. The study finding suggests the feasibility of multimodal data fusion in developing game-based learning analytics. Also, this study proposes the benefits of optimizing several methodological means for multimodal data fusion in educational game research.

Engaging cognitive domains of people with dementia during tablet game play

AbstractBackgroundKeeping cognitively active is important for people living with dementia or MCI but finding engaging activities can be challenging. Playing digital games has been shown to engage cognitive processes such as visual attention, working memory, and visuomotor skills, in children and adults. This study explored whether these cognitive domains are engaged when people living with dementia or MCI play digital games.MethodParticipants (n = 32) with dementia or MCI, mean MoCA 13.9, were recruited from adult day programs to play four mainstream games on touchscreen tablets. Each session was recorded and a video coding scheme was developed using behavioral analysis software to map observable behaviors, including eye gaze, hand movements, and game strategy, to five DSM cognitive domains : complex attention, executive function, learning and memory, language, and perceptual motor (DSM‐V, 2013). Five minutes from each initial gameplay session were analyzed to explore the engagement of cognitive domains.ResultsAll five cognitive domains were engaged but varied across the games. For example, a word search games engaged language much more than a jigsaw puzzle game which was higher on perceptual‐motor skills. The most engaged cognitive domain was ‘complex attention’ and the least engaged cognitive domain was ‘language’. All participants demonstrated cognitive engagement whilst playing the games.ConclusionPlaying digital games can provide cognitive engagement for people living with dementia or MCI. Using mainstream games increases accessibility of opportunities for cognitive engagement, in a way which is also immersing and rewarding. Future research incorporating eye tracking will increase understanding of how people living with dementia learn to play digital games and respond to prompts. This study presents huge potential for low cost cognitive activity for people with dementia or MCI.

"I Want To See How Smart This AI Really Is": Player Mental Model Development of an Adversarial AI Player

Understanding players' mental models are crucial for game designers who wish to successfully integrate player-AI interactions into their game. However, game designers face the difficult challenge of anticipating how players model these AI agents during gameplay and how they may change their mental models with experience. In this work, we conduct a qualitative study to examine how a pair of players develop mental models of an adversarial AI player during gameplay in the multiplayer drawing game iNNk. We conducted ten gameplay sessions in which two players (n = 20, 10 pairs) worked together to defeat an AI player. As a result of our analysis, we uncovered two dominant dimensions that describe players' mental model development (i.e., focus and style). The first dimension describes the focus of development which refers to what players pay attention to for the development of their mental model (i.e., top-down vs. bottom-up focus). The second dimension describes the differences in the style of development, which refers to how players integrate new information into their mental model (i.e., systematic vs. reactive style). In our preliminary framework, we further note how players process a change when a discrepancy occurs, which we observed occur through comparisons (i.e., compare to other systems, compare to gameplay, compare to self). We offer these results as a preliminary framework for player mental model development to help game designers anticipate how different players may model adversarial AI players during gameplay.

Information System for Symptom Diagnosis and Improvement of Attention Deficit Hyperactivity Disorder: Protocol for a Nonrandomized Controlled Pilot Study.

BackgroundAttention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders during childhood; however, the diagnosis procedure remains challenging, as it is nonstandardized, multiparametric, and highly dependent on subjective evaluation of the perceived behavior.ObjectiveTo address the challenges of existing procedures for ADHD diagnosis, the ADHD360 project aims to develop a platform for (1) early detection of ADHD by assessing the user’s likelihood of having ADHD characteristics and (2) providing complementary training for ADHD management.MethodsA 2-phase nonrandomized controlled pilot study was designed to evaluate the ADHD360 platform, including ADHD and non-ADHD participants aged 7 to 16 years. At the first stage, an initial neuropsychological evaluation along with an interaction with the serious game developed (“Pizza on Time”) for approximately 30-45 minutes is performed. Subsequently, a 2-week behavior monitoring of the participants through the mADHD360 app is planned after a telephone conversation between the participants’ parents and the psychologist, where the existence of any behaviors characteristic of ADHD that affect daily functioning is assessed. Once behavior monitoring is complete, the research team invites the participants to the second stage, where they play the game for a mean duration of 10 weeks (2 times per week). Once the serious game is finished, a second round of behavior monitoring is performed following the same procedures as the initial one. During the study, gameplay data were collected and preprocessed. The protocol of the pilot trials was initially designed for in-person participation, but after the COVID-19 outbreak, it was adjusted for remote participation. State-of-the-art machine learning (ML) algorithms were used to analyze labeled gameplay data aiming to detect discriminative gameplay patterns among the 2 groups (ADHD and non-ADHD) and estimate a player’s likelihood of having ADHD characteristics. A schema including a train-test splitting with a 75:25 split ratio, k-fold cross-validation with k=3, an ML pipeline, and data evaluation were designed.ResultsA total of 43 participants were recruited for this study, where 18 were diagnosed with ADHD and the remaining 25 were controls. Initial neuropsychological assessment confirmed that the participants in the ADHD group showed a deviation from the participants without ADHD characteristics. A preliminary analysis of collected data consisting of 30 gameplay sessions showed that the trained ML models achieve high performance (ie, accuracy up to 0.85) in correctly predicting the users’ labels (ADHD or non-ADHD) from their gameplay session on the ADHD360 platform.ConclusionsADHD360 is characterized by its notable capacity to discriminate player gameplay behavior as either ADHD or non-ADHD. Therefore, the ADHD360 platform could be a valuable complementary tool for early ADHD detection.Trial RegistrationClinicalTrials.gov NCT04362982; https://clinicaltrials.gov/ct2/show/NCT04362982International Registered Report Identifier (IRRID)RR1-10.2196/40189

The dual role of humanoid robots in education: As didactic tools and social actors

The idea of using social robots for teaching and learning has become increasingly prevalent and robots are assigned various roles in different educational settings. However, there are still few authentic studies conducted over time. Our study explores teachers' perceptions of a learning activity in which a child plays a digital mathematics game together with a humanoid robot. The activity is based on the idea of learning-by-teaching where the robot is designed to act as a tutee while the child is assigned the role of a tutor. The question is how teachers perceive and talk about the robot in this collaborative child-robot learning activity? The study is based on data produced during a 2-years long co-design process involving teachers and students. Initially, the teachers reflected on the general concept of the learning activity, later in the process they participated in authentic game-play sessions in a classroom. All teachers' statements were transcribed and thematically coded, then categorized into two different perspectives on the robot: as a social actor or didactic tool. Activity theory was used as an analytical lens to analyze these different views. Findings show that the teachers discussed the activity’s purpose, relation to curriculum, child-robot collaboration, and social norms. The study shows that teachers had, and frequently switched between, both robot-perspectives during all topics, and their perception changed during the process. The dual perspectives contribute to the understanding of social robots for teaching and learning, and to future development of educational robot design.

Surface Electromyography-Driven Therapeutic Gaming for Rehabilitation of Upper Extremity Weakness: A Pilot Study.

In patients with severe upper extremity weakness that may result from peripheral nerve injuries, stroke, and spinal cord injuries, standard therapy in the earliest stages of recovery consists primarily of passive rather than active exercises. Adherence to prescribed therapy may be poor, which may contribute to suboptimal functional outcomes. The authors have developed and integrated a custom surface electromyography device with a video game to create an interactive, biofeedback-based therapeutic gaming platform. Sensitivity of the authors' custom surface electromyography device was evaluated with simultaneous needle electromyography recordings. Testing of this therapeutic gaming platform was conducted with a single 30-minute gameplay session in 19 patients with a history of peripheral nerve injury, stroke, spinal cord injury, and direct upper extremity trauma, including 11 patients who had undergone nerve and/or tendon transfers. The device was highly sensitive in detecting low levels of voluntary muscle activation and was used with 10 distinct muscles of the arm, forearm, and hand. Nerve and tendon transfer patients successfully activated the donor nerve/muscle and elicited the desired movement to engage in gameplay. On surveys of acceptability and usability, patients felt the system was enjoyable, motivating, fun, and easy to use, and their hand therapists expressed similar enthusiasm. Surface electromyography-based therapeutic gaming is a promising approach to rehabilitation that warrants further development and investigation to examine its potential efficacy, not only for building muscle strength and endurance but also for facilitating motor relearning after nerve and tendon transfer surgical procedures. Therapeutic, IV.

An explorer’s journal for machines: Exploring the case of Cyberpunk 2077

With increasing technological capabilities, video games provide ever more expansive virtual worlds for players to explore. Designers employ various mechanics and level design principles to encourage such exploration. However, what motivates people to explore in virtual environments, and which approaches are successful, is not yet clearly established. Methods for measuring player experience often take place retroactively after a play session, relying on recall, and are thus prone to missing less salient events and their motivations. Journaling events as they occur allow promises to be a suitable method of collecting data, but presents a challenge in aggregating data points due to their freeform nature. This makes it difficult to identify overarching patterns of exploration behaviour. We present an exploratory study in devising a journaling protocol for gathering information during gameplay sessions, focused on documenting moments of exploration in a manner that balances ease of data capture and data evaluation. We describe our efforts in prototyping this journaling method and how we used it to analyse Cyberpunk 2077 and several other game designs. Our results show that exploration occurs in a pattern of event → expectation → resolution. The motivation to explore depends on the expectations that a game raises through its marketing and design. At the same time, exploratory behaviour going unrewarded by the game quickly reduces the desire to explore. When recording instances of exploration, the addition of emotion words is essential in order to properly contextualize the information. This article lays the foundation for documenting exploration in games, as well as other emotional or behavioural constructs that require in-the-moment data collection.

An old game, new experience: exploring the effect of players’ personal gameplay history on game experience

This experimental study explores how game experience differs between players with different gameplay histories within the same game universe. We are interested in how prolonged engagement with a game series affects the gameplay experience in relation to the most recent game version in the series. A total of 54 participants were divided into four groups depending on their gaming experience, namely non-gamers, new-gamers, old-gamers and core-gamers. They played the mobile version of Super Mario Run, and questionnaire data was collected after the gameplay session. The results of the study showed that not only the players’ personal gameplay history but also the length of experience or degree of familiarity with the game universe affected the experience of playing a new game in the same game universe. Additionally, familiarity with the game universe had a positive impact on the feeling of competence, immersion, emotions and flow.