Robotics education fosters computational thinking, creativity, and problem-solving, but remains challenging due to technical complexity. Game-based learning (GBL) and gamification offer engagement benefits, yet their comparative impact remains unclear. We present the first PRISMA-aligned systematic review and comparative synthesis of GBL and gamification in robotics education, analyzing 95 studies from 12,485 records across four databases (2014–2025). We coded each study’s approach, learning context, skill level, modality, pedagogy, and outcomes (κ =.918). Three patterns emerged: (1) approach–context–pedagogy coupling (GBL more prevalent in informal settings, while gamification dominated formal classrooms [p <.001] and favored project-based learning [p =.009]); (2) emphasis on introductory programming and modular kits, with limited adoption of advanced software (~17%), advanced hardware (~5%), or immersive technologies (~22%); and (3) short study horizons, relying on self-report. We propose eight research directions and a design space outlining best practices and pitfalls, offering actionable guidance for robotics education.

Intelligence and knowledgeability are sometimes treated interchangeably in virtual agents, yet they shape interaction in different ways. We disentangled these traits and tested how each drives human perceptions and interaction in virtual reality (VR). To address the lack of prior research examining both traits simultaneously, we created a VR application where participants collaborated with a virtual agent to complete a jigsaw puzzle while engaging in free-flowing conversation about the puzzle’s art piece. We manipulated intelligence through the virtual agent’s puzzle-solving ability and knowledgeability through its predefined depth of knowledge in art. Using a 2 × 2 within-group study, we collected perceptual responses, logged data, and qualitative feedback. Results showed intelligence significantly influenced perceptions of intelligence, knowledge, rapport, trust, co-presence, uncanny valley, and intelligence and knowledge comparisons, while knowledgeability impacted perceived knowledge, trust, and intelligence and knowledge comparisons. Interaction effects further highlighted their interdependence, offering design implications for virtual agents.

Preference-based reinforcement learning (PbRL) has emerged as a promising paradigm for teaching robots complex behaviors without reward engineering. However, its effectiveness is often limited by two critical challenges: the reliance on extensive human input and the inherent difficulties in resolving query ambiguity and credit assignment during reward learning. In this paper, we introduce PRIMT, a PbRL framework designed to overcome these challenges by leveraging foundation models (FMs) for multimodal synthetic feedback and trajectory synthesis. Unlike prior approaches that rely on single-modality FM evaluations, PRIMT employs a hierarchical neuro-symbolic fusion strategy, integrating the complementary strengths of vision-language models (VLMs) and large language models (LLMs) in evaluating robot behaviors for more reliable and comprehensive feedback. PRIMT also incorporates foresight trajectory generation to warm-start the trajectory buffer with bootstrapped samples, reducing early-stage query ambiguity, and hindsight trajectory augmentation for counterfactual reasoning with a causal auxiliary loss to improve credit assignment. We evaluate PRIMT on 2 locomotion and 6 manipulation tasks on various benchmarks, demonstrating superior performance over FM-based and scripted baselines. Website at https://primt25.github.io/.

In virtual reality, creating intelligent virtual characters has been a long-lasting endeavor. However, while researchers have investigated several aspects of a virtual character’s intelligence, little attention has been paid to the impact of the implemented intelligence levels assigned to a virtual character during human–virtual character collaboration. Thus, we conducted a within-group study ( \(N=24\) ) to explore how three different intelligence levels (low vs. medium vs. high) assigned to a virtual character can impact how study participants perceive that virtual character and interact with the task they are instructed to complete. Specifically, for our study, we developed a jigsaw puzzle game and instructed our participants to solve it with the help of a virtual character. During the jigsaw puzzle solving process, we collected application logs related to how the participants executed the task and observed the virtual environment. Moreover, after each condition, we asked the participants to respond using a questionnaire that examined their social presence, how they perceived the character’s intelligence and compared it with their own, and how they rated the virtual character’s realism. Our results indicated that the different intelligence levels assigned to the virtual characters impacted participants’ responses on several variables, including co-presence, perceived intelligence, intelligence comparison, and character interaction and behavior realism. Moreover, based on the collected logged data, we found that the intelligence levels assigned to our virtual character significantly impacted the performance of our participants. Our results could be valuable to the research community for creating more engaging experiences with intelligent virtual characters for collaborative tasks in immersive environments.

Designing interactions between humans and virtual characters requires careful consideration of various human perceptions and user experiences. While numerous studies have explored the effects of several virtual characters' properties, the impacts of the virtual character's personality and age on human perceptions and experiences have yet to be thoroughly investigated. To address this gap, we conducted a within-group study (N = 28) following a 2 (personality: egoism vs. altruism) × 2 (age: child vs. adult) design to explore how the personality and age factors influence human perception and experience during interactions with virtual characters. In each condition of our study, our participants co-solved a jigsaw puzzle with a virtual character that embodied combinations of personality and age. After each condition, participants completed a survey. We also asked them to provide written feedback at the end of the study. Our statistical analyses revealed that the virtual character's personality and age significantly influenced participants' perceptions and experiences. The personality factor affected perceptions of altruism, anthropomorphism, likability, safety, and all aspects of user experience, including perceived collaboration, rapport, emotional reactivity, and the desire for future interaction. Additionally, the virtual character's age affected our participants' ratings of the uncanny valley and likability. We also identified an interaction effect between personality and age factors on the virtual character's anthropomorphism. Based on our findings, we offered guidelines and insights for researchers aiming to design collaborative experiences with virtual characters of different personalities and ages.

‘Juicy’ or immediate abundant action feedback is widely held to make video games enjoyable and intrinsically motivating. Yet we do not know why it works: Which motives are mediating it? Which features afford it? In a pre-registered (n=1,699) online experiment, we tested three motives mapping prior practitioner discourse—effectance, competence, and curiosity—and connected design features. Using a dedicated action RPG and a 2x2+control design, we varied feedback amplification, success-dependence, and variability and recorded self-reported effectance, competence, curiosity, and enjoyment as well as free-choice playtime. Structural equation models show curiosity as the strongest enjoyment and only playtime predictor and support theorised competence pathways. Success dependence enhanced all motives, while amplification unexpectedly reduced them, possibly because the tested condition unintentionally impeded players’ sense of agency. Our study evidences uncertain success affording curiosity as an underappreciated moment-to-moment engagement driver, directly supports competence-related theory, and suggests that prior juicy game feel guidance ties to legible action-outcome bindings and graded success as preconditions of positive ‘low-level’ user experience.

We investigate how gender-anonymous voice avatars influence women’s performance in online computing group work. Female participants worked with two male confederates. Voices were filtered according to four voice gender anonymity conditions: (1) All unmasked, (2) Male confederates masked, (3) Female participant masked, and (4) All masked. When only male confederates used masked voices (compared to all unmasked), female participants spoke for a longer period of time and scored higher on computing problems. When everyone used masked voices (compared to all unmasked), female participants spoke for a longer period of time, spoke more words, and scored higher on computing problems. Effects were not significant on subjective measures and one behavioral measure. We discuss the implications for virtual interactions between people.

Although researchers have explored how humans perceive the intelligence of virtual characters, few studies have focused on the ability of intelligent virtual characters to fix their mistakes. Thus, we explored the self-correction behavior of a virtual character with different intelligence capabilities in a within-group design ( \(N=23\) ) study. For this study, we developed a virtual character that can solve a jigsaw puzzle whose self-correction behavior is controlled by two parameters, namely, Intelligence and Accuracy of Self-correction . Then, we integrated the virtual character into our virtual reality experience and asked participants to co-solve a jigsaw puzzle. During the study, our participants were exposed to five experimental conditions resulting from combinations of the Intelligence and Accuracy of Self-correction parameters. In each condition, we asked our participants to respond to a survey examining their perceptions of the virtual character’s intelligence and awareness (private, public, and surroundings awareness) and user experiences, including trust, enjoyment, performance, frustration, and desire for future interaction. We also collected application logs, including participants’ dwell gaze data, completion times, and the number of puzzle pieces they placed to co-solve the jigsaw puzzle. The results of all the survey ratings and the completion time were statistically significant. Our results indicated that higher levels of Intelligence and Accuracy of Self-correction enhanced not only our participants’ perceptions of the virtual character’s intelligence, awareness (private, public, and surroundings), trustworthiness, and performance but also increased their enjoyment and desire for future interaction with the virtual character while reducing their frustration and completion time. Moreover, we found that as the Intelligence and Accuracy of Self-correction increased, participants had to place fewer puzzle pieces and needed less time to complete the jigsaw puzzle. Finally, regardless of the experimental condition to which we exposed our participants, they gazed at the virtual character for more time compared to the puzzle pieces and puzzle goal in the virtual environment.

We explored the avoidance movement behaviors of study participants immersed in a virtual reality environment. We placed a static virtual character at the midpoint between the start and target spot for the avoidance task, and a virtual walker character in front of the starting spot and scripted it to reach the target spot. Participants were placed behind the virtual walker in order to measure its influence on participants’ behavior. We developed nine experimental conditions assigned to the virtual walker character by following a 3 (speed: slow vs. normal vs. fast walking speed) $\times 3$ (proximity: close vs. middle vs. far proximity to the static virtual character) study design. For this within-group study, we collected data from 22 study participants to explore how speed and proximity walking patterns assigned to a virtual walker character could impact participants’ avoidance movement behaviors and decisions. Our data revealed that 1) the speed factor impacted the participants’ avoidance movement behavior; 2) the proximity factor did not significantly impact the participants’ avoidance movement behavior; 3) the virtual walker character did not significantly impact participants’ avoidance decisions regarding the static virtual character; 4) in all examined conditions, the side-by-side distances between the participants and the static virtual character were inside the social space according to the proxemics model; and 5) in conditions in which a slow virtual walker character was present or in the condition of normal speed and far proximity, we observed an increased number of participants pass the virtual walker character.

We developed a method to synthesize game levels that accounts for the degree of collaboration required by two players to finish a given game level. We first asked a game level designer to create playable game level chunks. Then, two artificial intelligence (AI) virtual agents driven by behavior trees played each game level chunk. We recorded the degree of collaboration required to accomplish each game level chunk by the AI virtual agents and used it to characterize each game level chunk. To synthesize a game level, we assigned to the total cost function cost terms that encode both the degree of collaboration and game level design decisions. Then, we used a Markov-chain Monte Carlo optimization method, called simulated annealing, to solve the total cost function and proposed a design for a game level. We synthesized three game levels (low, medium, and high degrees of collaboration game levels) to evaluate our implementation. We then recruited groups of participants to play the game levels to explore whether they would experience a certain degree of collaboration and validate whether the AI virtual agents provided sufficient data that described the collaborative behavior of players in each game level chunk. By collecting both in-game objective measurements and self-reported subjective ratings, we found that the three game levels indeed impacted the collaboration gameplay behavior of our participants. Moreover, by analyzing our collected data, we found moderate and strong correlations between the participants and the AI virtual agents. These results show that game developers can consider AI virtual agents as an alternative method for evaluating the degree of collaboration required to finish a game level.

The refresh rate of virtual reality (VR) head-mounted displays (HMDs) has been growing rapidly in recent years because of the demand to provide higher frame rate content as it is often linked with a better experience. Today's HMDs come with different refresh rates ranging from 20Hz to 180Hz, which determines the actual maximum frame rate perceived by users' naked eyes. VR users and content developers often face a choice because having high frame rate content and the hardware that supports it comes with higher costs and other trade-offs (such as heavier and bulkier HMDs). Both VR users and developers can choose a suitable frame rate if they are aware of the benefits of different frame rates in user experience, performance, and simulator sickness (SS). To our knowledge, limited research on frame rate in VR HMDs is available. In this paper, we aim to fill this gap and report a study with two VR application scenarios that compared four of the most common and highest frame rates currently available (60, 90, 120, and 180 frames per second (fps)) to explore their effect on users' experience, performance, and SS symptoms. Our results show that 120fps is an important threshold for VR. After 120fps, users tend to feel lower SS symptoms without a significant negative effect on their experience. Higher frame rates (e.g., 120 and 180fps) can ensure better user performance than lower rates. Interestingly, we also found that at 60fps and when users are faced with fast-moving objects, they tend to adopt a strategy to compensate for the lack of visual details by predicting or filling the gaps to try to meet the performance needs. At higher fps, users do not need to follow this compensatory strategy to meet the fast response performance requirements.

Avatar customization is known to positively affect crucial outcomes in numerous domains. However, it is unknown whether audial customization can confer the same benefits as visual customization. We conducted a preregistered 2 x 2 (visual choice vs. visual assignment x audial choice vs. audial assignment) study in a Java programming game. Participants with visual choice experienced higher avatar identification and autonomy. Participants with audial choice experienced higher avatar identification and autonomy, but only within the group of participants who had visual choice available. Visual choice led to an increase in time spent, and indirectly led to increases in intrinsic motivation, immersion, time spent, future play motivation, and likelihood of game recommendation. Audial choice moderated the majority of these effects. Our results suggest that audial customization plays an important enhancing role vis-à-vis visual customization. However, audial customization appears to have a weaker effect compared to visual customization. We discuss the implications for avatar customization more generally across digital applications.

Synthesizing game levels that evoke players’ curiosity, driving them to explore different level parts, is time-consuming and tedious. Typically, game level designers manually perform this synthesis using trial and error. In this paper, we propose a method with which to replace this manual, time-consuming process. We benefited from recent work that had proposed game level design patterns to evoke curiosity, and we propose an approach to automatically synthesizing game levels in order to encourage players to pursue designer-specified exploration goals. We started by creating a dataset of level assets, based on the four design patterns that evoke curiosity-driven exploration in games (reaching extreme points, resolving visual obstructions, out-of-place objects, and understanding spatial connections). We annotated the assets in our dataset with spatial exploration measurements (the time players took to explore an asset over their total time spent in the game level). We then formulated game level design as an optimization problem, encoding both spatial exploration (mean spatial exploration, spatial exploration variance, and spatial exploration distribution) and game level design (occupied area, adjacent penalty, and height distribution) decisions. Then, we solved this problem by implementing a reversible-jump Markov chain Monte Carlo method. We demonstrate our method’s ability to synthesize game level variations with different spatial exploration and level design decisions. Finally, a user study showed that our approach can automatically synthesize game levels, encouraging a certain amount of spatial exploration by players.

Games are now ubiquitous, and educational games are becoming increasingly prevalent. Like other games, educational video games attract participants from different ethnicities and with different gender expressions. As such, educational game designers face a necessity to develop inclusive games. In this paper, we focus on inclusivity, diversity, and equity (DEI) issues by investigating if the computer programming game Mazzy benefited participants from broad demographic backgrounds. We highlight inclusive features present in Mazzy, and, focusing on the participants’ self-reported gender and race/ethnicity, reflect on their play experience and learning outcomes. We found evidence that the game supported learning outcomes and facilitated an engaging play experience for participants from diverse demographic backgrounds. We discuss challenges and implications for the broader literature.

Mastering thousands of logographic characters, such as the Chinese hanzi or Japanese kanji, is a unique and daunting obstacle for many students of those languages. In this paper, we investigate the efficacy of our component-focused hanzi learning game, Zen Hanzi, in addressing this issue. Zen Hanzi aims to assist Chinese as Foreign Language (CFL) learners in getting over some of the trickier aspects of hanzi, such as differentiating between similar-looking components. We describe our experimental game and provide a comparison study where 63 participants learned 10 complex hanzi using either our game or Quizlet, a flashcard app frequently used in Chinese courses. Results found that both groups had similar improvement on the hanzi recognition test, but the treatment group showed significantly better scores on the hanzi composition test (p<0.004). Our work extends prior findings on the benefits of component awareness to beginner hanzi learners, as well as contributes a scalable design for component-focused logographic learning tools.

Surveillance is ubiquitous. It is well known that the presence of other people (in-person or remote, actual or perceived) increases performance on simple tasks and decreases performance in complex tasks (Zajonc 1965). But little is known about these phenomena in the context of video games, with recent advances finding that they do not necessarily extend to games (Emmerich and Masuch 2018). In Experiment 1 (N=1489; No Observation vs. Researcher Observing), we find that participants observed by a researcher played significantly longer, and performed significantly better, across three video games. Moreover, we find some support that participants observed by a researcher score higher on player experience and intrinsic motivation. In Experiment 2 (N=843; Researcher Observing vs. Professor Observing), we seek to understand whether different roles differing in their perceived evaluativeness would influence the effects of observation. We find that participants observed by a professor had, at times, significantly lower performance, player experience, intrinsic motivation, playing time, and higher anxiety. In Experiment 3 (N=1358; No Observation vs. Researcher Observing), we further validate Experiment 1 by extending our results to three additional game genres. Here, we provide the largest study on observation in video games to date. The study is also the first to show that observer type can significantly influence player outcomes.

COVID-19 has upended lives everywhere, causing millions of deaths and tens of millions of infections worldwide. Nevertheless, for many people, staying in permanent isolation is neither desirable nor possible. To mitigate the spread of the disease, we iteratively developed a game that teaches hygienic best practices for preventing COVID-19. We consulted professional game designers, health experts, and educational technology designers. We then compared the effectiveness of the game to an equivalent video in two longitudinal experiments during the pandemic: 1) an experiment in a programming lab (N=11), and 2) an online-only experiment (N=475). In Experiment #1, we observe that participants in the game condition had higher intrinsic motivation, and a more sustained rise in hygienic self-efficacy, compared to participants in the video condition. Both conditions saw a rise in COVID-19 knowledge and positive hygienic attitude. Both conditions were relatively unchanged in COVID-19 anxiety and hygienic behavior. In Experiment #2, participants in the game condition experienced greater intrinsic motivation than participants in the video condition. Both conditions saw a sustained rise in COVID-19 hygienic self-efficacy, positive hygienic attitudes, and knowledge. Neither condition saw an effect on COVID-19 anxiety. Our work demonstrates that game-based learning can be an effective approach for teaching COVID-19 hygienic knowledge, for improving COVID-19 hygienic self-efficacy, and for fostering COVID-19 hygienic positive attitudes, and is more intrinsically motivating than video-based learning.

Intelligent tutoring systems (ITS) have shown to be useful learning aids for helping students learn STEM subjects. Previous studies on ITS tend to focus on developmental aspects of the system, such as system design, programming architecture, and dialogue moves. In this systemic literature review, we focus on pedagogical aspects of ITS within STEM domains. Specifically, we identified the implemented scaffolding approach and the grounding on learning theories of ITS implementations. Specific research questions were: (1) what types of knowledge (i.e., conceptual learning, problem-solving, and model building) are delivered via an ITS within STEM domains? (2) what pedagogies or scaffolding methods are used to guide the ITS learning experiences? (3) what are the characteristics of the research designs and specific learning outcomes when learning with the ITS? The steps followed for performing this systematic literature review were: (1) identifying the scope and research questions, (2) defining the inclusion and exclusion search criteria of literature, and (3) classifying and cataloging the literature sources that use ITS for STEM in classroom research. The final data set is comprised of a total of 22 papers that meet our criteria. We found a lack of fine-grained research on the effectiveness of using ITS to improve the three major learning modes: conceptual learning, problem-solving, and model building, particularly in STEM domains. In addition, we recommend that research conducted on ITS and other learning technology aids should emphasize the utilization of well-established learning theories and pedagogical scaffolding methods so that ITS will be more accessible to STEM educators for introducing ITS to their students to better learn STEM subjects.

This virtual reality study was conducted to assess the impact of the appearance of virtual characters on the avoidance movement behavior of participants. Five experimental conditions were examined. Under each condition, one of the five different virtual characters (classified as mannequin, human, cartoon, robot, and zombie) was studied. Each participant had to experience only one condition and was asked to perform the collision avoidance tasks two times. During the walking task, the motion of participants was recorded. After finishing the collision avoidance segment of the study, a questionnaire that examined different concepts (emotional reactivity, emotional contagion, attentional allocation, behavioral independence, perceived skill, presence, immersion, virtual character realism, and virtual character unpleasantness) was distributed to the participants. Based on the collected measurements (avoidance movement behavior and self-reported ratings), we tried to understand the effects of the appearance of a virtual character on the avoidance movement behavior, and its possible correlation to subjective ratings. The results obtained from this study indicated that the appearance of the virtual characters did affect the avoidance movement behavior and also some of the examined concepts. Additionally, participant avoidance movement behavior correlates with some subjective ratings.

Abstract This between‐group study investigated participants' experiences of tactile feedback patterns when asked to hug a virtual character. Five experimental conditions were developed, one with no tactile feedback and four with tactile feedback. The participants were placed in a virtual city and informed they would be meeting a virtual friend, who they were instructed to hug once the character came close to them. During the virtual hug, one of the five experimental conditions was examined. Immediately after the hug, participants were asked to complete a questionnaire to capture their experiences. The results obtained from this study indicated that: (1) even if the tactile feedback is not considered to be highly accurate in terms of timing, duration, and position, as long as it is perceived as less persistent, it provides a more positive experience; (2) the perceived realism of the virtual hug is strongly correlated with the perceived realism of the tactile feedback; and (3) the female participants had a more intense interaction with the virtual character (friend) compared with the male participants. Limitations and future study directions are discussed.

Virtual reality (VR) has disrupted the gaming market and is rapidly becoming ubiquitous. Yet differences between VR and traditional mediums, such as controllers that are visible in the virtual world, enable entirely new approaches to instruction. In this paper, we present four studies, each using a different VR game. Within each study, we compared three different modalities of tutorials: Text (text-only), Text+Diagram (text with controller diagrams), and Text+Spatial (text with controller tooltips appearing on top of the player's virtual controllers). Data from our studies show that the importance of tutorial modality depends greatly on game type. In a third-person shooter, Text+Spatial led to significantly higher controls learnability than Text and Text+Diagram, and also led to significantly higher performance, player experience, and intrinsic motivation than Text. In a puzzle game, Text+Spatial led to significantly higher controls learnability and performance than Text. Additionally, Text+Diagram led to significantly higher controls learnability than Text. However, in a wave shooter and a rhythm game, differences between conditions were negligible on all measures. Our studies show that game type is an important factor to consider when designing tutorial modality.

Avatar identification is one of the most promising research areas in games user research. Greater identification with one's avatar has been associated with improved outcomes in the domains of health, entertainment, and education. However, existing studies have focused almost exclusively on the visual appearance of avatars. Yet audio is known to influence immersion/presence, performance, and physiological responses. We perform one of the first studies to date on avatar self-similar audio. We conducted a 2 x 3 (similar/dissimilar x modulation upwards/downwards/none) study in a Java programming game. We find that voice similarity leads to a significant increase in performance, time spent, similarity identification, competence, relatedness, and immersion. Similarity identification acts as a significant mediator variable between voice similarity and all measured outcomes. Our study demonstrates the importance of avatar audio and has implications for avatar design more generally across digital applications.

Help facilities have been crucial in helping users learn about software for decades. But despite widespread prevalence of game engines and game editors that ship with many of today’s most popular games, there is a lack of empirical evidence on how help facilities impact game-making. For instance, certain types of help facilities may help users more than others. To better understand help facilities, we created game-making software that allowed us to systematically vary the type of help available. We then ran a study of 1646 participants that compared six help facility conditions: 1) Text Help, 2) Interactive Help, 3) Intelligent Agent Help, 4) Video Help, 5) All Help, and 6) No Help. Each participant created their own first-person shooter game level using our game-making software with a randomly assigned help facility condition. Results indicate that Interactive Help has a greater positive impact on time spent, controls learnability, learning motivation, total editor activity, and game level quality. Video Help is a close second across these same measures.

We have developed a modular virtual reality gaming application that can be used to synthesize exercise drills for racket sports. By defining cost terms that are related to the gameplay and the mechanics of the game, as well as by allowing a user to control the parameters of the cost terms, users can easily adjust the objectives and the intensity levels of the exercise drills. Based on the user-defined exercise objectives, a Markov chain Monte Carlo optimization method called “simulated annealing” was used to optimize the exercise drill. The effectiveness of the developed virtual reality gaming application was measured in two studies by using virtual reality table tennis as the evaluation tool. The first study investigated the potential usefulness of the developed virtual reality gaming application as an exercise tool by comparing its workout effectiveness at three intensity levels (low, medium, and high) through the collection of heart rate readings. The second study explored the potential utility of the virtual reality gaming application as a training tool by exploring whether there was any improvement in participants' performance across the three conditions (no training, virtual reality training, and real-world training). The results indicate that a virtual reality gaming application, such as the examined virtual reality table tennis exergame, could indeed be used effectively as both an exercise and a training tool. Limitations and future research directions are discussed further below.

This paper examines a common problem found in a number of virtual reality setups—mismatches between real and virtual environments. Specifically, this paper investigates whether the mismatching between a real and a virtual environment in terms of appearance and physical constraints can affect the arousal (electrodermal activity) and movement behavior in the participants. For this study, one baseline condition and four mismatch conditions that examine different mismatching types were developed and tested in a between-group study design. The participants were immersed in a virtual environment and were asked to walk in a direction given to them along a provided path. During that time, electrodermal activity and the walking motion of participants were captured to assess potential alterations in their arousal and movement behavior respectively. Results obtained from this study indicate significant differences in the electrodermal activity and movement behavior of participants, especially when walking in a virtual environment that is mismatched both in appearance and physical constraints. Even though to a lesser degree, evidence was also found that correlates electrodermal activity with movement behavior. Limitations and future research directions are discussed.

Loot boxes are garnering increased attention in both the industry and media. One focal point of the discussion is whether loot boxes should be considered a form of gambling. While parallels can be drawn between loot boxes and random reward schedules, researchers have argued that the “glorification” aspect of loot boxes that have heightened player awareness (e.g., opening a box, a pack of cards, or spinning a wheel) of randomness is a relatively new trend in games. However, there is currently a dearth of empirical research on loot boxes. We make two contributions in this paper: 1) Infinite Loot Box, an open-source Unity platform for experimenting with loot boxes created from scratch; and 2) a 2 × 2 experiment (high/low visual effects × high/low audial effects; N = 1235). We find that high audial effects significantly increase the number of loot boxes opened. Neither audial nor visual effects were found to significantly impact other variables. These contributions push forward our understanding of loot boxes and their contextual factors.

In this paper, we describe JavaStrike1. JavaStrike is a Java development and execution environment that was developed from scratch inside Unity. The engine currently supports classes, functions, inheritance, polymorphism, interfaces, key-value stores, and much more. JavaStrike allows code to be displayed, executed, and debugged in the virtual world. We then create a third-person shooter game called CodeBreakers, which leverages the JavaStrike engine. CodeBreakers covers basic programming concepts such as variable types, intermediate programming concepts such as stacks, queues, and hashmaps, and advanced programming concepts such as inheritance, interfaces, and method overriding. JavaStrike is a first step towards general purpose programming engines embedded in virtual worlds.

Avatar identification is a topic of increasingly intense interest. This is largely because avatar identification can promote a wide variety of outcomes: game enjoyment, intrinsic motivation, quality of made artifacts, and more. Yet we still understand very little about how different avatar types affect users. Here, we contribute one of the few highly controlled studies of this nature (N=1074). Specifically, we compare three avatar types in a jumping game: 1) Human (high anthropomorphism), 2) Block-like (low anthropomorphism), and 3) Robot (high anthropomorphism). We find that players randomly assigned to the Robot condition have significantly higher player experience. We find that both Robot and Human conditions lead to higher avatar identification. Finally, using linear hierarchical regression, we find that avatar identification significantly promotes player experience (29.8% variance) and time played (3.5% variance). Our study demonstrates the importance of considering avatar type in designing virtual systems.

Time of day effects have been observed for the last five decades in cognitive tasks, athletic performance, and even ethical behavior. However, in the context of games, little is known about how time of day influences preference or performance. We present a first study (N=504) to explore how preference and perceived performance vary over the course of the day, by game genre. We find that the genres First-Person Shooter and Other RPG are more popular at 6 p.m. to midnight. Conversely, the genres Puzzle and Board/Card were less popular at 6 p.m. to midnight. However, 6 a.m. to noon is a more popular time for Puzzle and Board / Card. Performance-wise, players feel they are more successful in First-Person Shooter games from 6 p.m. to midnight, and less successful at all other times. On the other hand, players feel they are more successful in Puzzle games from 6 a.m. to noon, and less successful from 6 p.m. to midnight. These inter-genre differences have a basis in the literature, which has postulated that cognitive function gradually declines throughout the day, but that athletic performance peaks in the evening along with core body temperature.

In our study (N=2189), we divided participants into 6 badge conditions: 1) Role model badges (e.g., Einstein), 2) Personal interest badges (e.g., Movies), 3) Achievement badges (e.g., "Code King"), 4) Choice, 5) Choice with badges always visible, and 6) No badges. Participants played a CS programming game, then used an editor to create their own level. Badges promoted avatar identification (personal interest, role model), player experience (achievement, role model), intrinsic motivation (achievement, role model), and self-efficacy (role model) during both the game and the editor. Independent of badges, avatar identification promoted player experience, intrinsic motivation, and self-efficacy. Additionally, avatar identification promoted greater overall time spent in both the game and the editor, and led to significantly higher overall quality of the completed game levels (as rated by 3 independent externally trained QA testers). Our study has implications for the design of badge systems and sheds new light on the effects of avatar identification on play and making.

This paper presents an overview of the MazeStar platform for Computer Science education. MazeStar is both a game (Mazzy) that teaches programming concepts like loops and conditionals, and a game editor which allows players to create and share their own game levels. By playing and creating, players are using computing concepts (e.g., block structuring, parallelism, etc.) and computing practices (e.g., debugging, iterative prototyping, etc.). To date the MazeStar platform has been used in controlled user studies involving > 10,000 participants. Here, our goal is to detail the different components of the MazeStar platform, and how we have/are leveraging these components to study the interplay of education, games/game-making, and virtual identity.