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1. The role of representational competencies for students’ learning from an educational video game for astronomy

   https://doi.org/10.3389/feduc.2022.919645  

   Herder, Tiffany; Rau, Martina A. (November 2022, Frontiers in Education)

   Educational video games can engage students in authentic STEM practices, which often involve visual representations. In particular, because most interactions within video games are mediated through visual representations, video games provide opportunities for students to experience disciplinary practices with visual representations. Prior research on learning with visual representations in non-game contexts suggests that visual representations may confuse students if they lack prerequisite representational-competencies. However, it is unclear how this research applies to game environments. To address this gap, we investigated the role of representational-competencies for students’ learning from video games. We first conducted a single-case study of a high-performing undergraduate student playing an astronomy game as an assignment in an astronomy course. We found that this student had difficulties making sense of the visual representations in the game. We interpret these difficulties as indicating a lack of representational-competencies. Further, these difficulties seemed to lead to the student’s inability to relate the game experiences to the content covered in his astronomy course. A second study investigated whether interventions that have proven successful in structured learning environments to support representational-competencies would enhance students’ learning from visual representations in the video game. We randomly assigned 45 students enrolled in an undergraduate course to two conditions. Students either received representational-competency support while playing the astronomy game or they did not receive this support. Results showed no effects of representational-competency supports. This suggests that instructional designs that are effective for representational-competency supports in structured learning environments may not be effective for educational video games. We discuss implications for future research, for designers of educational games, and for educators. 

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2. A Bayesian Analysis of Adolescent STEM Interest Using Minecraft

   Gadbury, Matt; Lane, H Chad (June 2023, Springer)

   Wang, N; Rebolledo-Mendez, G; Dimitrova, V; Matsuda, N; Santos, O C (Ed.)

   Minecraft continues to be a popular digital game throughout the world, and the ways in which adolescents play can provide insight into their existing interests. Through informal summer camps using Minecraft to expose middle school students to concepts in astronomy and earth science, we collected self-reports of STEM and Minecraft interest, as well as behavioral log data through player in-game interactions. Finding relationships between in-game behaviors and individual interest can provide insight into how educational experiences in digital games might be designed to support learner interests and competencies in STEM. Bayesian model averaging of data across camps was implemented to address the relatively small sample size of the data. Results revealed the important role of existing interest and knowledge for developing and sustaining interest. 

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3. Individual differences in cooperative and competitive play strategies

   https://doi.org/10.1371/journal.pone.0293583  

   Hauge, Theresa C; Ferris, Daniel P; Seidler, Rachael D (November 2023, PLOS ONE)

   Barreda-Tarrazona, Iván (Ed.)

   Cooperation and competition are common in social interactions. It is not clear how individual differences in personality may predict performance strategies under these two contexts. We evaluated whether instructions to play cooperatively and competitively would differentially affect dyads playing a Pong video game. We hypothesized that instructions to play cooperatively would result in lower overall points scored and differences in paddle control kinematics relative to when participants were instructed to play competitively. We also predicted that higher scores in prosociality and Sportspersonship would be related to better performance during cooperative than competitive conditions. Methods: Pairs of participants played a Pong video game under cooperative and competitive instructions. During competitive trials, participants were instructed to score more points against one another to win the game. During the cooperative trials, participants were instructed to work together to score as few points against one another as possible. After game play, each participant completed surveys so we could measure their trait prosociality and Sportspersonship.Results: Condition was a significant predictor of where along the paddle participants hit the ball, which controlled ball exit angles. Specifically, during cooperation participants concentrated ball contacts on the paddle towards the center to produce more consistent rebound angles. We found a significant correlation of Sex and the average points scored by participants during cooperative games, competitive games, and across all trials. Sex was also significantly correlated with paddle kinematics during cooperative games. The overall scores on the prosociality and Sportspersonship surveys were not significantly correlated with the performance outcomes in cooperative and competitive games. The dimension of prosociality assessing empathic concern was significantly correlated with performance outcomes during cooperative video game play. Discussion: No Sportspersonship survey score was able to predict cooperative or competitive game performance, suggesting that Sportspersonship personality assessments are not reliable predictors of cooperative or competitive behaviors translated to a virtual game setting. Survey items and dimensions probing broader empathic concern may be more effective predictors of cooperative and competitive performance during interactive video game play. Further testing is encouraged to assess the efficacy of prosocial personality traits as predictors of cooperative and competitive video game behavior. 

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4. Adaptive Agent Architecture for Real-time Human-Agent Teaming

   Ni, T. (January 2021, AAAI Workshop on Plan, Activity, and Intent Recognition)

   Teamwork is a set of interrelated reasoning, actions and behaviors of team members that facilitate common objectives. Teamwork theory and experiments have resulted in a set of states and processes for team effectiveness in both human-human and agent-agent teams. However, human-agent teaming is less well studied because it is so new and involves asymmetry in policy and intent not present in human teams. To optimize team performance in human-agent teaming, it is critical that agents infer human intent and adapt their polices for smooth coordination. Most literature in human-agent teaming builds agents referencing a learned human model. Though these agents are guaranteed to perform well with the learned model, they lay heavy assumptions on human policy such as optimality and consistency, which is unlikely in many real-world scenarios. In this paper, we propose a novel adaptive agent architecture in human-model-free setting on a two-player cooperative game, namely Team Space Fortress (TSF). Previous human-human team research have shown complementary policies in TSF game and diversity in human players’ skill, which encourages us to relax the assumptions on human policy. Therefore, we discard learning human models from human data, and instead use an adaptation strategy on a pre-trained library of exemplar policies composed of RL algorithms or rule-based methods with minimal assumptions of human behavior. The adaptation strategy relies on a novel similarity metric to infer human policy and then selects the most complementary policy in our library to maximize the team performance. The adaptive agent architecture can be deployed in real-time and generalize to any off-the-shelf static agents. We conducted human-agent experiments to evaluate the proposed adaptive agent framework, and demonstrated the suboptimality, diversity, and adaptability of human policies in human-agent teams. 

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5. Black Women Getting a Seat at the Table in the Video Game Industry

   https://doi.org/10.18848/2328-6261/CGP/v24i02/65-83  

   Miles, Tianna; Herman, Bernease; Aragon, Cecilia; Evans, Sarah (July 2024, The International Journal of Organizational Diversity)

   Historically, the African American female population has been underrepresented within the STEM workforce, creating non-inclusive environments. The metaphor of getting a seat at the table reflects the idea of inclusivity where Black women’s opinions are valued, and they can make decisions and create change within the workplace. Intersectionality poses several unique challenges for Black women pursuing careers in the video games industry. We motivate further research on this group through existing literature and insights from running a research group on the group design of human-centered data science games in a collaborative university setting. We take an autoethnographic perspective on the topic, with the first and second authors grounding their findings in their own experiences as Black women in STEM and higher education and with video game development studios. We outline tangible actions toward the recruitment, development, and retention of Black women in the video games industry in the future. 

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