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1. Modeling Player Engagement with Bayesian Hierarchical Models

   Sawyer, R.; Rowe, J.; Azevedo, R.; Lester, J. (January 2018, Proceedings of the Fourteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment)

   Modeling player engagement is a key challenge in games. However, the gameplay signatures of engaged players can be highly context-sensitive, varying based on where the game is used or what population of players is using it. Traditionally, models of player engagement are investigated in a particular context, and it is unclear how effectively these models generalize to other settings and populations. In this work, we investigate a Bayesian hierarchical linear model for multi-task learning to devise a model of player engagement from a pair of datasets that were gathered in two complementary contexts: a Classroom Study with middle school students and a Laboratory Study with undergraduate students. Both groups of players used similar versions of Crystal Island, an educational interactive narrative game for science learning. Results indicate that the Bayesian hierarchical model outperforms both pooled and context-specific models in cross-validation measures of predicting player motivation from in-game behaviors, particularly for the smaller Classroom Study group. Further, we find that the posterior distributions of model parameters indicate that the coefficient for a measure of gameplay performance significantly differs between groups. Drawing upon their capacity to share information across groups, hierarchical Bayesian methods provide an effective approach for modeling player engagement with data from similar, but different, contexts. 

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

   https://doi.org/10.1145/3549482  

   Villareale, Jennifer; Harteveld, Casper; Zhu, Jichen (October 2022, Proceedings of the ACM on Human-Computer Interaction)

   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. 

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3. In-Game Actions to Promote Game-Based Math Learning Engagement

   https://doi.org/10.1177/0735633119878611  

   Moon, Jewoong; Ke, Fengfeng (September 2019, Journal of Educational Computing Research)

   Game-based learning (GBL) has increasingly been used to promote students’ learning engagement. Although prior GBL studies have highlighted the significance of learning engagement as a mediator of students’ meaningful learning, the existing accounts failed to capture specific evidence of how exactly students’ in-game actions in GBL enhance learning engagement. Hence, this mixed-method study was designed to examine whether middle school students’ in-game actions are likely to promote certain types of learning engagement (i.e., content and cognitive engagement). This study used and examined the game E-Rebuild, a single-player three-dimensional architecture game that requires learners’ application of math knowledge. Using in-depth gameplay behavior analysis, this study sampled a total of 92 screen-recorded and video-captured gameplay sessions attended by 25 middle school students. We adopted two analytic approaches: sequential analysis and thematic analysis. Whereas sequential analysis explored which in-game actions by students were likely to promote each type of learning engagement, the thematic analysis depicted how certain gameplay contexts contributed to students’ enhanced learning engagement. The study found that refugee allocation and material trading actions promoted students’ content engagement, whereas using in-game building tools and learning support boosted their cognitive engagement. This study also found that students’ learning engagement was associated with their development of mathematical thinking in a GBL context. 

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4. A Systematic Review of Data Collection and Analysis Methods in K–12 Educational Games

   https://doi.org/10.3102/2110403  

   Ma, Tianyu; Kahn, Jennifer; Hardy, Lisa; Radke, Sarah (April 2024, AERA Online Paper Repository)

   This paper reports on systematic literature review that examined learning theories and data collection and analysis methods used to study game-based learning in research on educational digital games for K-12 populations. Through electronic database, hand, and ancestral searches, we identified 25 empirical studies (29 educational games) published in peer-review journals that report evidence of how students learn through in-game and out-of-game data collection and analysis methods. Taking an approach to game-based learning as identity-driven and situated, we found that while games do not take such an approach to game-based learning, games tend to collect data on players’ social interactions and collaborative experiences. The review also highlighted the opportunity for providing real-time feedback and data to players during gameplay. 

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5. Towards Accessible Sports Broadcasts for Blind and Low-Vision Viewers

   https://doi.org/10.1145/3544549.3585610  

   Jain, Gaurav; Hindi, Basel; Courtien, Connor; Wyrick, Conrad; Xu, Xin Yi; Malcolm, Michael C; Smith, Brian A. (April 2023, Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems)

   Blind and low-vision (BLV) people watch sports through radio broadcasts that offer a play-by-play description of the game. However, recent trends show a decline in the availability and quality of radio broadcasts due to the rise of video streaming platforms on the internet and the cost of hiring professional announcers. As a result, sports broadcasts have now become even more inaccessible to BLV people. In this work, we present Immersive A/V, a technique for making sports broadcasts —in our case, tennis broadcasts— accessible and immersive to BLV viewers by automatically extracting gameplay information and conveying it through an added layer of spatialized audio cues. Immersive A/V conveys players’ positions and actions as detected by computer vision-based video analysis, allowing BLV viewers to visualize the action. We designed Immersive A/V based on results from a formative study with BLV participants. We conclude by outlining our plans for evaluating Immersive A/V and the future implications of this research. 

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