Joe A. Wasserman and Christine E. Rittenour
Abstract: Male/female-based stereotypes appear to be widespread, providing a potential barrier to women's participation and success in gaming contexts, such as recreational gaming, competitive eSports, and game-based learning. Differences in the strength of stereotypes associated with different kinds of games, which would have im- plications for reducing these barriers, are currently unknown. In an online between-participants experiment manipulating the platform (analog tabletop, digital tablet computer, digital desktop computer) of the game Splendor, 105 participants responded to questions asking them to separately rate their perceptions of men's and women's affinity for the game. Confirming extant research on gaming stereotypes, they perceived women as having less of an affinity for this game. While this trend emerged similarly between all platforms of the game depicted, the magnitude of this difference was less when participants had a stronger social group identification with gamers. These perceptions did not depend on social group identification with women. Given the potential for stereotypes to discourage women from gaming and threaten their performance and learning in gaming contexts, as well as the prominent and persistent public interest in gaming, we suggest researchers further examine stereotypes and identity in the study of diverse games, game platforms, and powerful perceptions.
Joe A. Wasserman and Kevin Koban
Abstract: This study investigated the development of player skill and cognitive understanding of a game over repeated plays to (a) bridge separate research traditions on skill acquisition and games learning and (b) provide deeper insight into the process of developing mental models of games. 325 participants responded to an online questionnaire with questions concerning their experience with the game, Hive, as well as both open- and closed-ended items designed to compare their understanding of the game to an expert’s understanding. Open-ended items were content analyzed and modeled as a latent variable. As predicted, both player skill and mental model matching were positively associated with number of plays. Additionally, while player skill had a curvilinear relationship with number of plays that indicated diminishing returns on additional plays, that between cognitive understanding and plays appeared to be linear. The implications of these findings for the cognitive underpinnings of player skill—and for mental model matching theory in particular—are discussed. Supplemental online material is provided here: https://osf.io/3yeg2/
Joe A. Wasserman and Jaime Banks
Background. Although the effectiveness of game-based learning (GBL) is well- supported, much less is known about the process underlying it. Nevertheless, developing a mental model that matches the game system, which in turn models a real-world system, is a promising proposed process.
Aim. This article explores the first steps in model matching: identifying the entities and (complex) relations in a game system.
Method. Participants (N = 30) played the analog game DOMINION and completed a multi-step mental model mapping exercise. Categories of entities in mental model maps were inductively identified with grounded theory coding, while complex relations in mental model maps were identified via content analysis.
Results. Participants described formal game entities, player actions, sociality, learning processes, and subjective experience in their mental model maps. Participants identified very few complex relations—and no feedback loops—in their mental model maps.
Conclusions. Games—and analog games specifically—provide a breadth of resources for model matching and GBL. Through gameplay, learners come to affix conceptual meanings to material objects, a process dubbed lamination.
Supplement. Complex Relations Codebook
Rory McGloin, Joe A. Wasserman, and Andy Boyan
Abstract: The primary aim of this article is to provide a comprehensive review and elaboration of model matching and its theoretical propositions. Model matching explains and predicts individuals’ outcomes related to gameplay by focusing on the interrelationships among games’ systems of mechanics, relevant situations external to the game, and players’ mental models. Formalizing model matching theory in this way provides researchers a unified explanation for game-based learning, game performance, and related gameplay outcomes while also providing a theory-based direction for advancing the study of games more broadly. The propositions explicated in this article are intended to serve as the primary tenets of model matching theory. Considerations for how these propositions may be tested in future games studies research are discussed.
As part of a larger essay on combining classic media theory (uses & gratifications) with more recent methods from complexity science (agent based models), I created this simulation in which agents (squares) assess their current need for social interaction and choose to either (a) consume media independently or (b) interact with their neighbors. Behavior and emergent outcomes depend on tunable model parameters. Skip the essay and play with the agent based model here.
I was curious about whether and how much US presidential election voting dynamics could potentially be explained just by partisan voters reacting to the results of the previous election cycle, so I made this simulation. In this model, state-by-state partisan voter participation (a) increases after losing a presidential election and (b) decreases after winning. And these dynamics can reproduce historical outcomes! So the answer is: maybe? Play with the agent based model here.
Games & Learning Syllabus
The syllabus for my Games & Learning class (Spring 2018), a combined upper-level undergraduate/graduate course with a boardgame design final project.