Ben Medler

Intelligent adaptation of digital game-based learning

Games for learning cannot take the same design approach as games when targeting audiences. While players of entertainment games have the luxury of choosing games that suit them, students using digital games for learning typically have a single game for them to learn from, regardless of whether or not it fits their playing style or learning needs. We contend that this problem can be addressed by creating games that identify the kind of player-learner using the game and adapts itself to best fit that individual. These adaptive games can specialize themselves according to a students learning needs, gameplay preferences, and learning style. We present a prototype mini-game, called S.C.R.U.B., which employs this method for teaching microbiology concepts.

Impacts of Forced Serious Game Play on Vulnerable Subgroups

Three vulnerable subgroups of players non-gamers, resistant players, and females were studied to understand how each approaches and plays serious games. The authors explore forced required play using four different online casual games. Their research strongly suggests that the most important threat to a serious games impact is when players dislike the game. Serious games are less effective for players who dislike a game and most effective for those who like the game. Non-gamers were at a distinct disadvantage as far as gameplay performance. They experienced a more negative effect in two of the four games. Finally, males tended to seek more difficult challenges in games than females. The optimal amount of challenge may be the most important gender difference to consider when designing serious games.

Beyond player types: gaming achievement goal

Education and psychology studies have used motivational constructs called achievement goals to predict learning success and response to failure. In this article we adapted classroom achievement goal scales to instead measure gaming achievement goals. We collected survey data from 432 university students to empirically examine the applicability and utility of achievement goal constructs from education research to gaming. We introduced a new approach to player types based on mastery and performance gaming achievement goals. Four types are studied: super-achievers, mastery-only, performance-only, and non-achievers. We also examined the relationship between our four achievement goal player types to the traditional achiever, explorer player types. We found that Interest in exploration in games can exist in any of the four types, but those with strong mastery goals have the lowest interest in exploration. Gender and gaming frequency were significantly related to gaming achievement goals. The implications and suggestions for designing games for learning and entertainment are discussed.

The implications of improvisational acting and role-playing on design methodologies

For decades designers have used theatre metaphors to describe design methodologies and have used performance techniques to enhance the design process, two of which are improvisational acting and role-playing. Unfortunately, most design literature does not differentiate between these two practices even while using them in combination with various design methods. This paper discusses how improvisation and role-playing have been employed during the design process and why they are distinct from one another. The authors draw upon their current research involving improvisational acting and compare it with other role-playing research which examines role-playing from both a serious and entertainment angle. They conclude through this comparison that both performance techniques have their place in the design process and that more informed definitions of each technique can aid designers in deciding which techniques properties will benefit them the most.

Using Recommendation Systems to Adapt Gameplay

Recommendation systems are key components in many Web applications (Amazon, Netflix, eHarmony). Each system gathers user input, such as the products they buy, and searches for patterns in order to determine user preferences and tastes. These preferences are then used to recommend other content that a user may enjoy. Games on the other hand are often designed with a one-size-fits-all approach not taking player preferences into account. However there is a growing interest in both the games industry and game research communities to begin incorporating systems that can adapt, or alter how the game functions, to specific players. This article examines how Web application recommendation systems compare to current games that adapt their gameplay to specific players. The comparison shows that current games do not use recommendation methods that are data intensive or collaborative when adapting to players. Design suggestions are offered within this manuscript for how game developers can benefit from incorporating the lesser used recommendation methods.

Game Design and the Challenge-Avoiding, Self-Validator Player Type

Achiever and Explorer player types are well known in MMOs and educational games. Players who enjoy being a winner, but dislike hard challenges (“Self-Validators”) are a heretofore ignored but commonly occurring player type. Self-Validators worry about and are distressed by failing. They can simply avoid playing overly difficult games for entertainment. But in a required learning game, Self-Validators’ excessive worry about failing can interfere with learning. The authors consider whether and how eight very different modern games accommodate Explorers, Achievers, and Self-Validators and discuss implications for entertainment and learning game design and research. Seven of eight diverse games analyzed primarily served either the Explorer or Achiever player type. Self-Validators were partially accommodated in some Achiever-oriented games, through user-selectable difficulty. Design with all three types in mind would encourage inclusion of features that enable players to optimize their preferred style of play. [Article copies are available for purchase from InfoSci-on-Demand.com]

Using conflict theory to model complex societal interactions

Conflict theory can be used to explain the interactions between societies during times of turmoil and change (i.e. revolutions, strikes or everyday debates). Games have been produced that make use of different aspects of conflict theory; however a common framework for organizing a system to produce realistic conflicts has not been created. This paper presents one such framework, based upon principles of conflict theory, which describe a generalized way of organizing a system to produce realistic conflict situations among societies. With our framework, we present how current commercial games represent conflict and how our framework can be implemented by these games in order to increase the systems flexibility and accuracy in representing conflict.

Views from atop the fence: neutrality in games

Games are play with conflict. However, players rarely get the chance to explore gameplay besides open conflict. Neutrality in the real world allows actors to avoid conflict and is also used to describe how mediators should act when they negotiate a conflict resolution. Reviewing different definitions of what it means to be neutral this paper investigates how game mechanics that simulate neutrality act as neutral mediators between players. The neutrality of each of the seven game mechanics discussed is related to how impartial they act towards players. This paper concludes that current games have not explored all of the possible neutral mechanics and suggests ways for game developers to incorporate these missing mechanics into games.

Feeling right about how you play: The effects of regulatory fit in games for learning

Serious games are often assigned to learners and not played voluntarily. A problem for educators is how to motivate learners in these assigned conditions. This study examines the influence of regulatory fit experience on player motivations and time spent on learning aspects of the game. Regulatory fit theory posits that when instructions match the learners’ promotion or prevention motivational systems, learners will experience “regulatory fit,” which will make them “feel right” about the current instructions and tasks. Our findings support the regulatory fit theory. When learners experienced regulatory fit, they played the game for 26% longer time than learners who did not experience regulatory fit. Learners in regulatory fit conditions also displayed more learning-related behaviors such as spending more time on learning feedback both during gameplay and between gameplay sessions. Positive feedback seems to motivate promotion-oriented learners; however, negative feedback did not demotivate prevention-oriented learners as theory predicted.

Conceptually Meaningful Metrics: Inferring Optimal Challenge and Mindset from Gameplay

In this chapter we describe our experience with constructing conceptually meaningful gameplay metrics that are helpful in exploring which players are likely to receive the intended benefits from playing serious games. We discuss takeaways and lessons learned from two case studies in which the authors sought to define, construct, and apply conceptually meaningful game metrics.