Learning Mechanics and Assessment Mechanics
for Games for Learning
Institute for Games for Learning
New York University
The Graduate Center of the City University of New York Teachers College Columbia University
White Paper # 01/2011
Version 0.1 September 30, 20011
Jan L. Plass, NYU
Bruce D. Homer, CUNY GC Charles Kinzer, TC/Columbia Jonathan M. Frye, NYU Ken Perlin, NYU
Learning Mechanics and Assessment Mechanics
for Games for Learning
In this paper, we will discuss the approach for assessment of learning and related learner variables taken by the Games for Learning Institute (G4LI). We will first describe game mechanics in general, and then introduce the concepts of learning mechanics and
assessment mechanics and describe criteria for their design and requirements of how they can inform the design of related game mechanics.
Game Mechanics
We begin with a discussion of the concept of game mechanics, a term that is central to an understanding of games. Game studies scholars and developers have offered definitions for this term that range from finely detailed to more broad and conceptual approaches. In general terms, the mechanics of the game consist of what the game allows players to do – they are the rule systems that players must abide by and therefore shape the interactions and the behaviors of players. Game mechanics are “the various actions, behaviors and control
mechanisms afforded to the player within a game context” (Hunicke, LeBlanc, & Zubek, 2004, p. 3). In a narrower sense, the core mechanic of a game is “the essential play activity players perform again and again and again” (Salen & Zimmerman, 2003, p. 316). In other words, the core mechanic of the game contains the moment-to-moment actions and interactions in which the player is engaging while playing the game. A definition proposed by Cook (2006) describes game mechanics as “rule based system/simulations that facilitate and encourage a user to explore and learn the properties of their possibility space through the use of
feedback mechanisms” (p. 1). Game mechanics are therefore a means to guide the player into particular behaviors by constraining the space of possible plans to attain goals (Järvinen, 2008). For instance, mechanics involve reward systems such as points or stars are feedback mechanisms that shape behaviors and interactions that the player performs. The same is
true for failure mechanics that are a way for the designer to playfully communicate what actions the player should and should perform.
This paper focuses on games for learning, yet it is clear that learning is a fundamental part of all games (Gee, 2008). At a minimum, players must learn the basics of a game’s
mechanics in order to play. Additionally, players must uncover the goals and purpose of these mechanics; and what actions the game designer was aiming to facilitate for the player (Cook, 2006). Feedback mechanisms are an example of how game designers encourage (reward) or discourage (punish) a particular action taken by the player. Game mechanics for learning must incorporate all of these aspects, from the moment-to-moment activities in which players engage to meaningful incentive systems.
The mechanics of the game therefore not only define the behaviors and actions players take, but directly facilitate players’ understanding of the game and what the game may be representing or aiming to teach. A good example of this is what Bogost (2008) refers to as procedural rhetoric;
“Following the contemporary model, procedural rhetoric entails expression—to convey ideas effectively. Procedural rhetoric is a subdomain of procedural authorship; its arguments are made not through the construction of words or images, but through the authorship of rules of behavior, the construction of dynamic models.” (p. 125).
In other words, the rules or mechanics of the game can be representative of arguments or models. Through the representations and rules of the game system, players can form mental models or understandings of similar real world systems, concepts, or formulas. Players/learners can gain deeper insights by not only learning the variables involved in a system, but also by understanding how these variables interact with each other.
Learning Mechanics
When games are designed with the explicit goal of facilitating learning, game mechanics must go beyond making a game fun and engaging–they must engage players in meaningful
learning activities. The game mechanic becomes an integral part of the learning activity. Game designers have long seen this connection and have argued that new mechanics are needed in order to engage the player in a way that facilitates learning (Isbister, Flanagan, & Hash,
2010). Most importantly, the designers interviewed by Isbister et al. made a strong case that learning needs to be embedded in the core mechanics of a game rather than added on to existing mechanics. Game play cannot be used as a reward for answering questions about facts; and vice versa, factual quizzes cannot be forced into unrelated game play.
In order to emphasize this qualitative difference of mechanics that are designed for learning, we offer the following definition of what we call Learning Mechanics, based on Salen & Zimmermann’s (2004) definition of game mechanics:
Learning mechanics are patterns of behavior or building blocks of learner interactivity, which may be a single action or a set of interrelated actions that form the essential learning activity that is repeated throughout a game.
The relationship between game mechanics and learning mechanics is that learning mechanics are design patterns or meta-mechanics that need to be instantiated in order to become a game mechanic. We use the term design pattern in the tradition of Christopher Alexander’s definition as general solutions to commonly occurring problems (Alexander, 1977). Learning mechanics adapt the moment-to-moment activity of a game mechanic into a meaningful learning activity. The learning aspects of a game are integrated in a way that they become an integral part of the game play and not merely an addendum to the game
mechanic. An example of a poor integration of learning into a game is when a learning game uses an established game mechanic such as a racing mechanic or a shooter mechanic, and the learning mechanic consists of a popup question that players are asked to answer before they can resume the race or shooter activities.
Player choice can play a significant role in learning mechanics. After all, the relationship between the player’s agency and the game’s rule systems are what make the game fun and challenging. All games offer players series of choices and then react to those choices with new challenges. Learning mechanics can push game mechanics further to offer player choices that help the student learn as well as facilitate gameplay.
Let’s review an example of a learning mechanic to illustrate this concept. Figure 1 shows a learning mechanic from a game we developed as part of our research at G4LI, entitled Noobs v. Leets. The goal of the game is to teach middle school geometry, in particular, rules that describe relations of angles in quadrilaterals, as included in the common core math standards for grades 6–8. These rules include the complementary angle rule, supplementary angle rule, opposite angle rule, as well as the sum of angles in a triangle rule.
Figure 1. Learning Mechanic: Apply Rules to solve Problems
The learning mechanic for this game is Apply Rules to solve Problems: Learner selects among different rules and indicates for which problems they apply. This mechanic was chosen to engage the learner in higher-level thinking: rather than solving angles and
responding with the numeric answer of the missing angle, we wanted the focus of the learning activity to be on the conceptual level of the rules to be learned. This approach is grounded in aspects of Schoenfeld’s (1985) approach to mathematical problem solving, Lave’s (1988) situated learning approach, and schema theory (Anderson, 1977). During our work on
mechanics such as this one as well as others (see case examples below), we began compiling a list of criteria for good learning mechanics.
Criteria for Learning Mechanics
Learning mechanics have to meet a series of criteria. Most importantly, learning mechanics are grounded in Learning Sciences and Learning Theory. Over the past several decades, many such theories and frameworks have been developed that can be used as the basis for the design of learning mechanics. Examples of these are Cognitive Flexibility Theory
(Spiro, Coulson, Feltovich, & Anderson, 1988; Spiro & Jehng, 1990), Cognitive Apprenticeship
(Collins, 1988; Liu, 1998), Anchored instruction (CTGV, 1990, 1991, 1992, 1993), and
Situated Learning (Lave, 1988; Lave & Wenger, 1990). From these and other, related theories, designers choose activities that engage the learner in meaningful interaction with a specific subject. These interactions should follow a theoretical model of interactivity such as the INTERACT model, which distinguishes three types of interactivity: behavioral interactivity, cognitive interactivity, and emotional interactivity, and describes their relation in learning processes such as feedback and guidance (Domagk, Schwartz, & Plass, 2010). Learning designers use these interactions to describe tools that allow learners to generate solutions to the learning problems that are designed to facilitate learning. If the subject matter allows for different but equally appropriate solutions to problems, the mechanic should enable the learner to generate their own solutions.
Based on this selection of a learning mechanic, game designers have a choice among different game mechanics to instantiate the learning mechanic in the game. In the case of our learning mechanic for Noobs v. Leets described above, possible game mechanics included the
Fling mechanic from Angry Birds, the Drag Mechanic from Implode!, and the Bounce
mechanic from Doodle Jump. In order to preserve the original learning goal, however, there are several requirements that a game mechanic has to meet in order to be an appropriate implementation of a learning mechanic.
Requirements for Designing Game Mechanics based on a Learning Mechanic
Learning mechanics describe activities that have the expressed goal of facilitating learning and are grounded in the learning sciences and learning theory. But learning mechanics are design patterns of mechanics, or meta-mechanics, not playable mechanics. They describe the functions of the tools available to players to solve problems, but they don’t describe the actual tools themselves. For example, the learning mechanic might specify that the learner/player should be able to apply rules to solve problems, but it does not describe whether this is done by flinging objects representing the rules at the problems, dragging those objects, or putting a jet pack on them to guide them to the problem.
Learning mechanics need to be instantiated as game mechanics to describe the concrete tools and moves the players have in the system that the game represents. Playing the game is learning these tools and moves, getting facile with them, and having the satisfaction of solving challenges, of ‘beating’ the game (Juul, 2003). In addition, game designers need to add the game feel, the feel of engaging the core mechanics through interactive elements, visual elements, emotional elements and sound elements that are added to the game mechanic (Swink, 2008).
However, in this creative process, game designers have to make sure that the learning goal is preserved. To that end, when selecting a game mechanic to implement a particular learning mechanic, designers need to consider the following requirements:
(1) Game Mechanic must not introduce excessive amounts of extraneous cognitive load. The instantiation of a learning mechanic as a game mechanic, and the addition of playful elements that this may involve, are by their very nature introducing demands on learners’ cognitive processing that are not directly related to the processing of the essential learning content. Although traditional cognitive load researchers would argue that such extraneous cognitive load should always be removed (Kirschner, Sweller, & Clark, 2006), the success of many games seems to indicate that elements such as a narrative, the requirement of
resource management, or incentive systems, can have a positive impact on learning. This suggests that the under some conditions motivational benefits of such features can outweigh the negative impact of the additional cognitive processing they require. It is therefore
necessary to design game mechanics that do not introduce excessive amounts of extraneous cognitive load that would turn this advantage into a disadvantage. For example, the game
Dimenxian X requires learners to perform a series of task that are only peripherally related to learning goals, such as in the scene depicted in Figure 2, where the player has to retrieve data packets from an underwater cavern.
Figure 2. Extraneous Cognitive Load in Dimenxian X.
In cases such as this it needs to be determined whether these additional game tasks enhance or suppress learning, which can often only be decided through empirical research.
(2) Game Mechanic must not reduce the amount of the required mental effort by too much. Another requirement related to cognitive load is that game mechanics do not reduce the task demands imposed on the learner too much, i.e., that the mechanic does not provide the results of the processing of the information or problem solving to the learner but instead requires the learner to introduce mental effort to generate a solution. Research has shown that such reduction of germane cognitive load can have a negative impact on learning (Schnotz, Böckler, & Grzondziel, 1999).
Figure 3. Low Germane Cognitive Load in the Algebra Game AlgebraActor
For example, in the Algebra game depicted in Figure 3, the game mechanic is designed to show the learner how the term (b) from the right side has to be added as (-b) on the left side when resolving for x. This eliminates the need for the learner to decide where to place the term (b) on the left side, and to specify that the sign of b has to change to (-), which
unnecessarily reduced the amount of useful cognitive load required to solve this problem. Unless a later level removes this scaffolding, it is less likely that the learner will be able to solve a similar question by themselves and in a different context, i.e., as transfer task.
(3) Game Mechanic must not introduce unnecessary confounds. Anytime a learning mechanic is instantiated as a game mechanic, there is a possibility that additional knowledge and skills are introduced that the learner has to master in order to succeed. Examples are requirements on fine motor skills, content knowledge, or content-related skills.
For example, the game mechanic in the popular game Angry Birds involves moving the correct bird to the appropriate part of the structure containing pigs via a sling mechanic that requires players to determine the angle and force of the bird trajectory (Figure 4). If a learning game were to use this mechanic, then a learner who might know which bird should hit which part of the structure would also need to have basic knowledge of Newtonian mechanics and the necessary fine motor skills to use the sling mechanic to be able to move the bird to this spot. In this case, the game mechanic would introduce the skill of using a sling and physics knowledge to the solution of the problem, which adds unnecessary confounds from a learning perspective.
Library of Learning Mechanics
In order to provide game designers of learning games with a set of learning mechanics and associated instances of game mechanics that they can use for their own game design, we have begun to compile a library of mechanics. This library includes a variety of options of game mechanics for each learning mechanic; see Figure 5 (see http://g4li.org for updates).
Figure 5. Library of Learning Mechanics and Associated Game Mechanics
It is important to note that since learning mechanics are design patterns, there is a one-to-many relationship of learning mechanics to game mechanics, and that each of the different game mechanics that instantiates a learning mechanic may only be suitable under specific conditions. Our ongoing research is concerned with adding new learning mechanics and
associated game mechanics, and with demonstrating their viability and usefulness through empirical research.
Assessment Mechanics
In addition to engage learners in meaningful activities that facilitate learning and assist in the creation of mental models, games also have the ability to provide educators and designers as well as the players/learners themselves with insight into players’ learning processes and advancements. The rules systems created by game mechanics can also be used for
assessment of a variety of variables including, but not limited to, learning outcomes.
Learning objectives and learning processes of interest can be operationalized into specific actions within the game that allow for an assessment of their level of achievement by the player/learner. Player actions can be captured in log files and can be analyzed to reveal what players learned and how they learned it. Game mechanics for assessment must therefore be designed to elicit relevant behaviors that can be observed through the user log and
interpreted to reveal learning process, outcome, and learner variables. We call mechanics designed for this purpose Assessment Mechanics and define them, based on Salen & Zimmerman (2004), as follows:
Assessment mechanics are patterns of behavior or building blocks of diagnostic interactivity, which may be a single action or a set of interrelated actions that form the essential diagnostic activity that is repeated throughout a game.
Similar to learning mechanics, assessment mechanics are design patterns, or meta-mechanics, that describe the tools or activities but are not playable mechanics. They describe the functions of the tools available to players to demonstrate their knowledge and skills or expressions of other variables of interest, but they don’t describe the actual tools or
assessment tasks themselves. For example, the assessment mechanic might specify that the learner/player should group related items in time or space, but it does not describe whether
this is done by shifting items on the screen like in Bejeweled, dropping them in specific locations like in Drop Seven, or placing them like in a tower defense game.
There are several variables that are of interest in the design of personalized or adaptive games. These variables can be grouped as general trait variables, general state variables, and situation-specific state variables. Some of these variables can be reliably assessed with valid traditional instruments, but for many variables only methods with low reliably and validity are available, often involving self-reported data, which are susceptible to learner biases and other response sets.
For example, learners’ self-regulation is of interest because it describes whether players establish learning goals, monitor their goal achievement, and change strategies when they are not able to achieve their goals. Another example is the assessment of specific aspects of learning. In the game Noobs vs. Leets, we were interested in understanding how well the learner comprehends rules related to angles in quadrilaterals, such as the complementary angles rule, and opposite angles rule. We therefore chose an assessment mechanic that required learners to drag the correct rule to the angle to be solved (Figure 6, right). An alternative assessment mechanic that would have required the learner to drag or enter the correct numeric value for each angle (Figure 6, left) could not have revealed the source of any possible errors, which could have been conceptual (lack of rule knowledge) or arithmetic (lack of subtraction skills).
Figure 6. Two Assessment Mechanic options in Noobs vs. Leets
This reasoning is based on the Evidence-Centered Design (ECD) Framework (Mislevy, Almond, & Steinberg, 2003). This framework provides a formal approach to the essential questions related to assessment design: What should be assessed (Student/ Competency Model); What kinds of learner behaviors can be used to reveal these constructs (Evidence Model), and, What tasks and activities can be designed to elicit these behaviors (Task Model). The ECD model can be user to compile a list of criteria that mechanics have to meet in order to be useful assessment mechanics.
Criteria for Assessment Mechanics
Similar to learning mechanics, assessment mechanics have to meet a series of criteria to assure they engage the player in meaningful and valid assessment activities. The overall goal of assessment mechanics is to elicit relevant behaviors that can be observed through the user log and interpreted to reveal learning process, learning outcomes, and learner variables. In order to be useful for this purpose, i.e., to be a valid measure producing reliable scores, a first criterion is that assessment mechanics have to be based on models such as ECD. Based on the student model of target competencies, described in relation to the changes in skills, knowledge, identity, values, and epistemology of interest (Rupp, Gushta, Mislevy, & Shaffer, 2010), assessment designers need to construct an ECD Evidence Model. This means that they have to specify the salient features of learner behavior and the rules for scoring and interpreting these features for the purpose of assessment. This includes another important criterion for the design of assessment mechanics, which is the consideration of Test
Theoretical Concerns. For example, since we cannot assume that individual test items can be independent of one another, the statistical model of the assessment has to reflect these possible dependencies (Rupp et al., 2010).
The next criterion is that the mechanics should to be designed based on aspects of the ECD Task Model, i.e., the description of the key tasks and activities in which the learner will engage for the purpose of assessment. These tasks form the assessment mechanic, and it is
essential that they are designed in a way that the task execution can be captured through the instrumentation of the game. This requires, for example, that the mechanics require the learner to make explicit the steps learners used for problem solving rather than simply provide the answer to the problem. The mechanics should also allow for repeated exposures to similar problems to allow for multiple observations of the behavior of interest.
Depending on the decisions made by the designers, the assessment character of the assessment mechanic may or may not be obvious to learner. We describe assessments in which learners are likely aware of the fact that they are being assessed as Embedded
Assessment, and those where they are not aware of this fact as Stealth Assessment (Shute, 2010).
Once measurement experts have designed an assessment mechanic, game designers can design corresponding instances of game mechanics. However, in this process, several design requirements have to be met.
Requirements for Designing Game Mechanics based on Assessment Mechanics
One of the most common problems in designing game mechanics based on assessment mechanics is the introduction of various confounds that make it difficult to determine whether variability in learning scores among learners can be attributed to their different knowledge and skills, or whether it is caused by other factors.
One such confound is the addition of new sources of extraneous cognitive load. For example, game mechanics such as in Flight Control, where players have to determine the approach patterns of airplanes for landing, are fun and engaging because the fast succession of a high number of planes to land puts high demands on players’ processing. This would be appropriate to assess speed of processing, but not to assess conceptual knowledge or higher level thinking.
A related confound is the addition of scaffolding or guidance that reduces the cognitive task demands for some learners but not for others. For example, if key information in an
adventure game is hidden so that only some players will find it then assessment of knowledge will be confounded by learners’ exploration strategies.
Another typical problem for assessment is when the game mechanic introduces confounds though demands on fine motor skills, which is highly variable in learners. For example, MotionMath asks learners to tilt their tablet device to direct a ball to the correct answer. Success in this task does not only depend on learners’ knowledge, but also of their fine motor skills in being able to move the ball to the correct location.
Likewise, many game mechanics include activities that require the learner to have additional content knowledge or skills from unrelated subject matter areas. For example, an assessment of algebra may be confounded by the need to know about Newtonian physics. Although the integration of different subject matter areas is a desirable design feature for learning mechanics, the use of the same strategy in the design of assessment mechanics may confound results.
A final confounding variable to consider is emotion. During game play, learners will likely experience a series of emotional responses that would impact learning outcomes (Um, Plass, Hayward, & Homer, 2011). Designers of assessment mechanics need to consider learners’ emotions and design mechanics aimed at assessment in a way that the learners’ emotional response does not interfere with their ability to solve the problems presented. A particularly problematic situation would be a mechanic in which different people respond emotionally in different ways. If such a situation is expected, the assessment model should include learners’ emotional state as a variable.
Our current work aims to compile a library of assessment mechanics with corresponding game mechanics that meet our requirements.
Library of Assessment Mechanics
In order to provide game designers of learning games with a set of assessment
have begun to compile a library of mechanics. This library lists a variety of options of game mechanics for each assessment mechanic, see Figure 7 (see http://g4li.org for updates).
Figure 7. Library of Assessment Mechanics and Associated Game Mechanics
It is important to note that there is a one-to-many relationship of assessment mechanics to game mechanics, and that each of the different game mechanics that can instantiate an assessment mechanic may only be suitable under specific conditions. Our ongoing research is concerned with adding new assessment mechanics and associated game mechanics, and with demonstrating their viability and usefulness in empirical research.
Summary and Conclusion
Our approach suggests that Game Mechanics, the essential game play activity, should be distinguished from Learning mechanics and Assessment mechanics. We define Learning Mechanics as patterns of specialized activities, grounded in the learning sciences, that have learning as the primary objective. In contrast, Assessment Mechanics are patterns of
specialized activities, grounded in test theory, that have assessment as the primary objective. Learning and assessment mechanics are design patterns, or meta-mechanics, that can be instantiated onto corresponding game mechanics, following criteria we outlined above to preserve their intended teaching or assessment objective.
Variables related to learning that can be measured through game metrics include learning outcomes (cognitive and skills), trait variables, general state variables, and situation-specific
state variables. Supplementing log data of game events and user behavior with observational data extends the ECD model and results in more valid assessments of these variables.
Our approach serves two related but separate goals. One goal is a measurement goal – embedded assessment allows for more detailed insights into learning than many traditional instruments, both with respect to the process of learning and learning outcomes. This has implications for research as well as learner competency testing. The other goal is related to improving the game play. By using assessment mechanics to measure a series of learner variables, a learner model can be compiled that allows for the design of games that are individualized and adaptive to a learner’s specific needs and characteristics. This has implications for the design of effective games for learning by making games more adaptive and personalized, and, hopefully, more effective.
In summary, we described an approach that, grounded in theory and tested in several game design projects, has implications both for research and practice of the design of games for learning.
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