In many ways, it seems as if video games should be treated differently from other potential learning experiences that might bring about transfer. They are new and unfamiliar to many, especially older adults; the games rely on technologies that weren’t around a generation ago. Some games have the added cachet of being branded a bad influence on modern youth, and some are said to be so exciting to play that people become addicted to them. All these qualities give video games a mysterious distinction that could tempt an uninformed commentator to jump to the conclusion that our existing understanding of the attributes of and constraints on transfer of learning do not apply here. One might hope that all that has to be done is to dress up some academic content as a game, place it in front of some children, and voila! If video games are so captivating that users can get addicted to them, surely games can be used to teach anything. However, learning is still learning, even if it is wrapped up in electronically assisted and captivating packaging. As has been amply demonstrated elsewhere (see Barnett & Ceci, 2002), the history of research on learning and transfer also suggests that it cannot be taken for granted that learning will transfer, or indeed that any learning will necessarily occur from a given set of experiences. In fact, quite the opposite is often claimed (see, e.g., Detterman, 1993).
Transferability of Learning
Famous psychologists have been debating the transferability of learning for more than 100 years. At the beginning of the twentieth century, Judd (1908) stated, “Every experience has in it the possibilities of generalization” (p. 38), while Thorndike and Woodworth (1901) claimed the contrary, stating, “There is no inner necessity for improvement of one function to improve others closely similar to it, due to a subtle transfer of practice effect” (p. 386). More recently, Halpern (1998) (p.16) claimed, “Numerous studies have shown that critical thinking … can be learned in ways that promote transfer to novel contexts” (p. 449), but Detterman (1993) argued, “Reviewers are in almost total agreement that little transfer occurs” (p. 8).
In his classic educational psychology text, Klausmeier (1961) asserted, “A main reason for formal education is to facilitate learning in situations outside school” (p. 352). Thus, if critics are correct in asserting that transfer very rarely happens, the justification for educational and training expenditures may need to be reevaluated: as Detterman suggests, “Cognitive psychologists, and other people who should know better, continue to advocate a philosophy of education that is totally lacking in empirical support” (p. 16). This debate is as important for video game learning as it is for traditional, nondigital, educational endeavors. If Detterman is right, the whole enterprise may be doomed to failure.
Barnett and Ceci (2002) reviewed evidence of transfer of learning from hundreds of studies across several decades in search of conclusive proof of transferable learning. They sought to get definitive answers as to whether transfer really happened. They also sought to determine why what seems like such a simple question has led eminent researchers to form such divergent opinions for so long. The answer was twofold. First, the question is not a simple one, because it is not well-defined and, second, because the answer depends on the circumstances, the answer may differ depending on the situation to which one hopes to generalize findings.
Defining the question requires defining successful transfer. One possible definition is “the carrying over of an act or way of acting from one performance to another” (Woodworth & Schlosberg, 1954, p. 734). But there are no clear, agreed-upon criteria for what constitutes “carrying over.” For example, does it count as true transfer if the experimenter has to drop loud hints to the participants to let them know that they should be able to carry over what they have just learned to a new set of apparently unrelated problems? One researcher may count the resulting behavior from this situation as successful transfer while another might not. Much disagreement comes from situations that essentially compare apples and oranges. For some purposes, aspects such as spontaneity may be crucial, whereas for others they may be unnecessary. For example, if the hope is that learners will apply acquired mathematical and statistical literacy to critical evaluation of issues and policies they read about in the news, spontaneity is required: there will be no omnipresent experimenter to remind them of the applicability of that knowledge. On the other hand, if the goal is to use the same mathematical and statistical literacy to analyze the results of a quantitative academic study, the analyst can look up the applicability of particular procedures in a textbook or ask a friend for advice, and depending on these prompts or hints is less of a problem.
Another proposed definition of successful transfer is “the ability to extend what has been learned in one context to new contexts” (Bransford, Brown, & Cocking, 1999, p. 39). Again, however, there is no clear, agreed-upon definition of (p.17) a “new context.” If a study participant learns a mathematics algorithm and applies it successfully to a novel problem on the next page of the math textbook five minutes later, is “page 29 at 2:33 p.m.” a new context from “page 28 at 1:58 p.m.”? Or does the participant need to apply the learned algorithm to, for example, calculating required quantities of flour and other ingredients while baking a cake at home that weekend? How different does a context have to be to be truly “new” and therefore count as transfer? Again, resolution to this question may come from agreement as to the goal of the education or training effort. If the goal is transfer to a later work or home context, researching immediate transfer in the laboratory doesn’t answer all these relevant questions. Generalizing from lab research to home and work can only be justified if elsewhere it has been shown that these contextual differences are irrelevant.
Thus, before judgment can be rendered regarding the value of video games as a tool to teach transferable learning, evidence must be assessed regarding the success of transfer from video games to academic subjects and other desired aspects of daily life. Further, before conclusions can be reached regarding the generalizability of specific findings, it is important to clearly define what is meant by successful transfer for that purpose. For example, in a given case, does transfer count if it only occurs when the circumstances of application make it obvious to the transferee that transfer is required, either by overt prompting or by more subtle hints, or should spontaneity be a requirement? Similarly, should transfer be considered successful if the learning experience changes one performance attribute, for example speed of response, but not others, such as accuracy or response quality? Further, if the success of transfer is assessed only in contexts that are very similar to the context of learning, it is unknown whether transfer would be successful in more remote contexts. Research elsewhere has shown that many dimensions of the learning and transfer context may affect transfer success (for examples, see Barnett & Ceci, 2002). Thus, generalizability of findings may be limited unless these dimensions are taken into account.
Effects of Learning Content on Transfer
Many characteristics of learning and transfer tasks may affect transfer success. Three attributes that have received some attention from transfer researchers are (1) the possible effects of the type of skill that is being learned; (2) the aspect of performance being changed; and (3) the memory demands of the improvement being assessed (see Table 2.1 as drawn from Barnett & Ceci, 2002).
The type of learned skill can vary from a very specific, routinized procedure to a broader, more general principle or heuristic as in the use of hierarchical classification to organize understanding. This specificity-generality continuum has been a topic of investigation in transfer and learning research for some time. For (p.18)
Table 2.1 Characteristics of the Learning and Transfer Tasks
A. Content: What transferred
| |||
|---|---|---|---|
Learned skill
|
Procedure
|
Representation
|
Principle or heuristic
|
Performance change
|
Speed
|
Accuracy
|
Approach
|
Memory demands
|
Execute only
|
Recognize and execute
|
Recall, recognize and execute
|
From: Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn?: A taxonomy for far transfer. Psychological Bulletin, 128, 4, 612–637, APA, reprinted with permission.
Similarly, the nature of the performance change being measured can affect the success of transfer. For example, Vasta, Knott, and Gaze (1996) found that training on a water jug level task eradicated a previously found sex difference in performance when the outcome measure being assessed was answering the problem correctly, but not when success was defined as articulation of the correct principle. Classic work by Reed, Ernst, and Banerji (1974) on analogical transfer also found that transfer success differed depending on whether the measure of transfer was solution speed or the number of errors made.
One of Detterman’s (1993) most powerful criticisms of studies purporting to demonstrate successful transfer of learning concerns the third content dimension—the memory demands of the transfer task. He notes that many studies only find successful transfer if the participants are told, either directly or by indirect hints, that they are supposed to be transferring their learning to a particular new task. However, as Detterman points out, a major component of the challenge of transferring learning is often determining when and where information that has been learned is relevant. In the real world outside academic settings, one is rarely told which specific learned skills or information to apply. Thus, for many purposes, spontaneity may be considered a requirement for proof of true, generalizable transfer success.
(p.19) Effects of Context on Learning and Transfer
Another challenging aspect of transfer research is ensuring that learning has been transferred to truly new contexts, often referred to as “far contexts” in the learning and transfer literature. At first blush, it is not obvious that context matters for learning and transfer. Surely, one might think, a learning experience should be the same, and the resulting learning the same, no matter where, when, or why it occurs, if the task and procedures are the same—end of story. It shouldn’t matter whether that learning is occurring at school, at home, or on the sports field. However, evidence suggests otherwise.
In a classic study of children’s learning, at home and in the laboratory, Ceci and Bronfenbrenner (1985) investigated the effect of varying the physical context of learning. Groups of children were asked to bake cupcakes. While the cupcakes were in the oven for 30 minutes, the children were allowed to play an appealing game (Pac-Man). The learning aspect of the task was the implicit requirement to keep track of time, without wasting too much effort staring continuously at the clock. At the beginning of the baking time, the children glanced at the clock frequently to check how fast time was passing. Once they learned how fast time was passing and their internal clock was calibrated, they should have needed to check the clock less frequently, allowing them to concentrate on the more enjoyable experience of playing the game, until the time was almost up. At this point, they might have been expected to check more frequently to ensure that they didn’t burn the cupcakes. This pattern was indeed shown by one group of children—those who participated in the experiment at home. However, children who participated in the same experiment in the laboratory behaved differently (except for a group of older boys). Those in the lab kept looking at the clock more and more frequently as time passed, throughout the entire 30 minutes, without taking a break. They never learned to calibrate their internal clock (or at least did not demonstrate any improved ability to do so). Thus, the simple fact of being in a laboratory rather than a home setting changed how they approached the task and the learning they demonstrated. Thus, the physical context affected learning.
Physical context also affects the transferability of learning. A well-known example of this type of contextual effect is Godden and Baddeley’s classic study (1975) showing that word lists learned underwater were more easily retrieved when the learner was again underwater than when memory was attempted on dry land. Thus, if an experimenter seeks to show that learning can be transferred to a new context, and if the purpose of the educational effort being evaluated requires transfer to a different physical context, the training and testing must occur in different physical contexts. Otherwise, generalization of study results will be limited.
What other aspects of context might also affect learning and transfer? Barnett and Ceci (2002) specified six dimensions (see Table 2.2). For each of these (p.20)
Table 2.2 Characteristics of the Learning and Transfer tasks: Context
B. Context: where transferred from/to
| |||||
|---|---|---|---|---|---|
Near ←--------------------------------------------------------------------→ Far
| |||||
Knowledge domain
|
Mouse vs. rat
|
Biology vs. botany
|
Biology vs. economics
|
Science vs. English
|
Science vs. cooking
|
Physical context
|
Same room at school
|
Different room at school
|
School vs. research lab
|
School vs. home
|
School vs. at the beach
|
Temporal context
|
Same session
|
Next day
|
2 weeks later
|
Months later
|
Years later
|
Functional context
|
Both clearly academic
|
Both academic but one non-evaluative
|
Academic vs. filling in tax forms
|
Academic vs. informal questionnaire
|
Academic vs. at play
|
Social context
|
Both individual
|
Individual vs. pair
|
Individual vs. small group
|
Individual vs. large group
|
Individual vs. nation?
|
Modality
|
Both written, same format
|
Both written, multiple choice vs. essay
|
Book learning vs. oral exam
|
Lecture vs. wine tasting
|
Lecture vs. wood carving
|
From: Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn?: A taxonomy for far transfer. Psychological Bulletin, 128, 4, 612–637, APA, reprinted with permission.
Similarly, aspects of temporal context, such as elapsed time between training and transfer, might be expected to affect transfer success. For most purposes, to be useful, learning must be enduring, but some learning may be fleeting, so transfer tests should evaluate the longevity of learning and transfer, if results are to be generalizable to different contexts. Although applying learning right away might be expected to be easier than applying that same learning months or even years later, this is not always the case: time can have more puzzling effects. For example, in a physics learning study, Craig, Chi, and VanLehn (2009) found no difference between three groups of learners on a short-term transfer task but did find a difference on longer-term retention and transfer measures. Thus, temporal context must be considered when generalizing from transfer studies.
The relevance of some of the other context dimensions is more questionable and begs further exploration. For example, the literature on the phenomenon of (p.21) functional fixedness suggests that functional context might affect transfer. The functional context is the purpose of the task, or the mindset it invokes—whether the purpose is to play, to learn, or to earn money, for example. We do not know whether functional context affects transfer. Functional fixedness (see Duncker, 1945) refers to the fact that the use of objects is often so tied to their original purpose that it’s hard for people to think of using them in other ways. For example, you may look for a screwdriver, not realizing that a dime in your pocket could get the job done. Similarly, academic learning may be tied in our mind to academic situations and not readily transfer to work or play, or video gaming skills may be tied to play (even if conducted at school) and not transferred readily to work. Unfortunately, much transfer research is conducted within a single functional context, so transfer across contexts is rarely tested.
Social context is another dimension that might affect transfer success, but for which evidence is currently scarce. Although schools may be increasingly using collaborative approaches, little is known about their effect on transfer (Druckman & Bjork, 1994). Some learning studies do exist. For example, Chi, Roy, and Hausmann (2008) compared learning from textbooks and watching tutoring videos collaboratively and individually, and found a benefit of collaboration. They attribute the benefit of collaboration to interaction resulting in deep learning, which has been associated with far transfer (see Barnett & Ceci, 2002).
The modality dimension is also not well understood. In one interesting example, Herrnstein, Nickerson, de Sanchez, and Swets (1986) compared transfer measures in various modalities, including written questions and questions read aloud by the teacher, and a practical design task and an oral argumentation task. Although transfer was found on most of the measures, transfer success varied between test modalities, with the largest benefits generally found on tests closest to the modality of original training.
Thus, there is some evidence to suggest that these dimensions of transfer context might affect transfer success. If these dimensions indeed do matter, it is possible that a particular learning experience might transfer successfully to a context that is near to the context of learning on some or all of these dimensions, but not to a far context. For example, an academic lesson about photosynthesis learned in the classroom from a textbook might successfully transfer to a paper-and-pencil test in the same classroom the next day, but the learner might not be able to show evidence of that learning while doing yard work that weekend. Similarly, knowledge acquired during video game play might not be accessible outside of that context. We do not know whether this is the case, but we do know enough about transfer to know that it cannot be taken for granted that learning that has been demonstrated in one near context will necessarily transfer to another far context. Barnett and Ceci’s (2002) taxonomy of transfer content and context provides a framework to guide design of future transfer tests to ensure that aspects relevant to generalizability are investigated.
(p.22) Evidence for Far Transfer
Even outside the world of video games, there are few documented examples of successful transfer to a far context. It is so much easier to study learning and transfer in the context of a single session, in the same location, and in the same domain. Two notable exceptions are a study of elementary school children by Chen and Klahr (1999) and a study of university students by Fong, Krantz, and Nisbett (1986). Chen and Klahr’s study involved training and transfer of the “control of variables” strategy for scientific reasoning. In addition to more immediate tests in a near context, Chen and Klahr tested what they termed “remote” transfer. Specifically, they evaluated transfer of learning several months later (a far temporal context), in different domains and using a different testing format (different modality). Testing was also conducted by different experimenters, providing a different physical context. Despite the successful near transfer found among both third- and fourth-grade children, only the fourth-grade group showed evidence of successful far transfer. Thus, evidence of successful near transfer cannot be assumed to mean that far transfer will also occur.
Fong et al.’s (1986) creative study of far transfer used a transfer test disguised as a household survey, and was conducted at home over the phone. Their training phase was a university statistics course, and the transfer test involved questions about sports, for which statistical principles were relevant. For example, one question posed was “In general, the major league baseball player who wins Rookie of the Year does not perform as well in his second year. This is clear in major league baseball in the past 10 years. In the American League, eight Rookies of the Year have done worse in their second year; only two have done better. In the National League, the Rookie of the Year has done worse the second year 9 times out of 10. Why do you suppose the Rookie of the Year tends not to do as well his second year?” Answers were coded for evidence of good statistical reasoning. A typical response that does not show evidence of good statistical reasoning would be, “The Rookie of the Year doesn’t do as well because he’s resting on his laurels; he’s not trying as hard in his second year.” A response that does show evidence of good statistical reasoning would be, “A player’s performance varies from year to year. Sometimes you have good years and sometimes you have bad years. The player who won the Rookie of the Year award had an exceptional year. He’ll probably do better than average in his second year, but not as well as he did when he was a rookie.” Students tested at the end of the semester, after taking the statistics course, showed more evidence of good statistical reasoning on the survey questions than those tested at the beginning, before taking the course, though only for some of the questions. Transfer was shown to a far context along many dimensions, including physical context (lecture hall versus home), functional context (academic class versus a household survey), and modality (lectures and written work versus a phone conversation).
(p.23) These two studies show that far transfer of learning is possible. However, their partial success also cautions that it cannot be assumed that far transfer will always occur, just because evidence for near transfer has been shown. Regardless, these studies set the standard against which future tests of far transfer, whether from video game learning or other, more conventional educational formats, can be assessed.
Evaluating Evidence for Video Game Transfer
Digital games have been hypothesized to offer a number of potential instructional benefits as a learning medium (O’Neil, Wainess, & Baker, 2005), including interactivity, which outside the world of video games has been associated with deeper understanding and more successful far transfer (Barnett & Ceci, 2002; Bransford, Brown, & Cocking, 1999; Reed & Saavedra, 1986; Halpern, 1998). However, “while effectiveness of game environments can be documented in terms of intensity and longevity of engagement (participants voting with their money or time), as well as the commercial success of the games, there is much less solid information about what outcomes are systematically achieved by the use of individual and multiplayer games to train participants in acquiring knowledge and skills. … What is missing is how games should be evaluated for education and training purposes” (O’Neil et al., 2005, p. 456). To attain this goal, it’s been suggested that assessment be built into the learning games themselves: “Games that teach also need to be games that test,” (Michael & Chen, 2005). However, such near transfer tests may not necessarily translate to successful far transfer to contexts outside video games, depending on the particular goal of the training program.
Even when the transfer goal is clear, that is, when the purpose is to train employees for particular work-related tasks, transfer testing cannot simply focus on that set of tasks in the work environment, without bearing in mind that a demonstration of successful transfer cannot necessarily be generalized to different training and transfer situations. That is, if a pilot test of a particular training program, which shows successful transfer, is conducted in an unrealistic environment where the transfer required is only to near contexts (testing soon after training in the same location by the same individuals), the findings may not generalize to real world applications of the same training program when scaled up. For example, Rosser, Lynch, Cuddihy, Gentile, Klonsky, and Merrell (2007) studied the relationship between surgeons’ video gaming experience and performance on a laparoscopic surgery skills game. They found a significant relationship. However, as pointed out by Curet (2007), scores on the laparoscopic surgery skills game did not necessarily relate to skill at actual surgery. Perhaps, for example, surgeons performing under the stress of a real life on the line might behave differently from when they are merely playing (that is, in a different functional context).
(p.24) The problem of how to assess transfer is even more complex when assessing the benefits of games for other educational purposes, because the desired outcomes may be less clear. In both cases, research on transfer of learning from video games can be evaluated by situating findings in the taxonomy of transfer content and context described above. One area in which video game play has been found to improve performance in transfer tasks for a potentially important skill is in three-dimensional (3-D) mental rotation: the visualization and imaginary rotation of an object that is presented as a two-dimensional drawing. This concept was introduced to the field by Shepard and Metzler in 1971 and further explored by Vandenberg and Kuse (1978), who popularized the classic mental rotation test. Each stimulus in this test is a two-dimensional image of a 3-D object. Each object is shown at different orientations and participants are required to recognize, as quickly as possible, which images represent rotated versions of the same object. Reliable gender differences are found on this measure, in favor of males.
The male superiority on tests of 3-D mental rotation has been the subject of a great deal of discussion in the debate surrounding the disproportionate number of men at the top of science, technology, engineering, and mathematics (STEM) fields (see Ceci, Williams, & Barnett, 2009). The overrepresentation of men in these fields has been attributed by some (see Summers, 2005) to innately superior mathematical skills. Although males were once thought to do better, on average, than females on all aspects of mathematics, in the face of more recent evidence, the supposed area of superiority has been narrowed to spatial skills only, and even more recently narrowed again to the particular skill of 3-D mental rotation, a skill for which there is reasonably robust evidence of superior average male performance. Whether this skill is linked to the overrepresentation of men in STEM jobs is currently unknown, but the finding has led to increased interest in understanding causes of differences in 3-D mental rotation skills. The argument for an innate difference in ability between males and females has been bolstered by findings of gender differences among kindergartners (Casey, Andrews, Schindler, Kersh, Samper, & Copley, 2008). However, evidence from innovative video game training studies (Terlecki & Newcombe, 2005; Feng, Spence, & Pratt, 2007) argues in favor of an experience-based explanation. If 3-D mental rotation may be a factor limiting the advancement of women in STEM fields, it is important to understand whether video game play can improve 3-D mental rotation skills in a durable way that transfers.
Feng, Spence, and Pratt (2007) trained participants by having them play a 3-D first-person shooter game (Medal of Honor) for several sessions in their laboratory. Such games require intense visual monitoring and attention. The hypothesis explored was whether spatial attention distribution, a “basic capacity that supports higher-level spatial cognition” (p. 850), could be modified by playing a video game and whether improving individuals’ spatial attention distribution would also lead to improved higher level mental rotation ability (3-D mental rotation). The control group played a different 3-D computer game (a maze (p.25) puzzle game) that did not involve focused attention on a target and was therefore not expected to improve distribution of spatial attention. Spatial attention was assessed using the Uniform Field of View task, in which participants are required to indicate the direction in which a target has very briefly appeared, after a visual mask. Mental rotation ability was assessed using Vanderberg and Kuse’s (1978) test, described above, in which different 2-D representations of a 3-D object must be recognized as representing the same object. Results confirmed the hypothesis and showed a reduction in the preexisting gender difference on both measures.
So does this mean that video game play can solve the problem of the dearth of women at the top of STEM fields? Clearly, the answer partly depends on the relevance of these skills to the success of women—compared to the relevance of other possible skill differences and societal and discriminatory factors—which learning studies such as that by Feng et al. (2007) do not address. It also depends on whether improvements shown in these kinds of studies robustly transfer. In general, the further the contexts to which transfer is demonstrated in the experimental situation (testing in a physical context remote from the context of learning; learning and transfer tasks differing in purpose and in contrasting modalities; testing after a substantial time has passed since learning occurred; etc.), the further we can be confident the skills will transfer outside of the experimental situation.
As can be seen in Table 2.3, apart from the knowledge domain, all aspects of context for the Feng, Spence, and Pratt (2007) posttest were near to the context of training. Assessments were conducted soon after training, in the same lab, and both training and transfer tests were overtly research-oriented, involved working individually, and were computer-based. However, the knowledge domain, involving transfer from shooting virtual soldiers to locating the direction of dots on a screen and mentally rotating abstract shapes, can be considered somewhat far transfer. Also, the follow-up test assessed durability of the enhancement an impressive five months later.
Presumably, transfer of these skills would be desirable in many contexts: in book and lab work; at school, in research institutions or in the workplace; in test performance; and at a later time, months or even years later. Thus, for example, it is important to know whether similar improvements would have been found if the training and transfer tasks had not both been computer-based. Would a team of engineers designing a bridge be better at visualizing their plans and detecting design issues from various perspectives if they were trained on Medal of Honor? Would a study group have enhanced success on their trigonometry exam? What is the likelihood that improving these skills will have an effect on women’s success in STEM fields? Greene, Li, and Bavelier (2009) have suggested that action video game experience teaches individuals to “form templates for, or extract the relevant statistics of, the task at hand” (p. 1). What we lack is an understanding of how and when aspects of STEM jobs might tap into such skills. (p.26)
Table 2.3 Transfer Context of Feng, Spence, and Pratt (2007) Experiment 2
Context: where transferred from/to
| |||
|---|---|---|---|
Near ←----------------------------------------------------------→ Far
| |||
Knowledge domain
|
Shooting virtual soldiers vs. locating dots and rotating abstract shapes
| ||
Physical context
|
Same lab
| ||
Temporal context
|
Soon after training (posttest)
|
About 5 months later (follow-up)
| |
Functional context
|
Both clearly research
| ||
Social context
|
Both individual
| ||
Modality
|
Both computer-based
| ||
Based on: Feng, J., Spence, I., & Pratt, J. (2007). Playing an action video game reduces gender differences in spatial cognition. Psychological Science, 18, 850–855.
Similarly, transfer success for these skills may be sensitive to the content of the tasks. For example, would the video game training enhance mental rotation performance measures that are not time-pressured tests? This issue is important, because many aspects of STEM professionals’ work do not require quick responses, but rather deliberate and prolonged thought. Studies such as these represent only the beginning of investigation into these exciting possibilities. Future research might fruitfully investigate transfer situations that involve other aspects of task content and that require far transfer on more of the dimensions highlighted by the simple framework detailed above. Further, many very different sorts of experience fall under the broad label of “video game” (Klopfer, Osterweil, & Salen, 2009) and a wide variety of people play these games, from stereotypical gamers, who dedicate countless hours deeply immersed in their games, to more casual players who play when they happen to be bored, and others who use games as a way to interact with friends. Although the dedicated gamers often come to mind when video games are mentioned, they only represent 11% of players, according to the researchers. Further, multilevel first-person shooter games with lengthy plots and complex graphics, played on a dedicated gaming platform such as an Xbox, offer a very different learning experience from simple driving games, dance-step copying and music-playing games, basic sports simulations (such as Wii tennis), slower moving computer-based (p.27) simulations (such as managing a family of Sims or building an ancient civilization), socially interactive Internet games played within non-game-specific communities such as Facebook, and cell-phone-based digital versions of board and card games. Intentionally educational variants of these formats would likely offer very diverse learning experiences. These diverse learning experiences also would translate to very different transfer challenges. What all these games have in common is that they have a digital component. As a transfer challenge, some video games might have more in common with chess than with a first-person shooter game, while others might share skills in common with deer hunting. Transfer from different kinds of games needs to be assessed on a case-by-case basis. For all these various forms of learning games, understanding how these learning experiences transfer across the dimensions of content and context detailed earlier should allow us to better evaluate the utility of educational investment in video game learning
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