Seeing Change in Time: Video Games to Teach about Temporal Change in Scientific Phenomena

Seeing Change in Time 1

Running Head: Seeing Time

Seeing Change in Time: Video Games to Teach about Temporal Change in Scientific

Phenomena

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Abstract

This article explores how learning biological concepts can be facilitated by

playing a video game that depicts interactions and processes at the subcellular

level. Particularly, this article reviews the effects of a real time strategy game

(RTS) that requires players to control the behavior of a virus and interact with

cell structures in a way that resembles the actual behavior of biological agents.

The evaluation of the video game presented here aims at showing that video games

have representational advantages that facilitate the construction of dynamic mental

models. Ultimately, the article shows that when video games characteristics come in

contact with expert knowledge during game design, the game becomes an excellent

medium for supporting the learning of disciplinary content related to dynamic

processes. In particular, results show that students that participated in a game-

based intervention aimed at teaching biology described a higher number of temporal-

dependent interactions as measured by the coding of verbal protocols and drawings

than students who used texts and diagrams to learn the same topic.

Keywords: Video games, learning, biology, dynamic mental models, dynamic visual

representations.


Video games favor learning in scientific and professional domains (Clark et

al., 2011; Barab et al., 2007; Halverson, 2005; Nash and Shaffer, 2010; Shaffer,

2005; Shaffer and Gee, 2005; XXXXXXXX). They do so, in part, by promoting social

interaction and collaborative reasoning (Black & Steinkuehler, 2009), by providing

learners with agency and feedback opportunities, and by creating adaptive levels of

task demand (Gee, 2005; Gee, 2008). Video games, additionally, have

representational characteristics that enhance the cognitive representation of

certain situations. Particularly, video games include representations that are

dynamic and interactive; features that are beneficial to learning (Plass, Homer and

Hayward, 2009). To refine our understanding of the way that these characteristics

influence learning further, this study specifically asks whether or not video games

produce better cognitive representations of temporally-dependent events than

traditional (print media) educational resources. This question is framed under the

idea that video game´s effects are produced in part through the formation of

perceptually-based representations, mental models (Johnson-Laird, 1983), that

traditionally have been defined as different than conceptual, propositional and

other non-perceptual representations (Anderson, 2005). To address this question,

this study compares the drawings and verbal protocols of students participating in

a video game-based intervention, and those of students that underwent an

intervention based on text and static diagrams . This study shows that games help

learners create robust mental models of scientific phenomena because of the way

that games favor the creation of dynamic representations that encode temporal

relationships.

This study draws on prior evidence that animated images help learners to


develop dynamic mental models (Boucheix & Guignard, 2005). The argument goes along

these lines: Different types of representations entail different cognitive

properties (Hahn & Kim, 1999; Johnson-Laird, 1998). For example, diagrammatic

representations index information by location while textual representations do so

by keeping a list of statements. For this reason, diagrams make spatial information

explicit and in so doing, reduce the cognitive search costs and allowing learners

to produce inferences directly (Larkin & Simon, 1987). This advantage makes

diagrams a better medium than text when the goal is to teach content that has a

strong dependence on spatial configurations.

Similarly, video games and simulations index information not only by location

but also by temporal contingency. That is, events that happen at the same time in

the phenomena are represented synchronously in the game. In this way, presenting

scientific phenomena through video games and other dynamic representations is more

congruent than presenting the same information through diagrams. Tversky, Morrison

and Betrancourt (2002) have proposed that cognitive processing is favored when “the

structure and content of external representations corresponds to the desired

structure and content of internal representations (p. 249)” and that there is a

natural cognitive tendency to prefer congruence between the event being represented

and the external representation being studied. They consider, therefore, that media

that offer dynamic representations are better for presenting temporally dependent

events because this type of representation has higher congruence with the to-be-

presented phenomena.

Additionally, animations help learners visualize dynamic scientific phenomena

because they produce a lower cognitive load when compared with a series of statics


pictures that require active reconstruction. Active reconstruction increases

cognitive load, particularly when users have to attend to signaling clues (e.g.,

arrows) and use them to interpret and integrate the corresponding mental model

(Hoffler & Leutner, 2007). In some cases, dynamic representations have been

considered as producing higher cognitive load (Lowe, 1999), but in most cases these

comparisons are based on a confounding factor because the dynamic representation

conveys more information than the static picture, ergo, the extra cognitive load is

intrinsic to the content and not produced by the type of representation. In other

words, when presenting the same information regarding temporal change, dynamic

representations seem to be more cognitively efficient than static ones. Further,

the interactive nature of video games can help learning by favoring engagement and

control over the task (Plass et al. 2009).

Finally, disciplinary content and principles can be integrated in the video

games mechanics, or the actions of the game and player (Clark et al., 2011),

thereby reducing the connections that are necessary in order to relate the

representation (the video game) with the underlying scientific models. Research in

basic cognitive psychology shows that when problem rules are implied in the

representational structure, cognitive load is reduced, thus increasing problem

solving rates (Zhang & Norman, 1994). Educational video games, such as the one used

in this study, combine both dynamic external representations, interactivity, and

integrated disciplinary content. For this reason, when designed correctly, they are

an ideal media to depict change in time in scientific phenomena. In fact,

literature shows that dynamic representations designed according with cognitive

principles produce higher learning gains and help learners to create mental models


of causal configurations involved in scientific models (Mayer & Chandler, 2001).

Similarly, recent reviews of research on games and simulations show they are

effective in the task of teaching science (Clark, Nelson, Sengupta & D’Angelo,

2009; Honey & Hilton, 2011).

Cognitive structure and perceptual representations

Sweller, Merrienboer and Paas (1998) describe the structure and function of

the cognitive system in order to support a theory of instructional design. They

base their theory on the canonical distinction between working and long term

memory. In their description of working memory, they focus on its limitations

(e.g., working memory constraints) and on the distinction between its visuo-spatial

and phonological components. For them, understanding instructional material depends

on cognitive load imposed on working memory. Low cognitive load allows

understanding of incoming information, and its translation to long term memory

schemas. They describe three types of cognitive load that intervene in the process

of learning: 1) Intrinsic cognitive load that corresponds to the difficulty of the

content at hand as measured by the number of elements that need to be learned and

their interactions; 2) Extraneous cognitive load that is produced by the

instructional design and other factors not related to the content to be learned

and; 3) germane cognitive load that represents the cognitive load directed to the

production of schemas and related to meaningful engagement in learning. Regarding

long-term memory, they describe the process of learning as the creation and

automation of an increasing number of schemas. Schemas are non-perceptual

representations that depict the configuration of the world (Anderson, 2005). While

we agree in the fundamental postulates of dual processing and schema constructions,


we also know that schemas are not the only type of mental representation used by

learners (Vosniadou & Brewer, 1992). Additionally to schemas, people support

reasoning on perceptual based-representations, among which mental models are

considered key in the process of learning. It is important to note here that the

term “mental model” is used here to refer to a specific type of perception-based

knowledge representation as it is used in cognitive psychology (Anderson, 2005;

Johnson-Laird, 1980), and not in the sense that is often is used in the literature

to refer any knowledge representation (e.g., scripts, schemas). It is important

also not to confuse mental models with image schemas (Johnson-Laird, 1983; Lakoff,

1987) because this second term refers to embodied pre-linguistic structures that

allow conceptual mappings in peoples experience.

Regarding the distinction between perception-based representations, it is

important to note that this idea is consistent with cognitive theories of

multimedia learning (Mayer, 2005) that divide knowledge representations in visual,

ergo perceptual, and verbal representations that do not preserve analogical details

of the situation being described. Mayer and Sims (1994) have proposed that

multimedia can favor learning because it allows learners to conduct dual coding of

instructional materials. In the dual coding theory of multimedia learning, people

use information coming from visual and verbal information. This type of

presentation has two basic advantages. First, it allows learners build multiple

representations of the same phenomena (via verbal and visual encoding) and to

elaborate referential connections between both types of representations (Mayer,

2005). Second, multimedia presentation, when designed correctly, decreases

cognitive load by exploiting different processing channels, particularly the


phonological loop and the sketchpad components of working memory (Mayer, 2005;

Mayer & Moreno, 2002; Mayer & Moreno, 2003). Mayer has shown the advantages of

multimedia presentations and identified the learning conditions under which it is

more successful. For example, Mayer (1997) found that when presenting subject

matter, coordinating animations with audio narration produced better results than

using narration alone, and that coordinating illustrations with text produced

superior learning results than using text alone for similar transfer tasks (e.g.,

producing creative solutions).

In Mayer´s view, learners using multimedia build a more robust mental

representation of scientific phenomena because they store information in different

modalities. In particular, according to this theory, learners build both a verbal

and a visual representations of events and they also construct a strict set of

referential connections between both types of representations. This set of

referential connections is composed by one-to-one mappings between words and visual

elements (Mayer, 1997). These representations and connections are built through

several complementary cognitive processes that focus on selecting relevant images

and words, organizing both images and words into coherent mono-modal

representations, and then integrating pictorial and verbal representations into a

coherent representation (Mayer, 2005). The Cognitive Theory of Multimedia Learning

(Mayer, 2005) is consistent with contemporary cognitive theories that consider that

conceptual contents are cross listed across different sensory modalities (McNorgan,

Reid & McRae, 2011; Schraw, 2006), and with recent views on conceptual change that

propose that, in the understanding of scientific phenomena, both visual and verbal

elements are integrated in a complex system that acts as a framework.


This article focuses on the visual component of Mayer´s Cognitive Theory of

Multimedia Learning. Our question is whether animations, simulations, games and

other dynamic representations are better than static pictorial media in creating

individual’s representations of temporally-based events, particularly, by favoring

the formation of perceptually based mental models. In other words, we want to

evaluate the effects of video games in the construction of representations of

temporal change within the visual component proposed by Mayer. We adopt the

perspective that dynamic information in this component is stored in the form of

dynamic mental models, or models that use perceptual representations to capture

temporal change in a system and indicate the underlying causal relationships that

determine its evolution (Johnson-Laird, 1983).

Current examples of games for science education

There are a large number of research agendas focused on the potential of

video games as educational tools. Video games can intervene in educational

processes in several different fashions. Games have been used as tools to promote

embodied participation in learning within a multi-user environment (Barab et al.,

2007). Such is the case of Quest Atlantis, a game environment that uses

characteristics of massively-multiplayer online role-playing games to promote

learning through the completion of educational challenges. The underlying

pedagogical model of the game is that by allowing students to assume roles within a

simulated world, Quest Atlantis allows them to connect their identities with the

social meaning of scientific activity. Barab et al. (2007) showed that this type of

intervention had significant effects both on multiple-choice questions, on

argumentative practice and on the use of different types of representation of


scientific data. The River City project uses a similar strategy by creating a

virtual world in which students can interact with other game characters, including

avatars assigned to instructors, other players, and artificial intelligence

characters. The basic task of the game is for learners to discover the form of

transmission and the treatment of an illness affecting the residents of a town. In

order to achieve this goal, participants have to produce data by creating

experiments and taking data samples using different resources available to them in

the virtual world. The presentation of these scientific tasks as students interact

within a Multi-User Virtual Environment (MUVE), has effects on students´ self-

efficacy, motivation, content understanding, and hypothesis generation (Ketelhut,

Dede, Clarke & Nelson, 2007).

These two projects use the potential of video games, particularly the

characteristics of multi-user environment role-playing mechanics, to supports

students’ content learning. In this case, games embed disciplinary concepts by

presenting scientific challenges within the virtual world. A different level of

pedagogical effects comes from games that use the potential of video games to

integrate scientific concepts within their basic design. In this case, the

educational advantage of video games comes from the integration of game mechanics

and formal scientific representations and concepts (Clark et al., 2011). One

example of this second type of game is the SURGE project. In the SURGE game,

principles and representation of physics concepts (e.g., gravity, vectors) were

integrated in the game mechanics allowing students to connect their actions in the

game with the underlying scientific concepts in the domain of physics. This

strategy produced significant positive effects on the levels of engagement with


content and on the understanding of the domain as measured by valid standardized

questions (Clark et al., 2011). The SURGE project shows clearly that games can act

as modeling tools that increase the understanding of physics by integrating formal

principles and representations of concepts in the game mechanics.

There is a blurred boundary between games and simulations in that both allow

students to manipulate models (Clark et al., 2009). This similarity makes it

possible for game designers to present actual disciplinary phenomena and to use

games as models of scientific processes (XXXXXXXX). Video games act as models that

represent actual or fictional worlds by connecting functions and structures given

certain parameters. In the context of games, learners can observe and intervene in

the interaction of several components according to principle-based mechanisms. In

this same context, students can also test and predict the behavior of micro-worlds

according to the models they have created to explain the game. Games can present

phenomena that are not directly observable and they can show how complex systems

are described using simplified models. Games, in fact, provide learners with

excellent opportunities to experience model-based reasoning (XXXXXXXX; XXXXXXXX;

Steinkuehler & Duncan, 2008).

Model-based reasoning is fundamental for scientific practice and for the

understanding of science (Clark & Segupta, 2013; Stewart, Cartier & Passmore,

2005). Models capture deep features of phenomena and separate signal from noise.

They are also simplifications of actual phenomena that make reality cognitively and

theoretically manageable. Model-based reasoning is essential to scientific

expertise because scientific thinking grows in part from the ability to distinguish

core features of the phenomena from superficial ones (Chi, Feltovich & Glaser,


1981). Normally, differentiating between deep and superficial features is

challenging for novices, and requires an extensive knowledge base to be properly

conducted. Games help to develop familiarity with models of real or fictional

worlds. Even when games do not model actual events, gamers conduct complex

calculations to understand which underlying model can better describe the behavior

of the system (Steinkuehler & Duncan, 2008).

Advantages of simulations, animations and other dynamic representations for

learning

Contemporary literature in science education and simulations focuses on the

effects of simulations on motivation, epistemological understanding of science, and

conceptual understanding of scientific topics (Honey & Hilton, 2011). In terms of

conceptual understanding, simulations and games have been shown to be effective

educational tools. This effect comes in part from the fact that simulations can be

used as models of scientific phenomena at different levels. For example,

simulations have been used to facilitate scientific inquiry in virtual environments

by acting as laboratories in online courses, and to reframe misconceptions of

disciplinary content by allowing the exploration of correct versions of scientific

phenomena or connections between different description levels (Evans, Yaron, &

Leinhardt, 2008; Meir, Perry, Stal, Maruca & Klopfer, 2005; Sengupta & Wilensky,

2009). In a similar fashion, games and simulations have been shown to produce

positive learning effects in different domains including electronics (Greenfield,

Camaioni, Ercolani, Weiss & Lauber, 1994), microbiology, (Miller, Moreno, Estrera &

Lane, 2004), epidemiology (Colella, 2000), genetics (Klopfer, 2008), physics (Clark

et al., 2011), and environmental science (Moreno & Mayer, 2000). These gains are


measured in different ways (e.g., knowledge assessments, prediction, disciplinary

reasoning level, problem solving transfer) and different types of dynamic

representations are used in the studies, ranging from simulations to agent based

and conceptually integrated games. However these results show that dynamic

visualizations overall present advantages for teaching and learning of scientific

content.

We consider that games have two basic representational features that give

them advantages over static representations. First, they are dynamic in nature, and

as other dynamic representations (e.g., animations, simulations) they are better

suited than static media in representing dynamical phenomena (Tversky et al.,

2002). Second, games are interactive, favoring the representation of causal

mechanisms and increased user control. Regarding dynamic representations,

literature shows that animations produce learning outcomes that are superior to

static representations (Barak & Dori, 2011; Hoffler & Leutner, 2002). However, the

superiority of animations are balanced with cognitive processing constraints

(Chandler, 2004) and for this reason, require nuanced design characteristics.

Dynamic visual representations are superior when they have a representational role.

That is when, “the topic to be learned is explicitly depicted in the animation”

(Hoffler & Leutner, 2002; pp. 727), but not when they are decorative in nature. On

a related note, Tversky et al. (2002) suggests that schematic dynamic

representations should be superior when compared to realistic (but static)

representations. Additionally Plass et al. (2009) have proposed that dynamic

representations work better when they include feedback and progression in the

complexity of the simulation. Dynamic representations are also better when learners


are allowed to manipulate the content of the simulation, when they are interactive

(increasing germane load) (Bodemer, Ploetzner, Feuerlein & Spada, 2004) and when

users engage in active content exploration (Lowe, 2004). Animations can be

isomorphic to dynamic phenomena (Hegarty, 2004; Lowe, 1999, Lowe 2003) and this

characteristic can be used to improve students’ mental models for this type of

phenomena. In particular, dynamic representations are useful for correcting

novices’ inaccuracies in certain scientific domains and to help them build more

differentiated knowledge structures regarding domain-specific content. These

effects however are restricted to perceptually salient aspects of the

representations (Lowe, 2003). Dynamic representation effects depend also on the

individual characteristics of learners. For example, pure animations facilitate

cognitive processing for learners with low prior knowledge and cognitive skills,

and dynamic displays that allow manipulation favor learning for students with high

cognitive prerequisites (Schnotz & Rasch, 2005).

For the case of video games, the effects of interactivity on the

comprehension of dynamic representations need to be also considered. Literature

shows that interaction helps learners through several mechanisms related to

cognitive processing. Namely, interaction helps learners pace the process of

learning, decreasing extraneous cognitive load (Mayer & Chandler, 2001; Schwan &

Riempp, 2004), and increasing germane cognitive load (in the case of user control

beyond pacing, for example when participants can manipulate the content of the

simulation). However, this second effect seems to be only present in users with

high-executive functions (Plass et al., 2007). For this reason, video games that

provide extensive interaction opportunities can amplify the beneficial effects of


animations and dynamic visual representations, and help in the formation of dynamic

mental models.

While we know that video games and simulations produce an increase in

conceptual understanding (Clark et al., 2009; Honey & Hilton, 2011) and we have a

reasonably good idea of which instructional and design characteristics potentiate

the learning effects of simulations and other dynamic representations (Moreno &

Mayer, 2000; Plass et al., 2007; Tversky et al., 2002), we do not know how

cognitive structures change with the interaction of games and simulations,

particularly at the perceptual level (e.g., visual). In this line, a recent report

in games and simulations suggests that “research should examine the mediating

processes within the individual that influence science learning with simulations

and games. This research would aim to illuminate what happens within the individual

—both emotionally and cognitively—that leads to learning and what design features

appear to activate these responses.” (Honey & Hilton, 2011, pp. 122). Similarly,

the development of internal visualization skills has been singled out as an

important educational goal in order to amplify the potential of different types of

graphic representations (Hegarty, 2004). Though differences in learners’

performance have been measured by multiple choice questions and other types of

tests, more needs to be known about the perceptual mechanisms driving this change.

That is, while it is clear that games can act as modeling tools and as models of

scientific phenomena, it is not clear whether the changes they produce are related

to perception-based mental representations.

Anderson (2005) separates perceptual representations from other mental

representations of knowledge. Both conceptual and propositional representations are


characterized by abstracting the meaning of experience and completely eliminating

the perceptual details of the situation. Perception-based representations on the

contrary represent the situation (or part of it) in terms of analog perceptual,

often visual, configurations. In fact, research shows that under certain

circumstances tasks that require inferences activate brain areas devoted to visual

processing (Goel, 2005). The point here is that research shows that video games

increase learning performance and modify non perceptual representations by, for

example, making more complex epistemic networks (e.g., conceptual maps) in a given

domain (Shaffer et al., 2009). However, other learning mechanisms related to the

modification of perception-based representations have not been explored. The

importance of these representations lies in the fact that many inferential and

comprehension processes depend on them. For example, having a conceptual network of

a car engine does not fully make people able to repair one – understanding the

actual physical and temporal configuration of an engine is also essential. In that

sense, presenting a video or simulation of a car engine can be more effective than

providing a text describing the process. This article aims at characterizing the

differences in representation produced by a video game, beyond the conceptual,

propositional or verbal level, by including evaluations of the perceptual level of

mental representation. To do that, literature describing different types of

perceptual-based representations are reviewed and then evidence from actual

students´ products is presented to show that some characteristics of these

representations are modified through video-game play.

Dynamic Mental Models

Mental models can be built by propositional integration, analogy, and


observation of external representations or events (Collins & Genter, 1987; Johnson-

Laird, 1998). They are analogical representations, perceptual in nature, that

reproduce the configuration of external situations, allowing learners to conduct

inferences and solve problems. Mental models operate on tokens that are analogous

to the elements involved in the situation being represented. In this way, mental

models can represent the properties of individual elements and the structural

relationships that govern their interactions. Additionally, mental models allow

cognitive representation of other properties in the model via annotations on the

elements (e.g., representing negation; physical properties). Video games that

depict dynamic processes can act as external representations that guide the

formation of mental models of those processes. Although internal mental models do

not encompass all problem-solving (Johnson-Laird, 1998) and the role of distributed

processing has been extensively highlighted (Zhang, 1997), mental models at a basic

level provide advantages for learners. Mental models facilitate processing and

integration of new information and they mediate problem solving in tasks that

require inferential reasoning.

There are several types of mental models. Some of them, called images and

spatial mental models, depict static configurations of elements while others,

called relational mental models, define entities, properties and relations that are

somehow operable but that do not explicitly change in time. This study focuses on

mental models that include change in time. Johnson-Laird (1983) defines three types

of models that include time-changing configurations: temporal, kinematic and

dynamic. Temporal mental models consist of a sequence of spatial models (divided in

several frames) that follow a fixed sequence. Kinematic models are similar to


temporal models but they are psychologically continuous. Finally, dynamic models

are similar to kinematic models but they are able to encode causal beliefs that

complement the spatial configuration of elements presented in the model (Johnson-

Laird, 1983). The distinction between the different types of mental models is not

trivial. While temporal and kinematic mental models follow relatively fixed

sequences. Dynamic mental models can be sensible to beliefs about causal and

physical relationships. Research shows that people predict the behavior of systems

(e.g., body and objects movement) in ways that go beyond the predictions of pure

spatial configurations and are influenced by physical beliefs about the functioning

of the world (Hubbard, 1995; Shiffrar and Freyd, 1990). In other words, dynamic

models allow the introduction of different types of constraints within visual

imagery (Schwartz, 1999).

Having a dynamic mental model has an advantage not only relative to verbal

representations of content, but also relative to visual representations that do not

change flexibly in time. By using a video game, students can learn not only spatial

and functional configurations (e.g., a cell´s model), but also the constraints on

the interactions among the elements included in the model. This dynamic mental

model will, in turn, facilitate students’ understanding of the flexible behavior of

dynamic processes. For example, observing that the entrance of a virus in a cell

depends on the matching of the virus capsid´s with the cell membrane receptors will

allow students to visualize different configurations through which this process can

happen. Students will be able to understand that the virus does not need to use the

same receptor every time, but that certain conditions need to be fulfilled every

time (e.g., successful evasion of antibodies, correct matching of membrane


receptors). Having a dynamic mental model is useful to understand how flexible

interactions at different levels are related (Frederiksen, White and Gutwill, 1999;

Gutwill, Frederiksen and White, 1999).

The role of mental models in problem solving has been clearly established in

cognitive psychology. Johnson-Laird (1980) showed in several studies how mental

models, that is, perceptual representations of reality or discourse, can be used to

solve problems and derive conclusions from a set of premises. When solving simple

deductive reasoning tasks (e.g., syllogisms), people represent premises in the form

of tokens, and use these models to answer the problems (Johnson-Laird, 1995; 1998;

1999). Mistakes are made when the memory resources necessary to finish the search

and keep the model in memory are exceeded. Cognitive theories of reading also rely

on mental models, called situation models, as basic mechanisms in text processing

(Kintsch, 1998).

Mental representations in science education

Conceptual change can happen at different levels: One level is belief

revision. At this level, incorrect ideas regarding certain scientific phenomena are

reviewed based on new information. A second level is the level of mental model

transformation. At this level, coherent but incorrect mental models are reviewed

after students experience a holistic confrontation with correct models. That is, a

situation where students are presented with a new model as a whole. Finally,

conceptual change can be achieved via ontological shifts, that is the assignment of

certain phenomena to new lateral categories (Chi, 2008). This article does not

focus on conceptual change, but the three levels of conceptual change proposed by

Chi (2008) are an adequate framework for understanding the initial process of


mental representation of scientific phenomena. People can learn beliefs about the

nature of phenomena, probably by verbal representations; they can also create new

categories to which incorporate existent scientific descriptions by modifying

categorical structures; or they can create mental models of the configuration

described by scientific theories. Pedagogically, we are not trying to replace an

incorrect model with a new one, rather are trying to create an initial model of a

biological phenomenon.

Vosniadou (2002b) has proposed that scientific knowledge should be described

as a complex system that includes several types of elements, including perceptual

information. In this context, both perceptual information and other types of mental

representations (e.g., verbal representations) are part of explanatory frameworks

that learners use to make sense of content. Children start the process of learning

by organizing perceptual experience into conceptual structures. Learning of

scientific concepts from this point of view is a process of assimilation of new

elements (more sophisticated scientific theories) within existent explanatory

frameworks. Vosniadou´s position is interesting for the goals of this study because

it assign a role to perceptual elements, and the perceptual mental representations

derived from them, in the process of conceptual change. From this point of view,

having adequate previous perceptual representations can favor the development of an

adequate scientific comprehension. For example, having an external representation

of the earth as a spherical object, and the corresponding mental model will make it

easy for students to integrate verbal information regarding the “roundness” of the

earth (Vosniadou, 2002b; Vosniadou, Skopeliti & Ikospentaki, 2005). Additionally,

Vosniadou et al. (2005) considers that having a mental model, a perceptual mental


representation preserving the structure of the natural world, is fundamental for

capturing the characteristics of the world and producing inferences and generative

questions.

In fact, research in conceptual change shows that dynamic external

representations, as video games and simulations, can fulfill a facilitating role in

the process of conceptual change (Chi, Roscoe, Slotta, Roy & Chase, 2012).

Simulations can help to make relationships visible in certain types of phenomena

(e.g., emergent) and improve the learning of scientific phenomena when accompanied

with adequate prompts (e.g, highlighting the way the behavior of different levels

of a system are related, or specifying the characteristics of the interaction among

agents in each type of process). In this study, viral reproduction is the phenomena

of interest, and we consider that the use of dynamic external representations (i.e.

within the game) can facilitate the learning of the mental models required to

achieve a comprehension of the material, especially with regards to temporal

components .

Viral reproduction as an excuse to teach genetics.

Viral reproduction is a topic within the domain of cellular biology that

includes the process of virus infection, replication and the immune system and cell

responses. This domain was chosen because it is critically important for

understanding the foundations of life. Cellular biology, particularly the

interaction between cell and virus at the molecular level, includes for example,

how genetic material is replicated and how its information is translated into

proteins that, in turn, control the cell´s functioning. Linking genetics to broader

phenomena allows learners to have an informed opinion on a variety of topics


including the ethics dilemmas of cell research, and the bases of ongoing research

regarding the origin and cure of multiple diseases. As a matter of fact, teaching

about genetics and about virus and cell structures, interactions and effects is

part of the American Association for the Advancement of Science Standards (AAAS,

2013), the National Science Education Standards (NRC, 1996) and the National

Standards of the Colombian National Education Ministry (MEN, 2006).

However, current research shows that genetics is a difficult topic to learn

(Chattopadhyay, 2004; Mills-Shaw, Van- Horne, Zhang & Boughman, 2007). Students

have deep misconceptions in this area and have problems understanding the genetic

basis of disease, the nature of genetic research, and the characteristics of genes

and genetic material (Wood-Robinson, Lewis and Leach, 2000). Many students, for

example, believe that lower organisms do not have DNA (Mills-Shaw, Van- Horne,

Zhang & Boughman, 2007). The difficulty in understanding genetics comes from

several factors: First, the phenomena are invisible and inaccessible. Second, its

understanding requires learners to coordinate representations at several

ontologically distinct levels. These levels include the information level in which

genetic information is stored and the physical level that emerges from that

information (e.g., proteins, cells, tissues and organs) (Duncan & Reiser, 2007).

Mapping across these levels is challenging because processes at the micro-level do

not have a one-to-one relationship with characteristics at the macro-level.

Multiple processes at the molecular level mediate the expression of the genetic

information and its translation into observable characteristics. Failing to

understand these processes makes it impossible for students to comprehend the

relationship between genotype and phenotype (Duncan, Rogat & Yarden, 2009; Lewis &


Kattmann, 2004), and leads them to believe that genes express traits directly

(Eklund, Rogat, Alozie & Krajcik, 2007). As a matter of fact, multiple researchers

have pointed out that not comprehending the role of proteins is an important

obstacle for a full understanding of genetics (Marbach-Ad, 2001, Eklund, Rogat,

Alozie & Krajcik, 2007). Any basic knowledge of genetics should include the idea

that information is encoded in the DNA, which can replicate itself using the

molecules available in the cell, and that DNA can be translated into proteins via

RNA. The game used in this study directly addresses these processes because the

game mechanics requires players to direct a virus through replication and to

respond to the cell´s reaction to that process.

The game also addresses other common mistakes held by students regarding cell

biology and genetics. Wood-Robinson, Lewis and Leach (2000) have shown that

students find it difficult to understand the function of cell structures, the

function of DNA and the replication process. In the same vein, most students do not

understand the role of RNA in the process of gene expression (Boujemaa et al.,

2010) and require instruction aimed specifically at correcting misconceptions

regarding the relationship between genes, DNA and chromosomes (Friedrichsen, Stone

& Brown, 2004). Students also have incorrect conceptions of mutations and their

consequences. For example, students believe that mutations consist of changes in

the form of a gene (e.g., “from circular to rectangular”) and believe that there

is a direct, one-to-one effect from mutation to phenotype and behavior

(Schwandewedel, HöBle & Kattman, 2007).

Game-like interventions in genetics education

In the case of genetics, games help learners to connect processes at the


information and physical levels, which constitute a necessary step for the full

understanding of the topic (Duncan & Reiser, 2007). In fact, research shows that

using 2D and 3D models of proteins and genetic material helps students to

understand the process of gene expression and to comprehend the process of

transcription and translation of genetic material (Eklund, Rogat, Alozie & Krajcik,

2007). At a different level, research shows that curricular interventions focused

on modeling patterns of inheritance help students develop skills for argumentation

and explanation within the domain. Particularly, teaching students about the models

that represent the molecular mechanisms involved in inheritance helps them to

understand several aspects of cell biology. For example, students that receive

instruction on the genetic model of inheritance were able to connect models of

inheritance (dominance, codominance) with events at the molecular level (e.g.,

alleles coding for proteins) (Stewart, Cartier & Passmore, 2005). In the case of

this study, the video game fosters better mental models of the genetic process and

allows students to see the invisible interactions that produce observable

consequences at the macro level. In this sense, the game is an excellent tool for

representing what is not visible and helping students build dynamic models of

invisible, underlying processes. The game depicts not only temporal change, but

also interaction constraints that are included in any multidimensional, dynamic

mental model (e.g., goals and causation). These constraints are represented in the

game because players have to attend to goals (e.g., reach a cell receptor/ get to a

ribosome to copy genetic material) and to causation (e.g., sending RNA-m to a

ribose will produce a copy of a specific protein). These constraints cannot be

presented in texts and graphs quite in the same way. Even if one introduces the


constraints via text, it is not possible to show how they operate in specific

temporal and spatial locations. Additionally, it is not possible to show how a

limited set of constraints produces flexible behavior in the form of multiple

possible outcomes. These representational advantages, along with the well-known

positive effects of video games in engagement, agency and identity (Gee, 2005; Gee,

2008; XXXXXXXX; Steinkuehler, 2006) create favorable conditions for the learning of

subject matter, or in the case of this study, the learning of cell biology and

genetics.

Even in contexts where there is a compulsory curriculum for science, many

students do not seem to understand how genetic information is transferred and are

unable to identify the structures that participate in this process (gene,

chromosome, cell). (Lewis & Wood-Robinson, 2000). The question then is how a better

understanding of genetics can be supported and what kind of interventions can be

used to achieve this goal. There are few examples of evaluations of video games

devoted to genetic content. The Federation of American Scientist has developed a

game called Immune Attack that requires players to train elements of the immune

system to respond to different types of infections in an virtual environment that

depicts the actual structure of body structures (e.g., limbic system). While Immune

Attack represents a very interesting design option, no comprehensive evaluations of

its educational effects are available. This game also focuses on the cellular

level, particularly on the interaction between macrophages and bacteria, but it

does not represent the molecular process involved in viral replication at the

subcellular level (http://www.few.vu.nl/~eliens/archive/science/p44-kelly.pdf).

Another game related with biology and genetics is“Fuzzies”. The goal of this video


game is to breed fictional organisms to produce an offspring with a given set of

characteristics. The game allows learners to observe the process of meiosis and to

see representations of traits in the genotype and phenotype. No significant effects

on enjoyment, engagement or learning were found (Gibson, Hu & Swast, 2010).

Similarly, a game that required students to conduct DNA fingerprinting to solve a

crime produced no significant effects on learning. In this case, however,

significant effects on engagement were found (Annetta, Minoque, Holmes & Cheng,

2009). Finally, the integration of genetic concepts at different levels (e.g.,

genotype-phenotype) has also been facilitated by the use of a research simulation

that required students to map a mutation and to compare normal and mutated alleles

in order to identify the causes of deafness (Gelbart & Yarden, 2006).

The game used in this study corrects several flaws in the design of these

experiences to increase its educational effectiveness. First, Virulent goes beyond

the relationship between genotype and phenotype. The basic reason for this decision

is that conceiving of genetics as a problem of trait expression overlooks the

molecular-level processes in which information contained in the genetic material

relates to the actual observable traits. This deficiency might create the wrong

idea that genes express themselves directly, making it difficult to understand,

among other things, that viruses can intervene in the signaling system of the cell,

blocking the expression of genetic information. We consider that this level of

description will help students to understand the complexity of gene expression that

is fundamental for a deep comprehension of genetics. Second, the game brings expert

knowledge to the table. Disciplinary experts participated in the design of the game

to assure the accuracy of the model presented. This decision was made because


literature shows that once learners have acquired an incorrect mental model of a

phenomenon, changing it is difficult (Vosniadou, 2002a). Third, Virulent provides a

game-like experience. Players do not only simulate phenomena by setting parameters

in an application; they actually have to conduct decision making related to the

interactions between virus and cell structures. The rationale behind this decision

was that there is a qualitative difference in how mental models are developed when

one sets parameters using a simulation and when one plays a video game. In the case

of simulation, the learners establish parameters through the simulation interface

and observe the results in the behavior of the system. In a video game, the learner

is an active agent in each step of a goal-directed process. For example, different

learning outcomes should be expected in a simulation that shows how different

levels of gas and oil affect car endurance and a game where the actual parts of the

engine interacting with the oil and gas values are visible, and the player must

decide, in each step of the process, the correct allocation of gas and oil. In

cognitive terms, we think this second approach creates a richer representation - a

dynamic mental model - of the process because learners encode the constraints of

the model. The game pushes players to learn the constraints of the model (e.g.,

goals and causation) by encoding them in the game mechanics. For example, gamers in

Virulent learn that sending an mRNA to a ribosome is a necessary step to synthetize

proteins, because he or she has to control that action within the game. Learning

the constraints of the model is fundamental in understanding that the video game is

more than just a visual representation of the viral reproduction process, and that

it can display a flexible behavior that emerges from a few basic principles. This

study evaluates the different mental models that arise from these design decisions,


showing that students that interact with a video game have a richer mental

representation that contains the dynamic characteristics of the model, particularly

including temporal organization and an awareness of model constraints.

Method

This study compared two learning conditions to which participants were

randomly assigned. The first condition used text and diagrams to explain the

process of viral infection and replication, as well as the basic genetic mechanisms

behind this process (Text-Diagrams condition-TD). The second condition used text

and game to explain the same process (Text-Game condition-TG). The text focused on

the polio virus and presented the steps through which the virus infects the cell

and replicates itself. The text was obtained from a reliable source in the topic

and was at an intermediate level. The topic was selected because the polio virus is

the type of virus depicted in the game (positive strand RNA virus). The texts in

the two conditions were identical. The difference between the two conditions was in

the added-value of games for the development of dynamic mental models, and its

consequences in propositional integration and learning from texts.

Procedure

The study was conducted within a unit of a biology class that covered four

weeks of coursework. It was divided in four weekly sessions. Prior to the study,

informed consent from parents was obtained. In the first session, students received

basic instructions regarding the study and the materials. Additionally they

attended a talk about the process of viral replication and about the cell

structures involved in it. This talk was designed to provide basic knowledge

regarding the topic, and to assure a similar starting point for both groups. In the


first session, students were not yet assigned to any condition. In the second

session, students were randomly assigned to the TD and TG conditions. Students in

the TD condition read a text that described the polio virus replication. This text

was taken from a website devoted to spreading scientific knowledge about the polio

virus, and it included a description of the steps through which the virus enters

the cell and how it uses the cell structures to make copies of itself.

Additionally, students in this condition observed several diagrams that depicted

the different steps in the process of viral replication and the virus and cell

structures involved in this process. The students were asked to study texts and

diagrams in pairs. They were instructed to talk to each other when they had a doubt

or comment. At the end of the session they were asked to draw a conceptual map

(Moon, Hoffman, Novak & Cañas, 2011) of the viral replication process. During the

second session, students in the TG condition read the same text and played Virulent

in pairs taking turns in controlling the game and reading the materials. They also

had access to a game manual that presented the game instructions in Spanish. This

manual was also available to students in the TD condition. Information contained in

both condition was standardized, meaning that students in both conditions had

access to the same information (e.g., same steps were presented in TD and TG). In

the third session, students in the TD condition built a representation of the viral

replication process in pairs using playdough. Students in the TG condition

continued playing Virulent. In the final session, students in both conditions were

asked to draw the viral replication process while thinking-aloud. Protocols were

transcribed and coded. After that, students in both groups played Virulent to give

students in the TD condition the opportunity to play the game.


Participants

Participants in this study included 86 students between the ages of 9 and 11.

Students were enrolled in primary school (5th grade equivalent to 6th grade in the

US) in a private institution in a large South American city. By their geographical

location in the city, they were identified as middle class students and most of

them had access to computers and Internet at home. All participants were Spanish

speakers. According to national standards, students in fifth grade should know the

parts of the cell and their functions. However, at the time of the intervention,

students had not yet started the cell unit, and the intervention was designed to

promote that learning. Participation in the study was part of the standard

instruction students received in natural sciences at their school. Complete data

was obtained only for 82 students due to different logistic factors (e.g.

absenteeism).

Task and coding

Students were asked to draw a graph describing the process of virus

infection and replication while thinking-aloud. This task was chosen because the

analysis of thinking-aloud protocols has been shown to be an adequate tool for

study reasoning and problem solving (Ericsson & Simon, 1993) in general and mental

model formation in particular (Clement, 2008). The basic rationale behind the

method was that drawings and thinking-aloud protocols would make evident the

difference in the mental models being acquired by the students in way that other

methods that rely in multiple choice answers could not capture. In fact, drawing

has been used to capture the effects of dynamic representations on learning because

they are closer to both the form of presentation (Lowe, 2003) and the natural in


the representation of dynamic processes (Tversky, 2005).

Data analysis protocols were coded to identify the number of temporal

organizers and genetic mechanisms mentioned by students. Temporal organizers were

coded when the protocol included temporal adverbials or prepositions that evidenced

the segmentation of the process in steps and sub-steps (First, when, after). The

use of temporal organizers, phrases, and adverbs has been linked to the presence of

mental models that describe situations that change in time (Carreira, Carriedo,

Alonso & Fernández, 1997; Garham, 2001). The number of temporal organizers was then

calculated for each protocol. Genetic mechanisms were defined as interactions between

at least two cell or virus structures that had a function in the viral replication

process or in the cell´s functioning and defense. Coding temporal organizers was a

proxy to evaluate whether or not the students developed a dynamic model of the

viral replication process. Genetics mechanisms allowed us to see how well students

understood interactions at the micro-level, the constraints of those interactions,

and their functions in the models presented in the intervention. In order to assess

reliability, 36 protocols (46.75%) were coded by a second independent researcher.

For temporal organizers, the second coder marked all the candidate words and

segments that could belong to the category, including words that could be confused

with temporal organizers (e.g., “very fast”, “quickly”). Reliability was then

calculated for that library of codes by comparing the original coding with the

coding produce by the second researcher. For temporal organizers, inter-coder

agreement was 94% (Kappa=.807). To calculate reliability for genetic mechanisms,

the library of codes was obtained by identifying candidate segments representing

elements interaction. In that case, inter-coder agreement was 92% (Kappa=.662).


Drawings were coded to assess the complexity of the representation students

obtained from the intervention. First, drawings were classified by whether or not

they contained written explanations of the process (not required in the task

instruction and therefore produced spontaneously by students). Second, drawings

were categorized by the presence or absence of actions. Actions were defined as an

activity of an element (e.g., DNA) implying change in time. Third, drawings were

coded to capture whether or not they described a process, defined as a sequence of

concatenated actions. Fourth, drawings were coded to identify whether or not they

evidenced the understanding of resources (e.g., energy) as constraints for the actions

of the elements in the process. Fifth, drawings were coded by whether or not they

presented narrative elements, a story-like depiction of the viral replication process.

Finally, the number of levels depicted by students was counted for each drawing. In some

cases, for example, the drawings presented the image of an infected person, and the

image of the cell level at which the virus was acting. This drawing was coded as a

two level drawing. The possible levels were population level, person level, cell

level, and molecular level. Additionally, drawings were coded to reflect whether

they presented an integrated view of the viral replication process. This category was

created to control the fact that the game presented the viral replication process

through several sub-games (levels) and we needed to be sure that students

understood that levels were part of continuous sequence. A second researcher coded

all the drawings. Inter-coder agreement ranged from 91 to 99% (Kappas from .661

to .964).

Game Design: Virulent

The game was designed by a team of experts in several fields including


education, game design, and computer science (Available at

http://www.eriainteractive.com/project_Virulent.php). One of the goals of the

design process was to apply first-hand disciplinary knowledge to the design of the

game. For this reason, experts in molecular biology were included very early in the

design process. Several cycles of design and evaluation were conducted in order to

make the game as accurate as possible with regards to the process of viral

replication. In these cycles, design experts met and created prototypes that were

evaluated by disciplinary experts. Then, modifications were conducted according to

the experts’ feedback. The game depicted a positive strand RNA virus and did not

delve into the differences among different types of viruses. The general idea was

to capture the process of virus replication and the cell responses to it.

Disciplinary experts were incorporated in the design process in order to present an

accurate version of the viral replication process. This decision was made because

it has been shown that media frequently present incomplete or wrong versions of

genetics (Mills-Shaw, Van-Horne, Zhang & Boughman, 2007), and that learning of

incorrect mental models problematize the learning of new content by interfering

with the formation of new mental models (Vosniadou, 2002a).

The game design also attended to the characteristics of games as learning

tools. At the cognitive level, game sequences were built in such a way that players

had access to task goals presented aurally. This feature made the game design

consistent with multimedia learning theories that recommend use of dual processing

channels (Clark & Mayer, 2009). Players had to perform well-defined tasks within

ill-defined tasks (Steinkuehler, 2006). For example, players had to find a receptor

in the cell’s wall that matched the receptor in the virus capsid. These well-


defined tasks were embedded within ill-defined tasks that required players to

direct the process of virus infection and replication through multiple possible

paths. There was no right answer for the virus replication. Players could for

example try to protect the genome by moving it around or by placing proteins around

it. The well-defined tasks provided contingent feedback while the ill-define tasks

provided students with a space to integrate knowledge in a complex problem-solving

situation. The game was designed to make the content regarding cell and virus

structures situated and relevant to tasks goals (Gee, 2008). The game was

structured so that a better knowledge of cell structures and functions and of the

virus replication process would allow players to perform better. For example,

understanding the function of ribosomes helped players to quickly identify where

they should go once they enter the cell. The game used all these characteristics to

present a model of viral replication that made explicit both spatial and temporal

relationships. This model also showed the relationship between the different

ontological levels of genetic replication (Duncan and Reiser, 2007).


Fig. 1 Snapshot of virulent!

Virulent was conceived as a Real Time Strategy game in which players have to

direct a virus during the process of viral replication. The game was initially

organized in 10 levels, each of which representing a sequential challenge

associated with a part of the viral replication process. For example in the first

three levels, the virus has to avoid the b-cells and other body defenses before

getting into the cell. In the next level, the player has to find a receptor

matching the structure of its own external membrane in order to be able to enter

the cell. In the following levels, the virus enters the cell and is decomposed into

its basic parts (i.e., the genome and different types of proteins). In the

following levels, the player has to conduct several interrelated tasks that match


the process that actually happen during viral replication. For example, the player

has to take the virus components close to the mitochondria to get energy to carry

out other processes (figure 1). Once the player has reached the mitochondria, he or

she can create different types of signals by clicking on the genome and selecting

one type of signal from a pop up menu. Each signal has a specific function. When a

particular signal is sent to the ribosome, for example, the ribosome produce a

specific type of protein. Each protein, in turn, has a specific function within the

viral replication process. Some proteins are used to create a protective membrane

on the genome, while other proteins are used to block cell signals, interfere with

cell defenses, or create changes in the genome that allow players to create an

exact copy of the spiral that represents genetic information. Other proteins are

sent to the Golgi apparatus to create a membrane for the copies of the virus that

are being created. It is important to note that the player has to control all these

processes, from picking the signals to directing the proteins and the genome to

different cell locations (e.g., nucleus, ribosomes, Golgi apparatus), according to

their functions. The description of this process was as accurate as possible to

the actual process of a negative strand RNA virus. When a player clicks on an

element on the screen (e.g., a protein), the name and function of the element is

presented through audio. In this sense, this game can be considered as a

conceptually integrated game (Clark et al., 2011) because disciplinary content is

integrated in the game mechanics. In order to be successful in the game, the player

has to control the viral replication processes in the same way in which this

process happens during actual viral replication. That is, all actions in the game

match actual actions in the scientific phenomena. The levels increase in complexity


until players achieve the goal of creating several copies of the virus.

The design of the game addressed different standards for students in this age

group. The AAASs standards, point out in section 6, the Human Organism, that

students in Grades 6 through 8 should understand that viruses cause illness by

interfering with the functioning of the organism (AAAS, 2013). Additionally, in

section 5, the living environment, the standards include for students in the same

grades, the comprehension of the role of genetic information in the transmission of

traits. On the other hand, the NRC´s Standards point out that students in grades 5-

8 should know the basic characteristics of the cell and other microorganisms (NRC,

1996). They should know particularly that cells produce and use materials to keep

the functions of the organism. Additionally, these standards specify that students

in this age group should know that each cell contains genes that store the

hereditary information, and that organisms respond to the environment at many

levels including the cellular level. The video game used in this study covers many

of these aspects. By controlling the virus through replication and interaction with

the cell, players learn about the cell structures and the processes involved in the

cells metabolism and responses. Additionally, they learn about the micro level

interactions involved in the transmission and expression of genetic information;

for example, by controlling coding of proteins in the ribosomes or the copy of the

genome. Additionally, the game prepares students to learn more complex information

about molecular process as suggested by the NRC standards.

Results

This section compares the performance of students in the TD and the TG

conditions. The results are organized around the codes obtained from thinking-aloud


protocols and from students’ drawings. In each section, examples of students’

behavior and products are presented to illustrate the effects of interacting with

the Virulent. These examples were chosen because they show how the game created

different patterns of representation of the viral replication process when compared

with traditional learning tools.

Thinking-aloud Protocols

Students in the TG condition produced more temporal organizers when

describing the process of viral replication than students in the TD condition

(Figure 2). While students in the TG condition mentioned 1.86 temporal organizers

per protocol, students in the TD condition mentioned 1.05 temporal organizers per

protocol. This difference was found significant using a Welch´s test, F(1, 58.137)

= 4.296, p<.05. This test was used because the Levene test applied to the data

showed significant unequal variances in both groups (p<.05). The difference in the

number of temporal organizers indicates that, on average, students in the TG

condition developed a more dynamic representation of the viral replication process

than students in the TD condition.


Text-Diagram Condition Text-Game Condition0

0.20.40.60.81

1.21.41.61.82

Numb

er o

f Te

mpor

al

Orga

nize

rs

Fig. 2 Temporal organizers in protocols per condition

Results also show that there was a significant difference in the number of

genetic mechanisms mentioned by each group. The TG condition verbalized 3.57

mechanisms per protocol, while the TD condition verbalized just 1.45 mechanisms per

protocol (Figure 3). A Levene´s statistic found that variances of both groups were

unequal (p<.01), and therefore a Welch´s test was used to assess the statistical

significance of the mean differences between both groups. The Welch´s test showed

that the differences were significant, F(1, 47.879)=11.05. A higher presence of

genetic mechanisms is evidence that students in the TG condition had a stronger

understanding of the genetics involved in the viral replication process, and that

they learned the constraints of a dynamic mental model of viral replication through

interaction with the game.


Text-Diagram Condition Text-Game Condition0

0.5

1

1.5

2

2.5

3

3.5

4

Numb

er o

f Ge

neti

c Me

chan

isms

Fig. 3 Genetic Mechanisms in Protocols per Condition

These differences indicate that using video games to support learning has

advantages in comparison to using traditional pedagogical resources that rely in

static representations of content (e.g., text and diagrams). When compared with

students in the TD condition, students in the TG condition conceived the process of

viral replication as a sequence of steps, which, in turn, indicates the presence of

dynamic models that encode the temporal relationships and constraints involved in

it. The following examples illustrate how these differences are evidence of a

superior understanding of the subject matter. In many cases, the presence of

temporal organizers and genetic mechanisms was interwoven in students´ explanations

(Table I). That is, having dynamic mental models, evidenced by the number of

temporal organizers, was associated with the presence of disciplinary knowledge

measured by the number of genetic mechanisms. In the following example, it is

possible to see how temporal organizers (in gray) segment the sequence of genetic

mechanisms (represented by numbers). In this example, a student in the TG condition


represents a fragment of the viral replication process using three genetic

mechanisms and divides it using seven temporal organizers. More importantly, the

temporal organizers mark the start of the interactions that define the genetic

mechanism. In this way, they give order to the sequence of events involved in the

viral replication process. This type of explanation was produced by 25.3% of the

students in the TD condition and by 65.4% of the students in the TG condition.

Table I. Dynamic Mental Model grounded in Disciplinary knowledge

1. Well, first the ARN comes in a nucleocapsid, that is made of proteins, and

it goes through the cell…the cell´s wall, and then it gets in and starts to

transport itself, and it keeps transporting itself,

2. and then it breaks the nucleocapsid and the ARN gets free, and at that

moment it start replicating itself and…. it is like it cannot replicate…

3. then it starts to replicate and to create a new nucleocapsid and the RNA

is made, and it starts to expand.

In certain cases, however students acquired a dynamic conception of the

process (high presence of temporal organizers), but they did not link those models

to disciplinary knowledge (Table II). In the following example, from a student in

the TG condition, it is possible to see how the student learned a certain sequence

of interactions but was unable to relate those interactions to core disciplinary

knowledge. In this case, the student grasped some disciplinary knowledge: the need

for energy in the process and the fact that the cell uses certain elements to

protect itself. However, the student did not connect most of the elements in the

model (e.g. blue little thing) with disciplinary relevant elements (e.g.,


ribosomes). For that reason, in the coding of this fragment, four temporal

organizers were identified but no genetic mechanisms were included. This type of

explanation was produced by 19.5% of the students in the TD condition and by 5.7%

of the students in the TG condition.

Table II. Dynamic Mental Model without Disciplinary knowledge

Then, that energy makes possible for it to produce this, and it takes it to the

blue little thing with a little hole, and from that point on a little triangle goes

out, then that little triangles comes back and turns the little thing where it was

produced, and it covers it with a layer that protects it from some spiny little

things that are the protection of…I don´t remember what was the name… the

protection of the cell. Those go faster than it and kill it.

Students also produced protocols that depicted static representations of the

viral replication process. Table III shows the case of a student in the TD

condition that produced an explanation of the viral replication process that

focused on the parts of the cell, but that did not include any representations of

temporal change. The sequence presented is divided by the student actions, but not

by the sequence of the process itself. In other words, the student acquired

disciplinary knowledge during the study but this knowledge was not organized

temporally. This type of protocol was produced by 35.7% of the students in the TD

condition and by 14.4% of the students in the TG condition. Students also produced

protocols that did not contain either dynamic representations or disciplinary

knowledge. No examples of this type are presented. This type of protocol was

produced by 19.5% students in the TD condition, and by 14.5% of the students in the

TG condition.


Table III. Static Mental Models grounded in disciplinary knowledge

I´m thinking, I´m doing the cell, ehh… here I´m doing the nucleus, the virus, eh,

and now I´m going to do the.. where it is going to enter, where it is going in, an

then, I´m going to make the other parts of the cell, and bip mmm and then I will

draw other more parts and that´s it.

Despite the variability in the answers, these results show overall that

students in the TG condition learned more about the dynamic nature of the viral

replication process and about its genetic mechanisms than students in the TD

condition. In fact, students presenting dynamic models tended to present more

disciplinary content than static representations in both conditions. Figure 4 shows

how the presence of organizers correlates with the mention of genetic mechanisms

for both groups. The correlation was .56 (p<.01) for the TD condition, and .37

(p<.05) for the TG condition. For the intervention in this study, researchers were

instructed not to prompt specific names related to genetics. According to the

design of the study, naming specific elements (e.g., ribosome) and genetic

mechanisms should arise from the interactions between students, and from their

independent exploration of the material. In a standard instructional situation, the

teacher has more room to connect content with the dynamic model presented in the

game, and thus it is reasonable to expect a higher correlation between temporal

organizers and genetic mechanisms.


Fig. 4 Scatterplot relating Organizers and Mechanisms

Drawings

Codes obtained from drawings were analyzed and significant differences were

evaluated using Chi square tests. Results show significant differences in the

number of levels included in the drawing χ2 (3, N = 82) = 11.08, p <.05. Students

in the TG condition had a higher frequency for two and three -level drawings, while

students in the TD condition had a higher frequency for one-level drawings. A

higher score in the number of levels implies that students move and coordinate

different grain sizes in the description of the viral replication process. They go

normally from the subcellular level to the cellular level; although, in some cases,

they go to the person or population levels. Additionally, a significant difference

was found in the presence of narrative-like elements, χ2 (1, N = 82) = 14.64, p


<.01 favoring students in the TG condition. Significant differences favoring

students in this same group were also found for actions, χ2 (1, N = 83) = 4.44, p

<.05, processes, χ2 (1, N = 83) = 4.43, p <.05, spontaneous explanations, χ2 (1, N =

83) = 3.84, p =.05, and resources, χ2 (1, N = 83) = 4.305, p <.05. All drawings

but two were coded as integrated. Students used different resources to give

coherence like arrows, written explanations and comic-like narrative structures.

This result is important because both the presence of arrows and the use comic-like

structures have been identified as graphical markers to indicate functional

descriptions of temporal change and other dynamic configurations (Tversky, 2005).

These results support the idea that playing Virulent creates a different type

of representation when compared to traditional class activities based on static

representations and text. Students that interacted with the game drew

representations that encoded actions of elements and sequences of events.

Additionally, in many cases, they included spontaneous explanations, which can be

interpreted as an indicator of high motivation. In many cases students also created

narrative-like descriptions of the process that encoded both the perceptual

elements of the model and the narrative elements emerging from the game story.

In Figure 5, we can see a typical drawing of a student in the TG condition.

The student used arrows to segment the steps of the process and lines to point

functions and constraints of the system. Interestingly, the student was able to

summarize the main elements of the process, including the main steps (e.g.,

entrance, RNA replication, defense), by connecting elements, actions and functions.

For example, the student points out that the virus frees the RNA in order to

reproduce itself and that certain elements (e.g., slicer, proteasome) defend the


cell from virus actions. This example was chosen because the student presents the

complete process and uses arrows to sequence the different steps. However, this

example does not show the level of detail that some drawings had when they focused

on specific sub-steps of the sequence. Other examples in this category depicted

more specific interactions within the process and showed, in very fine grain size,

their details. Some students, for instance, focused on the interaction between the

virus’ genetic material and the cell structures the virus uses to reproduce itself

(e.g., ribosome and mitochondria). Other students focused on the interactions that

happen outside of the cell (e.g., avoiding B-cells), and on the interactions that

constrained the virus entrance into the cell (e.g., finding a matching cell

receptor).

Fig. 5 Arrow-based representation of the viral replication process by student in

the TG condition

In Figure 6, it is possible to see how some students built a multilevel


explanation of the viral replication process. The student connects the persona

being infected with the process at a cellular level and subcellular (molecular)

level. This pattern was very common among students in the TG condition. A

multilevel representation is important because it means that students can

coordinate different levels of explanation regarding genetics (Duncan & Reiser,

2007). One reason for this effect is the structure of the game. Virulent players go

through a series of levels whose point of view moves from depicting the transition

of a virus from one cell to another, to describing the interactions between the

virus and the cell´s internal structures at the molecular level. This transition

from different grain sizes might help students to think about genetic phenomena as

a multilevel process.

Fig. 6 Drawing displaying both multilevel and narrative structure.


In Figure 6, it is also possible to see how this student elaborated a comic-

like story explaining the viral replication process. Several students in the TG

condition produced this type of narrative account, despite the fact that the game

itself did not present a comic-like storyboard. Although the game itself was not

particularly explicit in its narrative (it very briefly presents text at the start

of each level), students grasped the narrative nature of video games and

transformed the strategic nature of the game into a temporally organized narrative

of the process. This narrative construction was only possible because the game was

able to convey the temporal nature of the process under consideration.

When compared with drawings of successful students in the TD condition, these

examples highlight the advantages of video games as educational tools. In the TD

condition, many students presented drawings with no dynamic elements, even in cases

when they learned disciplinary content. Figure 7 shows how the viral replication

process is represented as a static set of elements without any specification of

temporal change, or genetic interactions. Several elements are mentioned, but there

is no reference to change in time nor to constraints in the interaction among virus

and cell structures. Summarizing, students in the TG condition presented more

complete drawings than students in the TG condition as evidenced by introduction of

time-related elements (e.g., processes, narrative structures, actions).


Fig. 7 Drawing of a successful student in the TD condition

Conclusions

The results of this study show that video games promote the creation of

mental models of scientific phenomena that are different than models produced by

traditional educational resources (e.g. text and graphs). Games favor the formation

of dynamic mental models of scientific phenomena, as shown by the differences in

thinking-aloud protocols and drawings between students in the TD and the TG

conditions. Students interacting with Virulent learned the dynamic nature of viral

replication and their change in time; they also learned the constraints (e.g.,

genetics mechanisms) from which flexible behavior arises. These two elements,

temporal change and causal constraints, are core elements in the definition of

dynamic mental models (Johnson-Laird, 1983).


In addition, the results show the emergence of comic-like narratives from the

interaction with Virulent. This result is interesting because it indicates that

dynamic phenomena and the dynamic mental models that represent them can be

communicated through narrative-like explanations. For education, this implies that

the complexity of micro-level interactions can be translated into narratives for

better understanding. It suggests also that in certain domains, such as history or

journalism, it is possible that the existence of narrative expertise consists of

the skill to translate the behavior of dynamic systems into narratives that

interweave multiple factors. For the case of this study, students took elements

from culturally relevant story boards and combined them with the experience of

playing an RTS game to produce narrative accounts of the process. Video games

particularly (but not exclusively RTS games), may create emergent narratives that

help learners to develop the skill to organize temporarily the complexity of

dynamic processes.

Implications for public policy and educational practice

This article has several implications in public policy and educational

practice. In general results show that video games have effects that are perceptual

and visual in nature. These effects however are hardly detectable with exclusively-

verbal assessments, let alone with multiple choice tests. Fine grained cognitive

changes are only detectable through alternative methods, in the case of this

article, drawings and verbal protocols. In this sense, public policy and class

assessment should include a variety of methods ranging from traditional

standardized test to student products. Additionally, this article exemplifies an

instructional intervention that lasts for at least four weeks. So there is no


guarantee that similar results can be obtained in shorter periods of time. In this

sense, educational policy needs to be informed by research that reviews similar

time spans. It is not clear that experiments that last 20 minutes can have similar

effects to those that last two weeks. In the same line, results as those obtained

in this research imply the use of multiple methods and tools within the classroom.

In the game condition both computer and texts were used, as well as collaborative

work.

The results of this study seem to support the integrated use of games within

hybrid learning environments. Consider for example, that some students did acquire

dynamic mental models but they did not introduce disciplinary content. This result

is a consequence of restrictions placed on researchers to increase experimental

control. However, in actual instructional situations, and in less controlled design

experiments, instructors and other educational actors should be encouraged to

increase the coordination between the video game and disciplinary content. In the

same line, it is important to note that Virulent was presented side by side with

other instructional material (e.g., virulent manual, texts). The findings exist

because of the interaction between the game and other instructional materials. The

video game by itself would not produce these effects. In this sense, the results of

this study support the idea that video games produce better effects when embedded

in adequate instructional situations that respond to an underlying curriculum

(XXXXXXXX). Educational games are not magic wands that can save education without

other systemic and pedagogical changes.

Acknowledgements

This research was supported partially by grants from XXXXXXXXXXXXXXXXXXXXXX


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XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX (To be introduced at the end of the

editorial process).

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