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WAINESS PHD QUALIFYING EXAM

Qualifying Examination

Richard Wainess

Rossier School of Education

University of Southern California

to

Dr. Harold O’Neil (Chair)

Dr. Richard Clark

Dr. Edward Kazlauskas

Dr. Janice Schafrik

Dr. Yanis Yortsos (Outside member)

14009 Barner Ave.

Sylmar, CA 91342

Home Phone: (818) 364-9419

E-Mail: [email protected]

In partial fulfillment of the requirement for the Degree

Doctor of Philosophy in Education in

Educational Psychology and Technology

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mailto:[email protected]


1. Review the theoretical and empirical literature on the impact of games on learning

and motivation. Please, focus on training of adults and include a discussion of

various game characteristics, such as fun, competition, fantasy, and challenge.

The purpose of this review is to discuss the literature on games and simulations, and studies

examine the potential of games and simulations as educational tools. The review begins with a

discussion of the differences between games and simulations, as well as the hybrid simulation-

game. Following that discussion is an examination of the motivational aspects of games as

informed by literature. Last is a discussion of various outcomes associated with games, including

learning outcomes.

Games and Simulations

According to Ricci, Salas, and Cannon-Bowers (1996), computer-based educational

games generally fall into one of two categories: simulation games and video games. Simulation

games model a process or mechanism relating task-relevant input changes to outcomes in a

simplified reality that may not have a definite endpoint. They often depend on learners reaching

conclusions through exploration of the relation between input changes and subsequent outcomes.

Video games, on the other hand, are competitive interactions bound by rules to achieve specified

goals that are dependent on skill or knowledge and that often involve chance and imaginary

settings (Randel, Morris, Wetzel, & Whitehill, 1992).

One of the first problems areas with research into game and simulations is terminology.

Many studies that claim to have examined the use of games, did not use a game (e.g., Santos,

2002). At best, they used an interactive multimedia that exhibits some of the features of a game,

but not enough features to actually be called a game. A similar problem occurs with simulations.

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A large number of research studies use simulations but call them games (e.g., Mayer, Mautone,

& Prothero, 2002). Because the goals and features of games and simulations differ, it is

important when examining the potential effects of the two media to be clear about which one is

being examined. However, there is little consensus in the education and training literature on

how games and simulations are defined.

Games

According to Garris, Ahlers, and Driskell (2002) early work in defining games suggested

that there are no properties that are common to all games and that games belong to the same

semantic category only because they bear a family resemblance to one another (Garris, Ahlers, &

Driskell, 2002). Betz (1995) argued that a game is being played when the actions of individuals

are determined by both their own actions and the actions of one or more actors. Dempsey,

Haynes, Lucassen, and Casey (2002) commented that a game is a set of activities involving one

or more players. It has goals, constraints, payoffs, and consequences.

A number of researchers agree that games have rules (Crookall, Oxford, and Saunders,

1987; Dempsey, Haynes, Lucassen, and Casey, 2002; Garris, Ahlers, & Driskell, 2002; Ricci,

1994). Researchers also agree that games have goals and strategies to achieve those goals

(Crookall & Arai, 1995; Crookall, Oxford, and Saunders, 1987; Garris, Ahlers, & Driskell, 2002;

Ricci, 1994). Many researchers also agree that games have competition (Dempsey, Haynes,

Lucassen, and Casey; 2002) and consequences such as winning or losing (Crookall, Oxford, and

Saunders, 1987).

Betz (1995) further argued that games simulate whole systems, not parts, forcing players

to organize and integrate many skills. Students will learn from whole systems by their individual

actions, individual action being the student’s game moves. Crookall, Oxfodr, and Saunders also

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noted that a game does not intend to represent any real-world system; it is a “real” system in its

own right. According to Duke (1995), games are situation specific. If well designed for a specific

client, the same game should not be expected to perform well in a different environment.

Simulations

In contrast to games, Crookall and Saunders (1989) viewed a simulation as a

representation of some real-world system that can also take on some aspects of reality for

participants or users. Similarly, Garris, Ahlers, & Driskell (2002) wrote that key features of

simulations are that they represent real-world systems, and Henderson, Klemes, and Eshet (2000)

commented that a simulation attempts to faithfully mimic an imaginary or real environment and

content that cannot be experienced directly, for such reasons as cost, danger, accessibility, or

time (Henderson, Klemes, & Eshet, 2000). Berson (1996) also argued that Simulations allow

students to engage in activities that would otherwise be too expensive, dangerous, or impractical

to conduct in the classroom.

Lee (1999) added that a simulation is defined as a computer program that relates them

together through cause and effect relationships. Reiber (1992; 1996) discussed microworlds, a

variant of simulations. He described microworlds as small representations of content areas or

domains that can be recognized by an expert, and simulations as designed to mimic real life

experiences, such as a flight simulator (Lee, 1999).

Thiagarajan (1998) argued that simulations do not reflect reality; they reflect someone’s

model of reality. According to Thiagarajan, a simulation is a representation of the features and

behaviors of one system through the use of another. Elements of a simulation correspond to

selected elements of the system being simulated. Some simulations focus on the physical features

of a real world object, such as a model airplane, while others focus on the processes and

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interactions of real world events, such as mathematical equations that predict the number of

traffic fatalities during a holiday weekend (Thiagarajan, 1998). At the risk of introducing a bit

more ambiguity, Garris, Ahlers, and Driskell (2002) proposed that simulations can contain game

features, which leads to the final definition: sim-games.

Simulation-Games

Thus, it is not too improper to consider games and simulations as similar in some

respects, keeping in mind the key distinction that simulations propose to represent reality and

games do not (Garris, Ahlers, & Driskell, 2002). Combining the features of the two media,

Rosenorn and Kofoed (1998) described simulation/gaming as a learning environment where

participants are actively involved in experiments, for example, in the form of role-plays, or

simulations of daily work situations, or developmental scenarios. Being away from the real

workplace, participants have the freedom to make wrong decisions and to learn from them.

This paper will use the definitions of games, simulations, and sim-games as defined by

Gredler (1996), which combine the most common features cited by the various researchers, and

yet provide clear distinctions between the three media. According to Gredler,

Games consist of rules that describe allowable player moves, game constraints and

privileges (such as ways of earning extra turns), and penalties for illegal

(nonpermissable) actions. Further, the rules may be imaginative in that they need

not relate to real-world events (p. 523).

This definition is in contrast to a simulation, which Gredler (1996) defines as “a dynamic

set of relationships among several variables that (1) change over time and (2) reflect authentic

causal processes” (p. 523). In addition, Gredler describes games as linear and simulations as non-

linear, and games as having a goal of winning while simulations have a goal of discovering

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causal relationships. Gredler also defines a mixed metaphor referred to as simulation games or

gaming simulations, which is a blend of the features of the two interactive media: games and

simulations.

Motivational Aspects of Games

According to Garris, Ahlers, and Driskell (2002), motivated learners are easy to describe.

They are enthusiastic, focused and engaged, they are interested in and enjoy what they are doing,

they try hard, and they persist over time. Furthermore, they are self-determined and driven by

their own volition rather than external forces (Garris, Ahlers, & Driskell, 2002). Ricci, Salas, and

Cannon-Bowers (1996) defined motivation as “the direction, intensity, and persistence of

attentional effort invested by the trainee toward training.” Similarly, according to Malouf (1987-

1988), continuing motivation is defined as returning to a task or a behavior without apparent

external pressure to do so when other appealing behaviors are available. And more simply, Story

and Sullivan (1986) commented that the most common measure of continuing motivation is

whether a student returns to the same task at a later time. In general, these descriptions of

motivation include the concept of continued motivation; persistence.

With regard to video games, and Asakawa and Gilbert (2003) argued that without sources

of motivation, players often lose interest and drop out of a game. However, there seems little

agreement among researchers on what those sources are—the specific set of elements or

characteristics that lead to motivation in any learning environment, and particularly with

educational games. According to Rieber (1996) and McGrener (1996), motivational researchers

have offered the following characteristics as common to all intrinsically motivating learning

environments: challenge, curiosity, fantasy, and control (Davis & Wiedenbeck, 2001; Lepper &

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Malone, 1987; Malone, 1981; Malone & Lepper, 1987). Malone (1981) and others also included

fun as a criteria for motivation.

For interactive games, Stewart (1997) described some of the same elements as above, but

also included additional motivational elements; goals and outcomes. Locke and Latham (1990)

also commented that on of the most robust findings in the literature on motivation is that clear,

specific, and difficult goals lead to enhanced performance (Locke & Latham, 1990). They argued

that clear, specific goals allows the individual to perceive goal-feedback discrepancies, which are

seen as crucial in triggering greater attention and motivation. Clark (2001) argued that

motivation cannot exist without goals. The role of goals will be discussed in question 2. The

response to this question will focus on fantasy, control and manipulation, challenge and

complexity, curiosity, competition, feedback, and fun.

Fantasy

Research suggests that material may be learned more readily when presented in an

imagined context that interests the learner than when presented in a generic or decontextualized

form (Garris, Ahlers, & Driskell, 2002). Malone and Lepper (1987) defined fantasy as an

environment that evokes “mental images of physical or social situations that do not exist” (p.

250). According to Garris, Ahlers, and Driskell (2002), games involve imaginary worlds; activity

inside these worlds has no impact on the real world; and when involved in a game, nothing

outside the game is relevant. Rieber (1996) commented that fantasy is used to encourage learners

to imagine that they are completing the activity in a context in which they are really not present.

However, Rieber also described endognenous and exogenous fantasies. Endogenous fantasy

weaves relevant fantasy into a game, while exogenous simply sugar coast a learning environment

with fantasy. An example of an endogenous fantasy would be the use of a laboratory

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environment to learn chemistry, since this environment is consistent with the domain. An

example of an exogenous environment would be a using a hangman game to learn spelling,

because hanging a person has nothing to do with spelling. Rieber (1996) describes endogenous

fantasy, but not exogenous fantasy, as important to intrinsic motivation, and further commented

that, unfortunately, exogenous fantasies are a common and popular element of many educational

games.

According to Malone and Lepper (1987), fantasies can offer analogies or metaphors for

real-world processes that allow the user to experience phenomena from varied perspectives. A

number of researchers (Anderson and Pickett, 1978; Ausubal, 1963; Malone and Lepper, 1978;

Malone and Lepper, 1987; Singer, 1973) argue that fantasies in the form of metaphors and

analogies provide learners with better understanding by allowing them to relate new information

to existing knowledge. According to Davis and Wiedenbeck (2001), metaphor also helps learners

to feel directly involved with objects in the domain so that the computer and interface become

invisible. The relationship of analogy and metaphor to learning is discussed in question 2.

Control and Manipulation

Hannifin and Sullivan (1996) define control as the exercise of authority or the ability to

regulate, direct, or command something. Control, or self-determination, promotes intrinsic

motivation because learners are given a sense of control over the choices of actions they may

take (deCharms, 1986; Deci, 1975; Lepper and Greene, 1978). Furthermore, control implies that

outcomes depend on learners’ choices and, therefore, learners should be able to produce

significant effects through their own actions (Davis, & Wiedenbeck, 2001). According to Garris,

Ahlers, & Driskell (2002), games evoke a sense of personal control when users are allowed to

select strategies, manage the direction of activities, and make decisions that directly affect

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outcomes, even if those actions are not instructionally relevant (Garris, Ahlers, & Driskell,

2002).

However, Hannafin & Sullivan (1996) warned that research comparing the effects of

instructional programs that control all elements of the instruction (program control) and

instructional programs in which the learner has control over elements of the instructional

program (learner control) on learning achievement has yielded mixed results. Dillon and

Gabbard (1998) commented that novice and lower aptitude students have greater difficulty when

given control, compared to experts and higher aptitude students, and Niemiec, Sikorski, and

Walberg (DATE) argued that control does not appear to offer any special benefits for any type of

learning or under any type of condition.

Challenge and complexity

Challenge, also referred to as effectance, compentence, or mastery motivation (Bandura,

1977; Csikszentmihalyi, 1975; Deci, 1975; Harter, 1978; White, 1959), embodies the idea that

intrinsic motivation occurs when there is a match between a task and the learner’s skills. The

task should not be too easy nor too hard, because in either case, the learner will lose interest.

Similarly, Malone and Lepper (1987) have claimed that individuals desire an optimal level of

challenge; that is, tasks that are neither too easy nor too difficult to perform. Stewart (1997)

commented that games that are too easy will be dismissed quickly. According to Garris, Ahlers,

and Driskell (2002), there are several ways in which an optimal level of challenge can be

obtained. Goals should be clearly specified, yet the probability of obtaining that goal should be

uncertain, and goals must also be meaningful to the individual. They further argued that linking

activities to valued personal competencies, embedding activities within absorbing fantasy

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scenarios, or engaging competitive or cooperative motivations could serve to make goals

meaningful (Garris, Ahlers, & Driskell, 2002).

Curiosity

According to Rieber (1996), challenge and curiosity are intertwined. Curiosity arises

from sitatuions in which there is complexity, incongruity, and discrepancy (Davis, &

Wiedenbeck, 2001). Sensory curiosity is the interest evoked by novel situations, cognitive

curiosity is the evoked by the desire for knowledge (Garris, Ahlers, & Driskell, 2002). Cognitive

curiosity motivates the learner to attempt to resolve the inconsistency through exploration

(Davis, & Wiedenbeck, 2001). Curiosity is identified in games by unusual visual or auditory

effects, and by paradoxes, incompleteness, and potential simplifications (Westbrook &

Braithwaite, 2002). Curiosity is the desire to acquire more information. This is a primary

component of the players’ motivation to learn how to operate the game (Westbrook &

Braithwaite, 2001).

Malone and Lepper (1987) noted that curiosity is one of the primary factors that drive

learning and is related to the concept of mystery. Garris, Ahlers, and Driskell (2002) commented

that make the distinction between curiosity and mystery to reflect the difference between

curiosity, which resides in the individual, and mystery, which is an external feature of the game

itself. Thus, mystery evokes curiosity in the individual, and this leads to the question of what

constitutes mystery (Garris, Ahlers, & Driskell, 2002). Research suggests that mystery is

enhanced by incongruity of information, complexity, novelty, surprise, and violation of

expectations (Berlyne, 1960), incompatibility between ideas and inability the predict the future

(Kagan, 1972), and information that is incomplete and inconsistent (Malone & Lepper, 1987).

Competition

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Studies on competition with games and simulations have mixed results, due to

preferences and reward structures. In a study by Porter, Bird, and Wunder (1990-1991)

examining competition and reward structures found that the greatest effects of reward structure

were seen in the performance of those with the most pronounced attitudes toward either

competition or cooperation. The results suggested that performance was better when the reward

structure matched the individual’s preference. According to the authors, implications are that

emphasis on competition will enhance the performance of some learners but will inhibit the

performance of others (Porter, Bird, and Wunder, 1990-1991).

Yu (2001) investigated the relative effectiveness of cooperation with and without inter-

group competition in promoting student performance, attitudes, and perceptions toward subject

matter studied, computers, and interpersonal context. With fifth-graders as participants, Yu

found that cooperation without inter-group competition resulted in better attitudes toward the

subject matter studies, and promoted more positive inter-personal relationships both within and

among the learning groups than cooperation/competition did (Yu, 2001). The exchange of ideas

and information both within and among the learning groups also tended to be more effective and

efficient when cooperation did not take place in the context of inter-group competition (Yu,

2001).

Feedback

Feedback within games can also easily be provided in order for learners to quickly

evaluate their progress against the established game goal. This feedback can take many forms,

such as textual, visual, and aural (Rieber, 1996). According to Ricci, Salas, and Cannon-Bowers

(1996), within the computer-based game environment, feedback is provided in various forms

including audio cues, score, and remediation immediately following performance. They argued

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that these feedback attributes can produce significant differences in learner attitudes, resulting in

increased attention to the learning environment.

Fun

Learning that is fun appears to be more effective (Lepper & Cordova, 1992). Quinn

(1994, 1997) argued that for games to benefit educational practice and learning, they need to

combine fun elements with aspects of instructional design and system design that include

motivational, learning, and interactive components. According to Malone (1981a, b) three

elements (fantasy, curiosity, and challenge) contribute to the fun in games (as cited in Armory et

al, 1999). While fun has been cited as important for motivation and, ultimately, for learning,

there is no empirical evidence supporting the concept of fun. This might be due to the fact that

fun is not a construct but, rather, represents other concepts or constructs. Relevant alternative

concepts or constructs are play, engagement, and flow.

Play is entertainment without fear of present or future consequences; it is fun (Resnick &

Sherer, 1994). According to Rieber, Smith, and Noah (1998), play describes the intense learning

experience in which both adults and children voluntarily devote enormous amounts of time,

energy, and commitment and, at the same time, derive great enjoyment from the experience. This

is termed serious play to distinguish it from other interpretations which may have negative

connotations (Rieber, Smith, & Noah, 1998). Webster et al. (1993) found that labeling software

training as play showed improved motivation and performance. According to Rieber and Matzko

(2001) serious play is an example of an optimal life experience.

Csikszentmihalyi (1975; 1990) defines an optimal experience as one in which a person is

so involved in an activity that nothing else seems to matter; termed flow or a flow experience.

When completely absorbed in and activity, he or she is “carried by the flow,” hence the origin of

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the theory’s name (Rieber and Matzko, 2001). Rieber and Matzko (2001) also content that a

person may be considered in flow during an activity when experiencing one or more of the

following characteristics: Hours pass with little notice; challenge is optimized; feelings of self-

consciousness disappear; the activity’s goals and feedback are clear; attention is completely

absorbed in the activity; one feels in control; and one feels freed from other worries (Rieber &

Matzko, 2001). And according to Davis and Wiedenbeck (2001), an activity that is highly

intrinsically motivating can become all-encompassing to the extent that the individual

experiences a sense of total involvement, losing track of time, space, and other events. Davis and

Wiedenbeck also argued that the interaction style of a software package is expected to have a

significant effect on intensity of flow. However, Rieber and Matzko contend that play and flow

differ in one respect; learning is an expressed outcome of serious play but not of flow (Rieber &

Matzko, 2001).

Engagement is defined as a feeling of directly working on the objects of interest in the

worlds rather than on surrogates. According to Davis and Wiedenbeck, this interaction or

engagement can be used along with the components of Malone and Lepper’s (DATE) intrinsic

motivation model to explain the effect of an interaction style on intrinsic motivation, or flow

(Davis, & Wiedenbeck, 2001). Garris, Ahlers, and Driskell (2002) commented that training

professional are interested in the intensity of involvement and engagement that computer games

can invoke, that the “holy grail” of training professionals is to harness the motivational

properties of computer games to enhance learning and accomplish instructional objectives.

Garris, Ahlers, and Driskell further argued that engagement in game play leads to the

achievement of training objectives and specific learning outcomes (Garris, Ahlers, & Driskell,

2002).

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Learning and Other Outcomes for Games

Simulations and games have been cited as beneficial to a number of disciplines and for a

number of educational and training situations, including aviation training (Salas, Bowers, &

Rhodenizer, 1998), aviation crew resource management (Baker, 1993), military mission

preparation (Spiker & Nullmeyer, n.d.), laboratory simulation (Betz, 1995), chemistry and

physics education (Khoo & Koh, 1998), urban geography and planning (Adams, 1998; Betz,

1995), farm and ranch management (Cross, 1993), language training (Hubbard, 1991), disaster

management (Stolk, Alexandrian, Gros, & Paggio, 2001), and medicine and health care

(Westbrook & Braithwaite, 2001; Yair, Mintz, & Litvak, 2001). For business, games and

simulations have been cited as useful for teaching strategic planning (Washburn & Gosen, 2001;

Wolfe & Roge, 1997), finance (Santos, 2002), portfolio management (Brozik, & Zapalska,

2002), marketing (Washburn & Gosen), knowledge management (Leemkull, de Jong, de Hoog,

& Christoph, 2003), and media buying (King & Morrison, 1998).

In addition to teaching domain-specific skills, games have been used to teach more

generalizable skills. Since the mid 1980s, a number of researchers have used the game Space

Fortress, a 2-D, simplistic arcade-style game, with a hexagonal “fortress” in the center of the

screen surrounded by two concentric hexagons, and a space ship, to improve spatial and motor

abilities that transferred far outside gameplay, such as significantly improving the results of

fighter pilot training (Day, Arthur, and Gettman, 2001). In a series of five experiments, Green

and Bavelier (2003) showed the potential of video games to significantly alter visual selection

attention. Similarly, Greenfield, DeWinstanley, Kilpatrick, & Kaye (1994) found, with

experiments involving colleges students, that skilled or expert video game players had better

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skills for monitoring two locations on a visual screen and that video game practice could alter the

strategies of attentional deployment.

According to Ricci, Salas, and Cannon-Bowers (1996), results of their study provided

evidence that computer-based gaming can enhance learning and retention of knowledge. They

further commented that positive trainee reaction might increase the likelihood of student

involvement with training (i.e., devote extra time to training). Druckman (1995) also concluded

that games seem to be effective in enhancing motivation and increasing student interest in

subject matter, yet the extent to which that translates into more effective learning is less clear.

With caution, Brougere (1999) commented that anything that contributes to the increase of

emotion (the quality of the design of video games, for example) reinforces the attraction of the

game but not necessarily its educational interest (p. 140). Similary, Salas, Bowers, and

Rhodenizer (1998) commented that liking a simulation does not necessarily transfer to learning.

Salomon (1984) went even further, by commenting that a more positive attitude can actually

indicate less learning.

Garris, Ahlers, and Driskell (2002) noted that although students generally seem to prefer

games over other, more traditional, classroom training media, reviews have reported mixed

results regarding the training effectiveness of games. According to Leemkull, de Jong, de Hoog,

and Christoph (2003), much of the work on the evaluation of games has been anecdotal,

descriptive, or judgmental, but there are some indications that they are effective and superior to

case studies in producing knowledge gains, especially in the area of strategic management

(Wolfe, 1997).

In contrast, in an early meta-analysis of the effectiveness of simulation games, Dekkers

and Donatti (1981) found a negative relationship between duration of training and training

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effectiveness. Simulation game became less effective the longer the game was used (suggesting

that perhaps trainees became bored over time). de Jong and van Joolingen (1998), after

reviewing a large number of studies on learning from simulations, concluded, “there is no clear

and univocal outcome in favor of simulations. An explanation why simulation based learning

does not improve learning results can be found in the intrinsic problems that learners may have

with discovering learning” (p. 181). These problems are related to processes such as hypothesis

generation, design of experiments, interpretation of data, and regulation of learning. After

analyzing a large number of studies, de Jong and van Joolingen (1998) concluded that adding

instructional support to simulations might help to improve the situation.

In meta-analyzing a number of studies and meta-analyses on video games, Lee (1999)

commented that effect size never tells us under what conditions students learn more, less, or not

at all compared with the comparison group. For instructional prescription, we need information

dealing with instructional variable, such as instructional mode, instructional sequence,

knowledge domain, and learner characteristics. If we don’t know how these variables are

connected to learning outcomes, there is no way to prescribe appropriate conditions of

instruction for specific target learners. As a result, findings of these studies cannot contribute to

the quality of instruction in various educational settings (Lee, 1999).

According to Thiagarajan (1998), if not embedded with sound instructional design,

games and simulations often end up truncated exercises often mislabeled as simulations (Gredler,

1996). Gredler further commented that poorly developed exercises are not effective in achieving

the objectives for which simulations are most appropriate—that of developing students’

problem-solving skills (Gredler, 1996). Berson (1996) argued that, with regards to research into

the effectiveness of computers in social studies, methodological problems persist in the areas of

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insufficient treatment definitions and descriptions, inadequate sampling procedures, and

incomplete reporting of statistical results. Overall, there is paucity of empirical evidence, and

most conclusions are impressionistic. Consequently, there is not satisfactory evidence on which

to base decisions to integrate computers into social studies instruction (Berson, 1996).

The generally accepted position is that games themselves are not sufficient for learning

but that there are elements of games that can be activated within an instructional context that

may enhance the learning process (Garris, Ahlers, & Driskell, 2002). In other words, outcomes

are affected by the instructional strategies employed (Wolfe, 1997). Leemkull, de Jong, de Hoog,

and Christoph (2003), too, commented that there is general consensus that learning with

interactive environments such as games, simulations, and adventures is not effective when no

instructional measure or support are added.

Reflection and Debriefing

Brougere (1999) argued that a game cannot be designed to directly provide learning. A

moment of reflexivity is required to make transfer and learning possible. Games require

reflection, which enables the shift from play to learning. Therefore, debriefing (or after action

review) appears to be an essential contribution to research on play and gaming in education

(Brougere, 1999). Similarly, Thiagarajan (1998) commented that participants of a simulation are

not in a position to learn anything worthwhile unless they are required and encouraged to reflect

on the experience through the process of debriefing (Thiagarajan, 1998).

According to Garris, Ahlers, and Driskell (2002), debriefing is the review and analysis of

events that occurred in the game. Debriefing provides a link between what is represented in the

simulation or gaming experience and the real world. It allows the learners to draw parallels

between game events and real-world events. Debriefing allows learners to transform game events

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into learning experiences. Debriefing may include a description of events that occurred in the

game, analysis of why they occurred, and the discussion of mistakes and corrective actions.

Garris, Ahlers, and Driskell argued that learning by doing must be coupled with the opportunity

reflect and abstract relevant information for effective learning to occur (Garris, Ahlers, &

Driskell, 2002).

Results of a study by Leemkull, de Jong, de Hood, and Christoph (2003) indicated that

the four-phase approach (introduction, instruction, engagement, and reflection/debriefing) was an

effective approach to learning. The debriefing process not only provided the opportunity to

improve learning, it provided the opportunity for feedback on the simulation, which resulted in

many suggestions for improvement (Leemkull, de Jong, de Hoog, & Christoph, 2003). Salas,

Bowers, and Rhodenizer (1998), noted that debriefing mechanisms are necessary to ensure

learning in simulation-based training systems. Thiagarajan (1998) argued that debriefing is too

important to be added on as an afterthought to an interactive simulation, especially one used for

training, increasing awareness, or team building. No simulation package can be considered

complete without an extensive debriefing guide.

SummaryAccording to Ruben (1999), the theoretical foundations for simulations, games, and other

forms of interactive, experience-based learning had been in place at least since the writings of Aristotle and the practices of Socrates (Ruben, 1999).

If we are able to participate in games and simulations, it is because as children we learned to master rules. We even ask ourselves if play does not prepare for a number of learning situations characterized by a more or less explicit dimension of simulation, which supposes master of the second degree and rules specific to certain situations. This is probably why the Romans had the same name ludus for play and for school and why the teacher was called magister ludi (Brougere, 1999).

Currently, the increase power and flexibility of computers technology is contributing to renewed interest in games and simulations. This development coincides with the current perspective of effective instruction in which meaningful learning depends on the construction of knowledge by the learner. Games and simulations, which can provide an environment for learner’s construction of new knowledge, have the potential to become a major component of this focus (Gredler, 1996).

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Games, simulations, and case studies have an important role in education and training in putting learning into a context. Furthermore, they are constructivistic environments in which students are invited to actively solve problems. Games and simulations provide students with a framework of rules and roles through which they can learn interactively through a live experience. They can tackle situations they might not be prepared to risk in reality, and they can experiment with new ideas and strategies (Leemkull, de Jong, de Hoog, & Christoph, 2003).

Computer games offer a new possibility for wedding motivation and self-regulated learning within a constructivist framework, on which strives to combine both training nd education, practice and reflection, into a seamless learning experience (Rieber, Smith, & Noah, 1998). They involve individual and group interpretations of given information, the capacity to suspend disbelief, and a willingness to play with the components of a situation in making new patterns and generating net problems (Jacues, 1995; as cited in Leemkull, de Jong, de Hoog, & Christoph, 2003).

Instructional games offer the opportunity for the learner to learn by doing, to become engaged in authentic learning experiences. However, people do not always learn by doing. Sometimes we learn by observing; sometimes we learn by being told. “Learners are not passive blotters at which we toss information; nor are they active sponges that absorb all they experience unaided. We must temper our enthusiasm for the gaming approach with knowledge that instructional games must be carefully constructed to provide both an engaging first-person experience as well as appropriate learner support” (Garris, Ahlers, & Driskell, 2002, p. 461).

A type of learning environment, which is very close to games, is simulation. Simulations resemble games in that both contain a model of some kind of system and learners can provide input (changes to variable values or specific actions) and observe the consequences of their actions (Leemkull, de Jong, de Hoog, & Christoph, 2003).

The modern computer technology has made possible a new and rich learning environment, the simulation. In an instructional simulation, students learn by actually performing activities to be learned in a context that is similar to the real world. Instructional simulation is used in most cases as unguided discovery learning. Students can generate and test hypotheses in a simulated environment by examining changes in the environment based on their input. Unlike the traditional classroom instruction, in which students’ roles are passive in most cases, this particular type of instruction requires students to be involved in their learning in an active way (Lee, 1999).

There have been a great number of experimental studies to examine the instructional value of simulation. In most cases of these studies, researchers used expository instructional methods, such as traditional classroom lectures or computer-based tutorials for comparison groups. The research results from these studies were conflicting (Lee, 1999).

Our findings from two experiments involving high school students suggest that the effectiveness of CAI may go beyond basic cognitive processes, such as rote memory. While previous work has found that CAI can be as effective as traditional teaching methods for rote memory, it has not always been shown to be more effective (Taylor, Renshaw, & Jensen, 1997).

When computer technologies are designed around principles gleaned from learning theories and implemented systematically, one can argue that the effect that these technologies have on student learning and achievement are both powerful and transformative. Technologies designed around educational and psychological theory compare favorably to other education reform efforts because they have embedded proven teaching principles into the technology.

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Thus, one gets the effects of both the teaching reform and the technology (Schacter & Fagnano, 1999).

We than make the more important distinction that computer technologies, when designed according to sound learning theory and pedagogy, have, and can substantially improve student learning (Schacter & Fagnano, 1999).

Computer-Based Instruction (CBI) has been shown to moderately improve student learning and achievement (Schacter & Fagnano, 1999).

Schacter and Fagnano (1999) conducted a meta-analysis of 12 meta-analyses on computer-based instruction, comprised of a total of 546 individual studies, with subjects from elementary, secondary, precollege, special, and college institutions (Schacter & Fagnano, 1999).

According to Cobb (1997), Clark’s work has prompted educators to be skeptical of inflated media claims; to notice when expensive media are promoted where cheap would do; to center instructional designs on the learner rather than the medium; to track learning effect to instructional cause at the lowest level of analysis possible (medium attribute rather than medium per se, method rather than medium, message rather than method; Cobb, 1997).

There are clearly many media for any instructional job, but this does not mean they all do it at the same level of efficiency—whether economic, logistical, social, or cognitive (Cobb, 1997).

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2. Review the theoretical and empirical literature on the relationship of cognitive load

to learning. Please, include a discussion of cognitive load in relationship to

interactive media (e.g., multimedia and games). Be sure to focus types of cognitive

load (e.g., intrinsic, germane, and extraneous load).

This review examines the constructs defined by cognitive load theory, including a limited

working memory, separate channels for auditory and visual stimuli, an unlimited long-term

memory, and development of information chunks into simple and complex schema that, with

practice can be automated. Related to schemas, which are abstract constructs that reside in

memory, are mental models, which are a learner’s definable interpretation of a problem space,

including the compoents of the space and how those components are linked or associated.

Following that discussion is a discussion of meaningful learning and related constructs:

metacognition, mental effort and persistence, self-efficacy, and problem solving. Meaningful

learning is defined as deep understanding of the material, which includes attending to important

aspects of the presented material, mentally organizing it into a coherent cognitive structure, and

integrating it with relevant existing knowledge. Meaningful learning is reflected in the ability to

apply what was taught to new situations; problem solving transfer (Mayer & Moreno, 2003).

Next is a discussion of games and learning as informed by cognitive load theory, with a

discussion of learner control. Last is a discussion of finding on the various forms of cognitive

load (intrinsic, germane, and extraneous), effects related to cognitive load (e.g., split-attention,

modality, and redundany effects) and recommendations for reducing cognitive load.

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Cognitive Load Theory

Cognitive load theory (CLT) began in the 1980s and underwent substantial development

and expansion in the 1990s (Paas, Renkl, & Sweller, 2003). Cognitive load theory is concerned

with the development of instructional methods aligned with the learners’ limited cognitive

processing capacity to stimulate their ability to apply acquired knowledge and skills to new

situations (i.e., transfer). Similarly, Burnken, Plass, and Leutner (2003) argue that cognitive load

theory is based on several assumptions regarding human cognitive architecture: the assumption

of a virtually unlimited capacity of long-term memory, schema theory of mental representations

of knowledge, and limited-processing capacity assumptions of working memory (Brunken, Plass,

& Leutner, 2003). According to Cooper (1998), cognition is the intellectual processes through

which information is obtained, represented mentally, transformed, stored, retrieved, and used.

CLT is based on the idea that a cognitive architecture exists consisting of a limited working

memory, with partly independent processing units for visual-spatial and auditory-verbal

information (Mayer & Moreno, 2003), and these structures interact with a comparatively

unlimited long-term memory (Mousavi, Low, & Sweller, 1995)..

Cognitive load is the total amount of mental activity imposed on working memory at an

instance in time (Chalmers, 2003; Cooper, 1998; Sweller, 1993; Sweller and Chandler, 1994,

Yeung, 1999). Researchers have proposed that working memory limitations can have an adverse

effect on learning (Sweller, 1993; Sweller and Chandler, 1994, Yeung, 1999). According to Paas,

Tuovinen, Tabbers, & Van Gerven, (2003), cognitive load can be defined as a multidimensional

construct representing the load that performing a particular task imposes on the learner’s

cognitive system. The construct has a causal dimension reflecting the interaction between task

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and learner characteristics and an assessment dimension reflecting the measurable concepts of

mental load, mental effort, and performance (Paas, Tuovinen, Tabbers, & Van Gerven, 2003).

Cognitive load can be treated as a theoretical construct, describing the internal processes of

information processing that cannot be observed directly (Brunken, Plass, & Leutner, 2003).

Working memory limits profoundly influence the character of human information

processing (Kalyuga, Ayers, Chandler, & Sweller, 2003; Mayer & Moreno, 2003; Mousavi,

Low, & Sweller, 1995). Only a few elements (or chunks) of information can be processes at any

time without overloading capacity and decreasing the effectiveness of processing (Cooper,

1998). In contrast to working memory, long-term memory contains huge amounts of domain-

specific knowledge structures organized to allow us to categorize different problem states and

decide the most appropriate solution moves (Kalyuga, Ayers, Chandler, & Sweller, 2003).

Central to CLT is the notion that working memory architecture and its limitations should

be a major consideration when designing instruction (Paas, Tuovinen, Tabbers, & Van Gerven,

2003). Overload of working memory (Mayer & Moreno, 2003) has long been recognized as an

important source of performance errors during human-computer interaction and is particularly

acute in unskilled users for whom unfamiliar procedures are likely to require greater

commitment of cognitive resources. Furthermore, overload of working memory capacity had

been found to be a limiting factor in the early stages of procedural skill acquisition (Gevins,

Smith, Leong, McEvoys, Whitfield, Du, & Rush, 1998). Cognitive load is not simply considered

as a by-product of the learning process but as a major factor that determines the success of an

instruction intervention (Paas, Tuovinen, Tabbers, & Van Gerven, 2003).

Working Memory

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Working memory refers to the limited capacity for holding information in mind for

several seconds in the context of cognitive activity (Gevins, Smith, Leong, McEvoys, Whitfield,

Du, & Rush, 1998). According to Brunken, Plass, and Leutner (2003), the Baddeley (1986)

model of working memory assumes the existence of a central executive that coordinates two

slave systems, a visuospatial sketchpad for visuospatial information such as written text or

pictures, and a phonological loop for phonological information such as spoken text or music

(Baddeley, 1986, Baddeley & Logie, 1999). It is also assumed that both slave systems are limited

in capacity and independent from one another in that the processing capacities of one system

cannot compensate for lack of capacity in the other (Brunken, Plass, & Leutner, 2003).

According to the dual-processing theory, visually presented information is processed—at

least partially—in visual working memory whereas auditorily presented information is processed

—at least partially—in auditory working memory (Mayer & Moreno, 1998). The dual coding

theory involves three processes: A verbal explanation is presented along with a visual

explanation, then in working memory the learner constructs mental representations of the two

explanations and accesses relevant prior knowledge from long term memory, and lastly the two

representations are combined or linked with referential connections (Mayer & Sims, 1994). If the

difference between total cognitive load and the processing capacity of the visual or auditory

working memory approaches zero, then the learner experiences a high cognitive load or overload

(Brunken, Plass, & Leutner, 2003).

Long-Term Memory

According to Paas, Renkl, and Sweller (2003), working memory, in which all conscious

cognitive processing occurs, can handle only a very limited number of novel interacting

elements; possibly no more than two or three. In contrast, long-term memory and unlimited,

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permanent capacity (Tennyson & Breuer, 2002) and can contain vast numbers of schemas—

cognitive constructs that incorporate multiple elements of information into a single element with

a specific function (Paas, Renkl, & Sweller, 2003). Noyes and Garland, 2003 contend that

information that is not held in working memory will need to be retained by the long-term

memory system. Storing more knowledge in long-term memory reduces the load on working

memory. This results in a greater capacity being made available for active processing.

The knowledge base stored in long-term memory consists of domains of knowledge that

can be described as complex networks (or schemas) of information (e.g., concepts or

propositions). Within a domain, knowledge is organized into meaningful modules called

schemas. Schemas vary per individual according to amount, organization, and accessibility

(Tennyson & Breuer, 2002).

According to CLT, multiple elements of information can be chunked as single elements

in cognitive schema (Chalmers, 2003), and through repeated use can become automated.

Automated information, developed over hundreds of hours of practice (Clark, 1999) can be

processed without conscious effort, bypass working memory during mental processing thereby

circumventing the limitations of working memory. (Clark 1999; Mousavi, Low, & Sweller,

1995). Consequently, the prime goals of instruction are the construction (chunking) and

automation of schemas (Paas, Tuovinen, Tabbers, & Van Gerven, 2003).

Schema Development

Schema is defined as a cognitive construct that permits people to treat multiple

subelements of information as a single element, categorized according to the manner in which it

will be used (Kalyuga, Chandler, & Sweller, 1998). Schemas are generally thought of as ways of

viewing the world and in a more specific sense, ways of incorporating instruction into our

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cognition. Schema acquisition is a primary learning mechanism. Piaget proposed that learning is

the result of forming new schemas and building upon previous schema (Chalmers, 2003).

Schemas have the functions of storing information in long-term memory and of reducing

working memory load by permitting people to treat multiple elements of information as a single

element (Kalyuga, Chandler, & Sweller, 1998; Mousavi, Low, & Sweller, 1995).

With schema use, a single element in working memory might consist of a large number

of lower level, interacting elements, which if processed individually might have exceeded the

capacity of working memory (Paas, Renkl, & Sweller, 2003). If a schema can be brought into

working memory in automated form, it will make limited demands on working memory

resources, leaving more resources available to search for a possible solution problem (Kalyuga,

Chandler, & Sweller, 1998). Controlled use of schemas requires conscious effort, and therefore,

working memory resources. However, after having being sufficiently practiced, schemas can

operate under automatic, rather than controlled, processing. Automatic processing of schemas

requires minimal working memory resources and allows for problem solving to proceed with

minimal effort (Kalyuga, Ayers, Chandler, & Sweller, 2003; Kalyuga, Chandler, & Sweller,

1998; Paas, Renkl, & Sweller, 2003).

Experts possess a larger (and potentially unlimited) number of domain-specific schemas.

Hierarchically organized schemas represent experts’ knowledge in the domain and allow experts

to categorize multiple elements of related information into a single, higher level element. When

confronted with a specific configuration of elements, experts are able to recognize the pattern as

a familiar schema and treat (and act on) the whole configuration as a single unit. Experts are able

to bypass working memory capacity limits by having many of their schemas highly automated

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due to extensive practice. Novices lack sophisticated schemas associated with a task or situation

at hand. (Kalyuga, Ayers, Chandler, & Sweller, 2003).

Schema development is at the route of the constructivist perspective of learning.

Constructivist learning occurs when learners construct meaningful mental representations from

presented information and build referential connections between them (Mayer, Moreno, Boire, &

Vagge, 1999). This network of interconnections can extend and link to other information to

broaden the range of cognitive activities, such as answering a variety of domain-specific

questions, drawing analogies, making inferences, and generalizing to other domains (Blanton,

1998). Constructivist learning is fostered when the learner is able to hold a visual representation

in visual working memory and a corresponding verbal representation in verbal working memory

at the same time. The model implicates working memory (or cognitive load) as a major

impediment to constructivist learning (Mayer, Moreno, Boire, & Vagge, 1999).

Mental Models

Mental models explain human cognitive processes of understanding external reality,

translating reality into internal representations and utilizing it in problem solving (Park &

Gittelman, 1995). According to Allen (1997), mental models are usually considered the way in

which people model processes. This emphasis on process distinguishes mental models from

other types of cognitive organizers such as schemas. A mental model synthesizes several steps of

a process and organizes them as a unit. A mental model does not have to represent all of the steps

which compose the actual process (e.g., the model of a computer program or a detailed account

of the computer’s transistors) (Allen, 1997). Mental models may be incomplete and may even be

internally inconsistent. The representation of a mental model is, obviously, not the same as the

real-world processes it is modeling (Allen, 1997).

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According to Kalyuga, Ayers, Chandler, and Sweller (2003), the process of mental

imagining is closely associated with constructing and running mental representations in working

memory. Because inexperienced learners have no appropriate schemas to support this process,

attempts to engage in imagining are likely to fail. When asked to study worked examples rather

than imagine procedures, novices can construct schemas of interacting elements, an essential first

step to learning (Kalyuga, Ayers, Chandler, & Sweller, 2003). Models of mental models may be

termed conceptual models. Conceptual models include: metaphor; surrogates; mapping, task-

action grammars, and plans. Mental model formation depends heavily on the conceptualizations

that individuals bring to a task (Park & Gittelman, 1995).

Elaboration and Reflection

Elaboration and reflection are processes involved to the development of schemas and

mental models. Elaborations are used to develop schemas whereby nonarbitrary relations are

established between new information elements and the learner’s prior knowledge (van

Merrienboer, Kirshner, & Kester, 2003). Elaboration consists of the creation of a semantic event

that includes to to-be-learned items in an interaction (Kees & Davies, 1990). There are at least

two kinds of elaboration: automatic; controlled and nonautomatic. Automatic elaboration is

carried out by well mastered mental processes over which a person exercises little conscious

control, and which are carried out with great ease in large chunks. Such elaborations usually

result from repeated practice and training (Salomon, 1983). Elaboration can also be controlled

and nonautomatic, requiring attention and effort. These elaborations are generally applied to

new, complex, or otherwise less practiced material. In support of CLT, controlled, effortful

elaborations improve learning because the are better recalled, the include more generated

references, and they include integration of the material in memory (Salomon, 1983).

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With reflection, learners are encouraged to consider their problem-solving process and to

try to identify ways of improving it. For instance, they are encouraged to reflect on the problems

that they have missed or to try to explain how to generate the correct solution, a process that can

increase the likelihood that the correct solution procedure will be internalized by the learner

(Atkinson, Renkl, & Merrill, 2003). Reflection is reasoned and conceptual, allowing the thinker

to consider various alternatives. This type of explorative and discover orientation is at the heart

of the developmentally appropriate practices we hope will take place in primary education

(Howland, Laffey, & Espinosa, 1997). According to Chi (2000) the self-explanation effect (aka

reflection or elaboration) is a dual process that involves generating inferences and repairing the

learner’s own mental model.

Meaningful Learning

Meaningful learning is defined as deep understanding of the material, which includes

attending to important aspects of the presented material, mentally organizing it into a coherent

cognitive structure, and integrating it with relevant existing knowledge. Meaningful learning is

reflected in the ability to apply what was taught to new situations; problem solving transfer. In

our research, meaningful learning involves the construction of a mental model of how a causal

system works (Mayer & Moreno, 2003). Meaningful learning results in an understanding of the

basic concepts of the new material through its integration with existing knowledge (Davis, &

Wiedenbeck, 2001).

According to assimilation theory, there are two kinds of learning: rote learning and

meaningful learning. Rote learning occurs through repetition and memorization. It can lead to

successful performance in situations identical or very similar to those in which a skill was

initially learning. However, skills gained through rote learning are not easily extensible to other

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situations, because they are not based on deep understanding of the material learning.

Meaningful learning, on the other hand, equips the learner for problem solving and extension of

learned concepts to situations different from the context in which the skill was initially learned

(Davis, & Wiedenbeck, 2001; Mayer, 1981).Meaningful learning takes place when the learner

draws connections between the new material to be learned and related knowledge already in

long-term memory, known as the “assimilative context” (Ausubel, 1963; Davis, & Wiedenbeck,

2001).

Metacognition

The term metacognition is used in two distinct ways: the conscious and purposeful

reflection on various aspects of knowing and learning, and the unconscious regulation of

knowledge structures and learning that some information-processing theorists posit to be under

the control of executive processes (Clements & Nastasi, 1999). Metacognition, or the

management of cognitive processes, involves goal-setting, strategy selection, attention, and goal

checking (Jones, Farquhar, & Surry, 1995). Many cognitive models include the executive

processes of selecting, organizing, and integrating. Selecting involves paying attention to the

relevant pieces of information in the text (Harp & Mayer, 1998). Organizing involves building

internal connections among the selected pieces of information, such as causal chains (Harp &

Mayer, 1998). Integrating involves building external connections between the incoming

information and prior knowledge existing in the learner’s long-term memory (Harp & Mayer,

1998).

Cognitive self-regulation refers to students being actively engaged in their own learning,

including analyzing the demands of school assignments, planning for and mobilizing their

resources to meet these demands, and monitoring their progress toward completion of

34


assignments (Covington, 2000). Another theoretical construct is the notion of cognitive

strategies. Cognitive strategies include rehearsal strategies, elaboration strategies, organization

strategies, affective strategies, and comprehension monitoring strategies. These strategies are

cognitive events that describe the way in which we process information (Jones, Farquhar, &

Surry, 1995).

Metacognition is a type of cognitive strategy that has executive control over other

cognitive strategies. As the executive controller of cognitive processes, metacognition selects the

appropriate strategy for the task at hand. The selection of a cognitive strategy depends upon the

individual’s understanding of the current problem or cognitive situation. Personal experiences in

solving similar tasks and using various strategies will affect the selection of a cognitive strategy

(Jones, Farquhar, & Surry, 1995)..

Cognitive engagement. According to Corno and Mandinah (1983), there are four forms of

cognitive engagement: self-regulation, task focus, resource management, and recipience. Each

form is defined by the amount of acquisition (alerting, monitoring, and high-level planning) and

transformation (selectivity, connecting, and low-level planning) processes used. Transformative

processes are cognitive processes that directly help in generating knowledge (Corno and

Mandinah, 1983). Examples of transformative processes include hypothesis generation and data

interpretation (de Jong, de Hoog, & de Vries, 1993). Transformation processes (i.e., selecting,

connecting, and planning) have both metacognitive and cognitive features; They can activate

other cognitive schemas that may be relevant for the task (Corno & Mandinah, 1983). According

to de Jong et al. (1993), alertness, monitoring, and high-level planning are predominantly

information acquisition processes; the information is gathered primarily from the environment.

Acquisition processes bound and control the transformation processes. The acquisition processes

35


are viewed as metacognitive because they regulate the transformation processes. The

transformation processes have both metacognitive and cognitive aspects (Corno & Mandinah,

1983). de Jong et al. (1993) defined similar processes, using the term regulative processes, which

combines some aspects of both acquisition and transformation. de Jong et al. (1993) stated that

regulative processes help manage learning through processes such as monitoring, planning, and

verifying, and that monitoring and planning together can be called navigation.

Mental Effort and Persistence

Mental effort is the aspect of cognitive load that refers to the cognitive capacity that is

actually allocated to accommodate the demands imposed by the task; thus, it can be considered

to reflect the actual cognitive load.. Mental effort is defined as “working ‘smarter’ at either a new

or old performance goal” (Condly, Clark, & Stolovitch, in press, p. 1). Mental effort, relevant to

the task and material, appears to be the feature that distinguishes between mindless or shallow

processing on the one hand, and mindful or deep processing, on the other. Little effort is

expended when processing is carried out automatically or mindlessly (Salomon, 1983).

Mindlessness refers to the ostensibly unattentive behavior of otherwise intelligent people; as the

absence of conscious processing (Salomon, 1983). According to Salomon (1983), mindfulness

refers to a cognitively active state characterized by the conscious manipulation of the envisioned

elements (Langer & Imber, 1980).

Motivation generates the mental effort that drives us to apply our knowledge and skills.

“Without motivation, even the most capable person will not work hard” (Clark, 2003, p. 21).

Motivated behavior involves attempting and persisting at academic achievement tasks (Corno &

Mandinah, 1983), and learning is strongly influenced by the amount of mental effort, the depth

or thoughtfulness, learners invest in processing material (Salomon, 1983. However, mental effort

36


investment and motivation are not to be equated. Motivation is a driving force, but for learning to

actually take place, some specific relevant mental activity needs to be activated. This activity is

assumed to be the employment of nonautomatic effortful elaborations (Salomon, 1983).

Goals

Motivation influences both attention and maintenance processes (Tennyson & Breuer,

2002), generating the the mental effort that drives us to apply our knowledge and skills. Without

motivation, even the most capable person will not work hard (Clark, 2003). Easy goals are not

motivating (Clark, 2003). Additionally, it has been shown that individuals without specific goals

(such as “do your best”), do not work as long as those with specific goals, such as “list 70

contemporary authors” (Thompson et al., 2002; Locke & Latham, 2003).

Goal setting theory, according to Thompson et al. (2002), is based on the simple premise

that people exert effort toward accomplishing goals. Goals may increase performance as long as

a few factors are taken into account, such as acceptance of the goal, feedback on progress toward

the goal, a goal that is appropriately challenging, and a goal that is specific (Thompson et al.,

2002). Goal setting guides the cognitive strategies in a certain direction. Goal checking are those

monitoring processes that check to see if the goal has been accomplished, or if the selected

strategy is working as expected. The monitoring process is active throughout an activity and

constantly evaluates the success of other processes. If a cognitive strategy appears not bo be

working, an alternative may then be selected (Jones, Farquhar, & Surry, 1995).

Goal orientation theory is concerned with the prediction that those with high

performance goals and a perception of high ability will exert great effort, and those with low

ability perceptions will avoid effort. (Miller et al., 1996). Once we are committed to a goal, we

must make a plan to achieve a goal. A key element of all goal-directed planning is our personal

37


assessment of the necessary skills and knowledge required to achieve a goal. A key aspect of self

efficacy assessment is our perception of how novel and difficult the goal is to achieve. The

ongoing results of this analysis is hypothesized to determine how much effort we invest in the

goal (Clark, 1999).

Self-Efficacy

A number of items affect motivation and mental effort. In an extensive review of

motivation theories, Eccles and Wigfield (2002) discuss Brokowski and colleagues’ motivation

model that highlights the interaction of the following cognitive, motivational, and self-processes:

knowledge of oneself (including goals and self perceptions), domain-specific knowledge,

strategy knowledge, and personal-motivational states (including attributional beliefs, self-

efficacy, and intrinsic motivation). In a study of college freshmen, Livengood (1992) found that

psychological variables (i.e., effort/ability reasoning, goal choice, and confidence) are strongly

associated with academic participation and satisfaction. And Corno and Mandinah (1983)

commented that students in classrooms actively engage in a variety of cognitive interpretations

of their environments and themselves which, in turn, influence the amount and kind of effort

they will expend on classroom tasks (Corno & Mandinah, 1983).

The more novel the goal is perceived to be, the more effort will be invested until we

believe that we might fail. At the point where failure expectations begin, effort is reduces as we

“unchoose” the goal to avoid a loss of control. This inverted U relationship suggests that effort

problems take two broad forms: over confidence and under confidence (Clark, 1999). The level

of mental effort necessary to achieve work goals can be influenced by adjusting perceptions of

goal novelty and the effectiveness of the strategies people use to achieve goals (Clark, 1999).

38


Self-efficacy is defined as one’s belief about one’s ability to successfully carry out

particular behaviors (Davis, & Wiedenbeck, 2001). Perceived self-efficacy refers to subjective

judgments of how well one can execute a course of action, handle a situation, learn a new skill or

unit of knowledge, and the like (Salomon, 1983). Perceived self-efficacy has much to do with

how a class of stimuli is perceived. The more demanding it is perceived to be the less efficacious

would the perceivers be about it, and the more familiar, easy, or shallow it is perceived, the more

efficacious they would feel in handling it. It follows that perceived self efficacy should be related

to the perception of demand characteristics (the latter includes the perceived worthwhileness of

expending effort), and that both should affect effort investment jointly (Salomon, 1983).

Self-efficacy theory predicts that students work harder on a learning task when they judge

themselves as capable than when they lack confidence in their ability to learn. Self-efficacy

theory also predicts that students understand the material better when they have high self-

efficacy than when they have low self-efficacy (Mayer, 1998). Effort is primarily influenced by

specific and detailed self efficacy assessments of the knowledge required to achieve tasks (Clark,

1999). Peoples’ belief about whether they have the skills required to succeed at a task is perhaps

the most important factor in the quality and quantity of mental effort people invest in their work

(Clark, 2003).

Expectancy-Value Theory. Related to self-efficaty theories, expectancy-value theories

propose that the probability of behavior depends on the value of a goal and expectancy of

obtaining that goal (Coffin & MacIntyre, 1999). Expectancies refer to beliefs about how we will

do on different tasks or activities, and values have to do with incentives or reasons for doing the

activity (Eccles & Wigfield, 2002). From the perspective of expectancy-value theory, goal

hierarchies (the importance and the order of goals) also could be organized around aspects of

39


task value. Different goals may be perceived as more or less useful, or more or less interesting.

Eccles and Wigfield (2002) suggest that the relative value attached to the goal should influence

its placement in a goal hierarchy, as well as the likelihood a person will try to attain the goal and

therefore exert mental effort.

Task value. Task value refers to an individual’s perceptions of how interesting,

important, and useful a task is (Coffin & MacIntyre, 1999). Interest in, and perceived importance

and usefulness of, a task comprise important dimensions of task value (Bong, 2001). Citing

Eccles’ expectancy-value model, Townsend and Hicks (1997) stated that the perception of task

value is affected by a number of factors, including the intrinsic value of a task, its perceived

utility value, and its attainment value. Thus, engagement in an academic task may occur because

of interest in the task, or because the task is required for advancement in some other area

(Townsend & Hicks, 1997). According to Corno and Mandinah (1983), a task linked to one’s

aspirations (a “self-relevant” task) is a key condition for task value (Corno & Mandinah, 1983).

Problem-Solving

Problem solving is the intellectual skill to propose solutions to previously unencountered

problem situations (Tennyson & Breuer, 2002). A problem exists when a problem solver has a

goal but does not know how to reach it, so problem solving is mental activity aimed at finding a

solution to a problem (Baker & Mayer, 1999). Problem solving is cognitive processing directed

at transforming a given situation into a desired situation when no obvious method of solution is

available to the problem solver (Mayer, 1990, as cited in Baker & Mayer, 1999).

Thinking strategies represent a continuum of conditions ranging from a low-order of

automatic recall of existing knowledge to a high-order of constructing knowledge. From low to

high, the strategies are recall, problem solving, and creativity (Tennyson & Breuer, 2002). Recall

40


strategies involve an automatic differentiation of knowledge from the existing knowledge base.

With recall involving more complex situations, the integration of all appropriate schemas is

required to succeed at a task (Tennyson & Breuer, 2002).

Problem solving is associated with situations dealing with previously unencountered

problems, requiring the integration of knowledge to form new knowledge (Tennyson & Breuer,

2002). A first condition of problem solving involves the differentiation process of selecting

knowledge that is currently in storage using known criteria. Concurrently, this selected

knowledge is integrated to form a new knowledge. Cognitive complexity within this condition

focuses on elaborating the existing knowledge base (Tennyson & Breuer, 2002). Problem solving

may also involve situations requiring the construction of knowledge by employing the entire

cognitive system. Therefore, the sophistication of a proposed solution is a factor of the person’s

knowledge base, level of cognitive complexity, higher-order thinking strategies, and intelligence

(Tennyson & Breuer, 2002).

The highest order of human cognitive processing is the creating of a problem situation.

Rather than having the external environment dictate the situation, the individual, internally,

creates the need or problem. Creativity seems to involve both the conscientious deliberations of

differentiation and integration and the spontaneous integrations that operate at a metacognitive

level of awareness (Tennyson & Breuer, 2002).

According to Mayer (1998), successful problem solving depends on three components—

skill, metaskill, and will—and each of these components can be influenced by instruction.

Metacognitiion—in the form of metaskill—is central in problem solving because it manages and

coordinated the other components (Mayer, 1998). Mayer suggests that the most obvious way to

improve problem solving performance is to teach the basic skills. One approach is to break apart

41


a task into its component skills and then systematically teach each skill to mastery (part task

component training). In this approach, any large task can be broken down into a collection of

“instructional objectives” (Mayer, 1998).

O’Neil’s Problem Solving model. O’Neil’s Problem Solving model (O’Neil, 1999; see

figure 1 below) is based on Mayer and Wittrock’s (1996) conceptualization: “Problem solving is

cognitive processing directed at achieving a goal when no solution method is obvious to the

problem solver” (p. 47). This definition is further analyzed into components suggested by the

expertise literature: content understanding or domain knowledge, domain-specific problem-

solving strategies, and self-regulation (see, e.g., O’Neil, 1999, in press). Self-regulation is

composed of metacognition (planning and self-checking) and motivation (effort and self-

efficacy). Thus, in the specifications for the construct of problem solving, to be a successful

problem solver, one must know something (content knowledge), possess intellectual tricks

(problem-solving strategies), be able to plan and monitor one’s progress towards solving the

problem (metacognition), and be motivated to perform (effort and self-efficacy; O’Neil, 1999,

pp. 255-256).

Fig 1. O’Neil’s Problem Solving Model

42


In problem solving, the skeletal structures are instantiated in content domains, so that a

set of structurally similar models for thinking about problem solving is applied to science,

mathematics, and social studies. These models may vary in the explicitness of problem

representations, the guidance about strategy (if any), the demands of prior knowledge, the focus

on correct procedure, the focus on convergent or divergent responses, and so on (Baker &

Mayer, 1999). Domain-specific aspects of problem solving (the part that is unique to geometry,

geology, or genealogy) involve the specific content knowledge, the specific procedural

knowledge in the domain, any domain-specific cognitive strategies (e.g., geometric proof, test

and fix), and domain specific discourse (O’Neil, 1998, as cited in Baker & Mayer, 1999). Both

domain-independent and domain-dependent knowledge are usually essential for problem solving.

Domain-dependent analyses focus on the subject matter as the source of all needed information

(Baker & O’Neil, 2002).

Games and Learning

ContentUnderstanding

Problem-SolvingStrategies

Self-Regulation

DomainSpecific

DomainIndependent

Metacognition Motivation

Planning Self-Monitoring

Effort Self-Efficacy

Problem Solving

43


In additional to their commercial popularity, computer games have captured the attention

of training professionals and educators, for a number of reasons. First, there has been a major

shift in the field of learning from a traditional, didactic model of instruction to a learner-centered

model that emphasizes a more active learner role. This represents a shift away from the

instructivist model of instruction, where students primarily listen, to one in which students learn

by doing (Garris, Ahlers, & Driskell, 2002). With active participation mind, Moreno and Mayer

(2002) suggest that because some media may enable instructional methods that are not possible

with other media, it might be useful to explore instructional methods that are possible in

immersive environments but not in others.

According to Mikropoulos (2001), the physical structure of the human brain is affected

by the way it is used. Different kinds of experiences configure the brain, especially children’s

brains. Mikropoulos further argues that the reorganization of children’s brains is an important

factor in the educational process, specifically in the case of the involvement of media and and

educational technology. Virtual learning environments provide users with experiences they

would otherwise not be able to experience in the physical world, and provide educational

environments for students to concentrate, perceive. In particular, Mikropoulos found that

subjects were more attentive when navigating in the virtual world. Less mental effort was used in

the real world version of tasks than in the virtual version as a result of less attentional demands,

as a result of less eye-movement and alpha signal dimunition (Mikropoulos, 2001).

Simulation in educational computing is a widely employed technique to teach certain

types of complex tasks (Tennyson & Breurer, 2002). The purpose of using simulations is to teach

a task as a complete whole instead of in successive parts, where learning the numerous variables

simultaneously is necessary to fully understand the whole concept (Tennyson & Breuer, 2002).

ADD SOMETHING REGARDING GAMES.

Learner Control

In contrast to more traditional technologies that simply deliver information, computerized

learning environments offer greater opportunities for interactivity and learner control. These

envrironments simply offer sequencing and pace control, or they can allow the learner to decide

which, and in what order, information was be accessed (Barab, Young, & Wang, 1999). The

44


term navigation refers to a process of tracking one’s position in an environment, whether

physical or virtual, to arrive at a desire destination. A route through the environment consists of

either a series of locations or a continuous movement along a path (Cutmore, Hine, Maberly,

Langford, & Hawgood, 2000).

Hypermedia environments divide information into a network of multimedia nodes

connected by various links (Barab, Bowdish, & Lawless, 1997). In a hypermedia environment,

learners are able to make navigational choices by activating clickable areas, allowing them to

jump from one location to another (Barab, Young, & Wang, 1999). According to Chalmers

(2003), how easily learners become disoriented in a hypermedia environment may be a function

of the user interface (Chalmers, 2003). One area where disorientation can be a problem is in the

use of links. Although links create the advantage of exploration, there is always the chance that

the explorer may get lost, not knowing where they were, where they are going, or where they are

(Chalmers, 2003). In a virtual 3-D environment, Cutmore, Hine, Maberly, Langford, & Hawgood

(2000) argue that navigation becomes problematic when the whole path cannot be viewed at

once but is largely occluded by objects in the environment. These can include walls or large

environmental objects such as trees, hills, or buildings. Under these conditions, once cannot

simply plot a direct visual course from the start to finish locations. Rather, knowledge of the

layout of the space is required (Cutmore, Hine, Maberly, Langford, & Hawgood, 2000).

Effective navigation of a familiar environment depends upon a number of cognitive

factors. These include working memory for recent information, attention to important cues for

location, bearing and motion, and finally, a cognitive representation of the environment which

becomes part of a long-term memory, a cognitive map (Cutmore, Hine, Maberly, Langford, &

Hawgood, 2000). The foundation and implications of CLT can be especially well investigated in

45


the context of multimedia learning, because the use of this technology as instructional medium

involves perceiving and processing information in different presentation modes and sensory

modalities. A process theory that supplements CLT in the description of the cognitive processes

in multimedia learning was introduced by Mayer (2001) as the generative theory of multimedia

learning (Brunken, Plass, & Leutner, 2003).

Two of the principle foundations of the generative theory of multimedia learning are the

dual-coding assumption and the dual-channel assumption (Brunken, Plass, & Leutner, 2003).

According to Brunken, Plass, and Leutner (2003), the generative theory of multimedia learning

combines these two assumptions with a gerative approach to learning (Wittrock, 1974, 1990) by

stating that learners actively select relevant visual and verbal information from the learning

material and organize them in visual and verbal working memory, respectively, by building

associative connections between them (Brunken, Plass, & Leutner, 2003). Learners then

integrate the mental representations as well as prior knowledge by building referential

connections (Mayer, 2001).

Message complexity, stimulus features, and additional cognitive demands inherent in

hypermedia, such as learner control, may combine to exceed the cognitive resources of some

learners (Daniels & Moore, 2000). Dillon and Gabbard, 1998 found that novice and lower

aptitude students have the greatest difficulty with hypermedia. Children are particularly

susceptible to the cognitive demands of interactive computer environments, According to

Howland, Laffey, and Espinosa (1997), many educators believe that young children do not have

the cognitive capacity to interact and make sense of the symbolic representations of computer

environments. Early childhood educators believe that young children learn best by investigating

with their senses, by examining that which is tactile and tangible, not by interacting with virtual

46


environments. Simply because interactive computer envionments are labeled as a concrete

activity, it cannot be assumed that children’s involvement with computers necessarily results in

high quality learning (Howland, Laffey, & Espinosa, 1997).

New technologies, such as the use of multimedia, can afford rich opportunities for

constructivist approaches in the field of education (Bailey, 1996), Although learners are not

physically active in the multimedia environment, it may possible to promote some degree of

cognitive activity that results in constructivist learning (Mayer, Moreno, Boire, & Vagge, 1999).

Yet Howland, Laffey, and Espinosa (1997) contend that the challenge of computer-using primary

educators is to find and use computational environments that meet the requirements of presenting

meaningful and manipulable developmentally appropriate activities which do not simply rely on

experiential cognition which may defeat the educational purpose of the activity.

As will be argued in the next section, when that load is unnecessary and so interferes with

schema acquisition and automation, it is referred to as extraneous or ineffective cognitive load.

Cognitive theorists spend much of their time devising alternative instructional design and

procedures that reduce extraneous cognitive load compared to conventionally used procedures

(Paas, Renkl, & Sweller, 2003). One potential source for extraneous cognitive load is learner

control. In spite of the intuitive and theoretical appeal of hypertext environments, empirical

findings yield mixed results with respect to the learning benefits of learner control over program

control of instruction (Niemiec, Sikorski, & Wallberg, 1996; Steinberg, 1989). And six extensive

meta analyses of distance and media learning studies in the past decade have found the same

negative or weak results (see Bernard, et al, 2003).

Reducing Cognitive Load

47


Cognitive load researchers have identified up to three types of cognitive load. All agree

on intrinsic cognitive load (Brunken, Plass, & Leutner, 2003; Paas, Renkl, & Sweller, 2003;

Renkl, & Atkinson, 2003), which is the load involved in the process of learning; the load

required by metacognition, working memory, and long-term memory. Another load agreed upon

is extraneous load. However, it is the scope of this load that is in dispute. To some researchers

(??), any load that is not intrindic load is extraneous load. To other researchers, non-intrinsic load

is divided into germane cognitive load and extraneous load. Germane load is the load required to

process the intrinsic load (Renkl, & Atkinson, 2003). From a non-computer-based perspective,

this could include searching a book or organizing notes, in order to process the to-be-learned

information. From a computer-based perspective, this could include the interface and controls a

learner must interact with in order to be exposed to and process the to-be-learned material. These

researchs see extraneous cognitive load as the load caused by any unnecessary stimuli, such as

fancy interface designs or unnecessary modalities, such as extraneous sounds (Brunken, Plass, &

Leutner, 2003).

For each of the two working memory subsystems (visual/spatial, and auditory/verbal), the

total amount of cognitive load for a particular individual under particular conditions is defined as

the sum of intrinsic, extraneous, and germane load induced by the instructional materials.

Therefore, a high cognitive load can be a result of a high intrinsic cognitive load (i.e., a result of

the nature of the instructional content itself). It can, however, also be a result of a high

extraneous or germane cognitive load (i.e., a result of activities performed on the materials that

result in a high memory load). In other wors, the same learning material can induce different

amounts of memory load when different instructional strategies and designs are used for its

presentation, because the different cognitive tasks required by these strategies and designs are

48


likely to result in varying amounts of extraneous and germane load (Brunken, Plass, & Leutner,

2003).

Low-element interactivity refers to environments where each element can be learned

independently of the other elements, and there is little direct interaction between the elements.

High-element interactivity refers to environments where there is so much interaction between

elements that they cannot be understood until all the elements and their interactions are

processed simultaneously. As a consequence, high-element interactivity material is difficult to

understand (Paas, Renkl, & Sweller, 2003). Element interactivity is the driver of intrinsic

cognitive load, because the demands on working memory capacity imposed by element

interactivity are intrinsic to the material being learned. Different materials differ in their levels of

element interactivity and thus intrinsic cognitive load, and they cannot be altered by instructional

manipulations; only a simpler learning task that omits some interacting elements can be chosen

to reduce this type of load (Paas, Renkl, & Sweller, 2003).

Germane or effective cognitive load. Germane cognitive load is influenced by the

instructional design. The manner in which information is presented to learners and the learning

activities required of learners are factors relevant to levels of germane cognitive load. Whereas

extraneous cognitive load interferes with learning, germane cognitive load enhances learning

(Renkl, & Atkinson, 2003).

Extraneous cognitive load (Renkl, & Atkinson, 2003) is the most controllable load, since

it is caused by materials that are unnecessary to instruction. Howerver, those same materials may

be important for motivation. Unnecessary items are global referred to as extraneous. However,

another category of extraneous items, seductive details (Mayer, Heiser, & Lonn, 2001), refer to

highly interesting but unimportant test segments. These segments usually contain information

49


that is tangential to the main themes of a story, but are memorable because they deal with

controversial or sensational topics (Schraw, 1998). The seductive detail effect is the reduction of

retention caused by the inclusion of extraneous details (Harp & Mayer, 1998) and affects both

retention and transfer (Moreno & Mayer, 2000). Seductive details are details that not part of the

to-be-learned material but tend to enhance the presentation of the material. According to the

distraction hypothesis, seductive details do their damage by “seducing” the reader’s selective

attention away from the important information.

A possible solution is to leave the details, but guide the learner away from them and to

the relevant information (Harp & Mayer, 1998). Animation can avoid being distracters to

learning if it is clear to the learner that the animation (e.g. a moving spaceship) is not part of the

to-be learned material (Rieber, 1996). Further complicating the issue of seductive details is the

arousal theory which suggests that adding entertaining auditory adjuncts will make a learning

task more interesting, because it creates a greater level of attention so that more material is

processed by the learner (Moreno & Mayer, 2000).

While attempting to focus on a mental activity, most of us, at one time or another, have

had our attention drawn by extraneous sound (Banbury, Macken, Tremblay, & Jones, 2001). The

more relevant and integrated sounds are, the more they will help students’ understanding of the

materials (Moreno & Mayer, 2000). On the surface, seductive details and auditory adjuncts seem

similar. However, the underlying cognitive mechanisms are quire different. Whereas seductive

details seem to prime inappropriate schemas into which incoming information is assimilated,

auditory adjuncts seem to overload auditory working memory (Moreno & Mayer, 2000a).

According to Brunken, Plass, and Leutner (2003), both extraneous and germane cognitive

load can be manipulated by the instructional design fo the learning material (Brunken, Plass, &

50


Leutner, 2003). Among the instructional strategies that have been found to reduce extraneous

cognitive load and optimize germane cognitive load are worked examples (Kalyuga, Chandler,

Tuovinen, & Sweller, 2001; see question 3); goal-free activities (Sweller, 1999); and activities

that are based on the completion effect (van Merrienboer, Schuurman, de Croock, & Paas, 2002),

modality effect (Brunker & Leutner, 2001; Mayer & Moreno, 2003; Sweller, 1999), and

redundancy effect (Sweller, 1999) as cited in Brunker, Plass, and Leutner, 2003).

Cognitive Effects

A number of theories grounded in Cognitive Load Theory (CLT) have been devised to

account for the influence of various conditions on learning and cognition. Each of these effects

are tied to cognitive and metacognitive processes. The theories, categorized as effects include:

the split-attention effect (Mayer & Moreno, 1998; Mousavi, Low, & Sweller, 1995; Tramizi &

Sweller, 1988; Yeung, Jin, & Sweller, 1997), contiguity effect (Mayer, 1997; Mayer & Moreno,

2003; Mayer, Moreno, Boire, & Vagge, 1999; Mayer & Sims, 1994; Moreno & Mayer, 1999),

modality effect (Mayer, 2001; Mayer & Moreno, 2003; Moreno & Mayer, 1999; Mousavi, Low,

& Sweller, 1995; Moreno & Mayer, 2002), coherence effect (Mayer, Heiser, & Lonn, 2001;

Moreno & Mayer, 2000), redundancy effect (Kalyuga, Ayers, Chandler, & Sweller, 2003;

Mayer, Heiser, & Lonn, 2001; Yeun, Jin, & Sweller, 1997), and expertise reversal effect

(Kalyuga, Ayers, Chandler, & Sweller, 2001; Kalyuga, Ayers, Chandler, & Sweller, 2003).

Split attention effect. When dealing with two or more related sources of information (e.g.,

text and diagrams), it’s often necessary to integrate mentally corresponding representations

(verbal and pictorial) to construct a relevant schema to achieve understanding. When different

sources of information are separated in space or time, this process of integration may place an

unnecessary strain on limited working memory resources (Atkinson, Derry, Renkl, & Wortham,

51


2000; Mayer & Moreno, 1998). Intensive search-and-match processes may be involved in cross-

referencing the representations and may severely interfere with constructing integrated schemas,

thereby increasing the burden on working memory and hindering learning (Kalyuga, Chandler, &

Sweller, 1998; Kalyuga, Chandler, & Sweller, 2000; Mayer & Moreno, 1998; Mayer & Moreno,

2003; Moreno & Mayer, 1999; Sweller, 1999; Mousavi, Low, & Sweller, 1995). Tramizi and

Sweller (1988) labeled this phenomenon the split-attention effect.

Contiguity effect. From the dual-coding theory, it is expected that meaningful learning

occurs in working memory when multiple modes of information are process and linked with

referential connection. This in turn leads to better transfer effects. Therefore, if the material is not

presented concurrently, this process is ill-supported. (Mayer & Sims, 1994). There are two forms

of the contiguity effect: spatial contiguity and temporal contiguity. Temporal contiguity occurs

when one piece of information is presented prior to other pieces of information (Mayer, 1997;

Mayer & Moreno, 2003; Mayer, Moreno, Boire, & Vagge, 1999; Moreno & Mayer, 1999).

Spatial contiguity occurs when modalities are physically separated (Mayer & Moreno, 2003).

Contiguity results in split-attention (Moreno & Mayer, 1999).

Modality effect. The modality effect may best illustrate how these principles allow for

design of multimedia instruction that enhances learning outcomes. Focusing on the sensory

modality of information, this principle states that knowledge acquisition is better facilitated by

materials presented in a format that simultaneously uses the auditory and the visual sensory

modalities, than by a format that uses only the visual modality (Mayer, 2001; Mayer & Moreno,

2003; Moreno & Mayer, 1999). With dual modalities representing to two sensori inputs, the total

load induced by this variant of the instructional materials is distributed among the visual and the

auditory system (Brunken, Plass, & Leutner, 2003; Kalyuga, Ayers, Chandler, & Sweller, 2003).

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Modality effects appear to be consistent across non-, low-, and high-immersive environments

(Moreno & Mayer, 2002).

Coherence effect. The coherence effect refers to situations in which adding words or

pictures to a multimedia presentation results in poorer performance on tests of retention or

transfer (Mayer, Heiser, & Lonn, 2001). The coherence principle or theory holds that auditory

adjuncts can overload the auditory channel (or auditory working memory). Any additional

material (including sound effects and music) that is not necessary to make the lesson intelligible

or that is not integrated with the rest of the materials will reduce effective working memory

capacity and thereby interfere with the learning of the core material, and therefore, resulting in

poorer performance on transfer tests (Moreno & Mayer, 2000a).

Redundancy Effect. The locus of the redundancy effect seems to be at the point of visual

attentional scanning, as posited by the split-attention hypothesis. The onscreen text competes

with the animation for visual attention, thus reducing the chances that the learner will be able to

attend to relevant aspects of the animation and text (Mayer, Heiser, & Lonn, 2001). Physical

integration of two or more sources of information to reduce split attention and cognitive load is

important if the sources of information are essential in the sense that they are not intelligible in

isolation for a particular learner. Alternatively, if they sources are intelligible in isolation with

one source unnecessary, elimination rather than physical integration of the redundant source is

preferable (Kalyuga, Ayers, Chandler, & Sweller, 2003; Kalyuga, Chandler, & Sweller, 1998;

Kalyuga, Chandler, & Sweller, 2000; Yeung, Jin, & Sweller, 1997).

The redundancy effect can also affect the value of worked examples (see question 3 for

an explanation of worked examples). For more experienced learners, some of the worked

example information may be unnecessary, because the information is already know to the learner

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and, therefore, redundant. Trying to incorporate that redundant information with the schema

already in working memory can create more cognitive load than necessary and even overload

working memory (Kalyuga, Chandler, Tuovinen, & Sweller, 2001). According to a cognitive

theory of multimedia learning, not all techniques for removing redundancy are equally effective.

For example, in the case of multimedia explanations consisting of animation, narration, and on-

screen text, one effective solution is to remove the on-screen text, but it does not follow that the

same benefits would occur by instead removing the narration (Mayer, Heiser, & Lonn, 2001).

Expertise reversal effect. Experts bring their activated schemas to the process of

constructing mental representations of a situation or task. They may not need any additional

instructional guiadance because their schemas provide full guidance. Therefore, if instruction

provides information designed to assist learners in constructing appropriate mental

representations, and experts are unable to avoid attending to this information, there will be an

overlap between the schema-based and the redundant instruction-based components of guidance

(Kalyuga, Ayers, Chandler, & Sweller, 2003). Cross-referencing and integration of redundant

components will require additional working memory resources and might cause a cognitive

overload. This additional cognitive load may be imposed even if a learner recognizes the

instructional materials to be redundant and so decides to ignore that information as best has he or

she can (Kalyuga, Ayers, Chandler, & Sweller, 2003). For more experienced learners, rather than

risking conflict between schemas and instruction-based guidance, it may be preferable to

eliminate the instruction-based guidance (Kalyuga, Ayers, Chandler, & Sweller, 2003).

Conclusion

According to Brunken, Plass, and Leutner (2003), Sweller (1999) distinguished three types of load: one type that is attributed to the inherent structure and complexity of the instructional materials and cannot be influenced by the instructional designer, and two types that

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are imposed by the requirements of the instruction and can, therefore, be manipulated by the instructional designer (Brunken, Plass, & Leutner, 2003).

The cognitive load caused by the structure and complexity of the material is called intrinsic cognitive load. The complexity of any given content depends on the level of item or component interactivity of the material, that is, the amount of information units a learner needs to hold in working memory to comprehend the information (Pollock, Chandler, & Sweller, 2002, as cited in Brunken, Plass, & Leutner, 2003). Cognitive load imposed by the format and manner in shich information is presented and by the working memory requirements of the instructional activities is referred to as extraneous cognitive load, a term that highlights the fact that this load is a form of overhead that does not contribute to an understanding of the materials (Brunken, Plass, & Leutner, 2003). Finally, the load induced by learners’ efforts to process and comprehend the material is called germane cognitive load (Gerjets & Scheiter, 2003; Renkl & Atkinson, 2003; as cited in Brunken, Plass, & Leutner, 2003).

Multimedia learning occurs when students use information presented in two or more formats to construct knowledge. This definition also applies to the term multimodal, since learners are exposed to more than one sense modality, rather that multimedia, which refers to the idea that the instructor uses more than one presentation medium (Mayer & Sims, 1994).

Mayer defines multimedia as the presentation of information in two or more formats, such as in words and pictures (Mayer, 1997; Mayer & Moreno, 1998).

Dreary intellectually, predictable pedagogically, despite cuter, more active graphics, our learning systems will need massive rethinking to make them useful for the challenges facing instruction for both children and adults (Baker & O’Neil, 2002, p. 611).

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3. Review the theoretical and empirical literature on the impact of scaffolding on

learning. Include a discussion of types (e.g., graphical scaffolding) and contexts (e.g.,

K-12).

There are a number of definitions of scaffolding in the literature. Chalmers (2003) defines

scaffolding as the process of forming and building upon a schema (Chalmers, 2003). Van

Merrionboer, Kirshner, and Kester (2003) defined the original meaning of scaffolding as all

devices or strategies that support students’ learning. More recently, van Merrienboer, Clark, and

de Croock (2002) defined scaffolding as the process diminishing support as learners acquire

more expertise. Allen (1997) defined scaffolding as the process of training a student on core

concepts and then gradually expanding the training. For the purpose of this review, all four

definitions of scaffolding will be considered.

Instructional methods are external representations of internal cognitive processes that are

necessary for learning but which learners cannot or will not provide for themselves (Clark,

2001). They provide learning goals (e.g., demonstrations, simulations, and analogies: Alessi,

2000; Clark 2001), monitoring (e.g, practice exercises), feedback (Alessi, 2000; Clark 2001;

Leemkull, de Jong, de Hoog, & Christoph, 2003), and selection (e.g., highlighting information:

Alessi, 2000; Clark, 2001). In addition, Alessi (2000) adds: giving hints and prompts before

student actions; providing coaching, advice, or help systems; and providing dictionaries and

glossaries. Jones, Farquhar, and Surry (1995) add advance organizers, graphical representations

of problems, and hierarchical knowledge structures. Each of these examples is a form of

scaffolding.

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When learning by doing in physical environments is not feasible, learning by doing can

be implemented using computer simulations. In learning by doing in a virtual environment,

students actively work in realistic situations that simulate authentic tasks for a particular domain

(Mayer, Mautone, & Prothero, 2002). A major instructional issue in learning by doing within

simulated environments concerns the proper type of guidance, that is, how best to create

cognitive apprenticeship (Mayer, Mautone, & Prothero, 2002). Mayer, Mautone, and Prothero

(2002) commented that their research shows that discovery-based learning environments can be

converted into productive venues for learning when appropriate cognitive scaffolding is

provided; specifically, when the nature of the scaffolding is aligned with the nature of the task,

such as pictorial scaffolding for pictorially-based tasks and textual-based scaffolding for

textually-based tasks. For example, in a recent study, the Mayer, Mautone, and Prothero (2002)

found that students learned better from a computer-based geology simulation when they are

given some support about how to visualize geological features, versus textual or auditory

guidance.

Due to the limited number of pages allotted to this review, not all forms of scaffolding

will be discussed. In particular, this review will examine worked examples, visual scaffolding,

and interface scaffolding, including issues related to learner control.

Worked Examples

If the instructional presentation fails to provide necessary guidance, learners will have to

resort to problem-solving search strategies that are cognitively inefficient, because they impose a

heavy working memory load (Kalyuga, Ayers, Chandler, & Sweller, 2003). Worked examples

(or worked out examples) are one form of effective guidance. Worked out examples usually

consist of a problem formulation, solution steps, and the final solution itself (Atkinson, Renkl, &

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Merrill, 2003; Renkl, & Atkinson, 2003; Renkl, Atkinson, Maier, & Staley, 2002). The notion of

worked examples indicates that the example phase is lengthened so that a number and variety of

examples are presented before learners are expected to engage in problem solving (Atkinson,

Derry, Renkl, & Wortham, 2000) or, alternatively, examples are interspersed with the to-be-

solved problem, which is an effective format (Mwangi & Sweller, 1998; Renkl & Atkinson,

2003).

Although there is no precise definition of worked examples, they share certain family

resemblances. As instructional devices, they typically include a problem statement and a

procedure for solving the problem; together, these are meant to show how other similar problsm

might be solved. In a sense, they provide an expert’s problem-solving model for the learner to

study and emulate (Atkinson, Derry, Renkl, & Wortham, 2000).

The worked examples literature is particularly relevant to programs of instruction that

seek to promote skills acquisition, the goal of many workplace training environments as well as

instructional programs in domains such as music, chess, and athletics (Atkinson, Derry, Renkl, &

Wortham, 2000, p. 185). Research indicates that exposure to worked-out examples is critical

when learners are in the initial stages of learning a new cognitive skill in well structured domains

such as mathematics, physics, and computer programming (Atkinson, Renkl, & Merrill, 2003).

The current view of worked examples suggests that examples can help educators achieve the

goal of fostering adaptive, flexible transfer among learners (Atkinson, Derry, Renkl, &

Wortham, 2000).

Value of worked examples. Learning is a constructive process in which a student convert

words and examples generated by a teacher or presented in a text into usable skills, such as

problem solving (Chi, Bassok, Lewis, Reimann, & Glaser, 1989). During the solving of practice

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problems, novices focus on goal attainment (i.e., solving the problem) thus leaving little

cognitive capacity for learning. In contrast, the use of various worked examples frees cognitive

capacity for more rapid knowledge acquisition because this range of examples presents

categories of problems in their initial state and illustrates correct steps for that problem type; the

very information that should be encoded in a schema (Carroll, 1994).

According to van Merrienboer, Clark, and de Croock (2002), automation is mainly a

function of the amount and quality of practice that is provided to the learners and eventually

leads to automated rules that directly control behavior. Strong models (schema) allow for both

abstract and case-based reasoning (van Merrienboer, Clark, & de Croock, 2002). A schema is

defined as a cognitive construct that permits problem-solvers to recognize a problem as

belonging to a specific category requiring particular moves for solutions (Tarmizi & Sweller,

1988). If a learner has acquired appropriate automated schemas, cognitive load will be low, and

substantial working memory resources are likely to be free. Schemas enable another use of the

same knowledge in a novel situation, because they contain generalized knowledge, or concrete

cases, or both, which can serve as an analogy (van Merrienboer, Clark, & de Croock, 2002).

Some material can be learned element by element without relating one element to

another. Learning a vocabulary provides an example. Such material is low in element

interactivity and low in intrinsic cognitive load. Alternatively, situations where a number of

elements must be considered simulataneously for the successful execution of a task are called

high element interactivity tasks. Under these circumstances, intrinsic cognitive load is high

because of high elemnent interactivity. These situations can occur often in mathematics,

computer programming, design development, etc. (Tuovinen & Sweller, 1999). Complex

learning alway involves achieving integrated sets of learning goals; It has little to do with

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learning separate skills in isolation (van Merrienboer, Clark, & de Croock, 2002). There is

overwhelming evidence conventional problems (real-world problems) are complex and,

therefore, exceptionally expensive in terms of working memory capacity (van Merrienboer,

Kirshner, & Kester, 2003).

Failing the possession of a schema to generate steps, the learner can still solve a problem

using a means-end strategy, working backward, rather than forward (Tarmizi & Sweller, 1988).

Means-end strategies are unrelated to schema construction and automation and are cognitively

costly because they impose heaving working memory load (Kalyuga, Ayers, Chandler, &

Sweller, 2003). Providing worked examples instead of problems eliminates the means-ends

search and directs a learner’s attention toward a problem state and its associated moves

(Kalyuga, Ayers, Chandler, & Sweller, 2003).

Learning tasks that take the form of worked examples confront learners not only with a

given state and a desired goal state but also with an example solution. Studying those examples

as a substitute for performing conventional problem solving tasks focuses attention on problem

states and associated solution states and enables learners to induce generalized solutions or

schemas (van Merrienboer, Kirshner, & Kester, 2003). A disadvantage to worked examples is

that they do not force learners to study them carefully. (van Merrienboer, Kirshner, & Kester,

2003).

According to Atkinson, Derry, Renkl, and Wortham (2000), learning from worked

examples causes learners to develop knowledge structures representing important, early

foundations for understanding and using the domain ideas that are illustrated and emphasized by

the instructional examples provide. Through use and practice, these representations are expected

to evolve over time to produce the more sophisticated forms of knowledge that experts use

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(Atkinson, Derry, Renkl, & Wortham, 2000). High school students, ages 15 to 17, who were

given worked examples required less acquisition time, needed less direct instruction, made fewer

errors, and made fewer types of errors during practice, as compare to students students who did

not receive worked examples. The worked examples were helpful to students defined as lower

achievers students indentified as learning disabled (Carroll, 1994).

However, Mwangi and Sweller (1998) warn that instructional formats that require

students to split their attention between multiple sources of information can interfere with

learning. In an exaperiment involving 22 eighth grade students, it was shown that in many areas,

conventionally used techniques such as worked examples imposed cognitive loads as heavy as

those imposed by conventional problems. Worked examples that require students to attend to

multiple sources of information which then must be mentally integrated are cognitively

demanding and interfere rather than facilitate learning (Tarmizi & Sweller, 1988).

The cognitive load associated with any task, including learning from worked examples,

consists of two parts. There is the intrinsic or natural cognitive load, that is, the inherent aspects

of the mental task that must be understood for the learner to be able to carry out the task.

Intrinsic load is determined by levels of element interactivity. However, in addition, there is

usually a range of extraneous matters associated with the way the instructional material is taught

that may add to the inherent nucleus of the intrinsic load. This category of cognitive load is

classified as extraneous cognitive load (Tuovinen & Sweller, 1999). Worked examples that are

not designed correctly can lead to a number of cognitive effects, such as the split-attention effect,

coherence effect, modality effect, and redundancy effect. See question 2 for a description of

these various effects.

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Elaboration. From the viewpoint of information presentation, learners should be

encouraged to connect newly presented information to already existing schemas, that is, to what

they already know. This process of elaboration is central to the instructional design of

information (van Merrienboer, Clark, & de Croock, 2002). Elaboration are used to develop

schemas whereby nonarbitrary relations are established between new information elements and

the learner’s prior knowledge (Atkinson, Derry, Renkl, & Wortham, 2000; van Merrienboer,

Kirshner, & Kester, 2003). According to Kees and Davies (1988), because the use of elaboration

is more spontaneous among older subjects, the mental effort required to activate and implement

this strategy sound decrease with age. The researchers suggest that because older subjects have a

richer repertoire of schema, it probably requires less effort on their part to elaborate (Kee &

Davies, 1988).

Tuovinen and Sweller (1999) argued that the effectiveness of worked examples clearly

depends on the previous domain knowledge of the students. If students have sufficient doman

knowledge, the format of practice is irrelevant, and discovery or exploration practice is at least as

good, or may even be better, than worked-examples practice. However, if the students’ previous

domain knowledge is restricted, than worked-examples practice can be more beneficial than

exploration practice (Tuovinen & Sweller, 1999). In this experiment using 32 university

students, Tuovinen and Sweller found that combining worked examples and problem solving

produced better learning for students totally unfamiliar with the new domain, but exploration

practice was just as good as this combined approach for students with some domain experience.

Potential problems associated with worked examples. The results of a study by Carrol

(1994) suggest that worked examples helped to prevent the practice of incorrect solutions and

learning incorrect associations, because the examples provided a scaffolding for learning,

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illustrating the correct form of equations, the proper use of symbols, and the meaing of words.

Further, these benefits carried over during homework and on posttest, even after the examples

were no longer available (Carroll, 1994). Yet, Atkinson, Renkl, and Merril (2003) warn that

although worked-out examples have significant advantages, their use as a learning methodology

does not, of course, guarantee effective learning.

Critics to worked examples may claim that students exposed to worked examples are not

able to solve problems with solutions that deviate from those illustrated in the examples, can not

clearly recognize appropriate instanced in which the learned procedures can be applied, and have

difficulty solving problems for which they have no worked examples (Atkinson, Derry, Renkl, &

Wortham, 2000).

Research on expertise suggests that people construct increasingly more accurate problem

schemas as they gain more experience in a domain. In particular, experts are more likely to sort

problems on the basis of structural features and less likely to sort on the basis of surface features

compared to novices (Quilici & Mayer, 1996). Chi, Bassok, Lewis, Reimann, and Glaser (1989)

found that higher achieving college students tended to study example exercises by explaining

and providing justifications for each action, whereas lower achieving students often do not

explain the example exercises to themselves. And when they do, their explanations do not seem

to connect with their understanding of the principles and concepts the example (Chi, Bassok,

Lewis, Reimann, & Glaser, 1989). Further, lower achieving students use examples in a very

different way from higher achieving students. In general, higher achieving students, during

problem solving, use the examples for a specific reference, whereas lower achieving students

reread them as if to search for a solution (Chi, Bassok, Lewis, Reimann, & Glaser, 1989).

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Lower ability students tend to focus on surface features unless primed to do otherwise,

while higher ability students tend to focus on structural features. Therefore, worked examples

designed to focus on of structural features will be more effective for lower ability students than

for higher ability students (Quilici & Mayer, 1996). Exposure to structural-emphasizing

examples promotes structural schema construction more than exposure to surface-emphasizing

examples (Quilici & Mayer, 1996). Overall, exposure to examples influences students’ structural

schema construction, especially when the examples emphasize structural characteristics rather

than surface characteristics and when the students are lower in mathematical knowledge rather

than highr in mathematical knowledge (Quilici & Mayer, 1996). According to van Merrienboer,

Clark, and de Croock (2002), learners are very good at inducing plausible patterns given

adequate examples. However, when working from examples alone, learners will initially look for

niave direct correspondence between their current problem and the examples, rather than trying

to extrapolate the underlying meaning from the example to the new problem. Providing

meaningful learning can occur if there are enough examples for the learners to see the unfolding

patterns (van Merrienboer, Clark, & de Croock, 2002).

Expertise reversal effect. In later stages of skill acquisition, emphasis is on increasing

speed and accuracy of performance, and skills, or at least subcomponents of them, should be

automated. During these stages, it is important that learners actually solve problems as opposed

to studying them (Renkl & Atkinson, 2003).

As a learner’s experience in a domain increases, solving a problem may not require a

means-end search and its associated working memory load, because of a now partially, or even

fully, constructed schema or schemas. When a problem can be solved relatively effortlessly,

analyzing a redundant worked example and integrating it with previously acquired schemas in

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working memory may impose a greater cognitive load than problem solving. In this instance,

termed the expertise reversal effect (Renkl & Atkinson, 2003), learning outcomes may be

negatively affected for experts. Under these circumstances, practice in problem solving may

result in more effective learning than studying worked examples, because solving problems may

adequately facilitate further schema construction and automation (Kalyuga, Ayers, Chandler, &

Sweller, 2003).

Worked examples are most appropriate when presented to novices, but they should be

gradually faded out with increased levels of learner knowledge and replace by problems

(Kalyuga, Ayers, Chandler, & Sweller, 2003; Renkl & Atkinson, 2003). The processes of fading

involve removal of solution steps, until all that remains is the problem (Renkl, & Atkinson,

2003). According to Atkinson, Renkl, and Merrill (2003), this approach is related of Vygotsky’s

(1978) “zone of proximal development” in which problems or tasks are provided to learners that

are slightly more challenging than they can handle on their own. Instead of solving the problems

or tasks independently, the learner must rely—at least initially—on the assistance of their more

capable peers and/or instructors to succeed. Learners will eventually make a smooth transition

from relying on modeling (worked examples) to scaffolded problem solving (faded or partial

examples) to independent problem solving (Atkinson, Renkl, & Merrill, 2003). In other words,

this model advocates the fading of instructional scaffolding during the transition. Partial worked

examples provide a scaffold that permits learners to solve problems they could not successfully

solve on their own. The instructional scaffolding—in the shape of worked-out solution steps—is

gradually faded in our learning environment (Atkinson, Renkl, & Merrill, 2003).

Intrinsic load gradually decreases over the course of cognitive skill acquisition so that a

gradual increase of problem-solving demands is possible without imposing an excessive load.

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When understanding is acquired, self-explanation activities become extraneous and problem

solving is germane (see question 2; Renkl & Atkinson, 2003). According to Renkl & Atkinson

(2003), a way to remove worked examples is through stages. First, a complete example (a model)

is presented. Second, an example is given in which one solution step is omitted (coached

problem solving). van Merrionboer, Kirshner, and Kester (2003) refer to faded worked examples

as completion tasks.

Backward fading refers to when final steps are removed before all earlier steps are

removed (Renkl & Atkinson, 2003). In a study involving college students, fading clearly fostered

near but far transfer performance. However, when backward fading was used, far transfer was

significant too (Renkl & Atkinson, 2003). According to Atkinson, Renkl, and Merrill (2003),

thier findings on the usefulness of a learning environment that combines fading worked-out steps

with self-explanation prompts support the basic tenets of one of the most predominant,

contemporary instructional models, namely the cognitive apprenticeships approach (Collins,

Brown, & Newman, 1989). This approach suggests that learners should work on problems with

close scaffolding provided by a mentor or instructor (Atkinson, Renkl, & Merrill, 2003). The

backward-fading condition may be more favorable because the first problem-solving demand is

imposed later as compared with forward fading. In the latter condition, the first to-be-determined

step might come before the learner has gained an understanding of the step’s solution, so that

solving the step may impose a heavy cognitive load (Renkl & Atkinson, 2003).

Visual Scaffolding

According to Allen (1997), selection of appropriate text and graphics can aid the

development of mental models, and Jones, Faruher, and Surry (19950 commented that visual

cues such as maps and menus as advance organizers help learners conceptualize the organization

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of the information in the program (Jones, Farquhar, & Surry, 1995). A number of researchers

support the use of maps as visual aids and organizers (Benbasat & Todd, 1993: Chou & Lin,

1998; Ruddle et al, 1999, Chou, Lin, & Sun, 2000; Farrell & Moore, 2000-2001)

Chalmers (2003) commented defines graphic organizers is organizers of information in a

graphic format, which act as spatial displays of information that can act as study aids. Jones,

Farquhar, and Surry (1995) argued that interactive designers should provide users with visual or

verbal cues to help them navigate through unfamiliar territory. Overviews, menus, icons, or other

interface design elements within the program should serve as advance organizers for information

contained in the program (Jones, Farquhar, & Surry, 1995). In addition, the existence of

bookmarks is important to enable recovering from an eventual possibility of disorientation; loss

of place (Dias, Gomes, & Correia, 1999). However, providing such support devices does not

guarantee learners will use them. For example, in an experiment involving a virtual maze,

Cutmore, Hine, Maberly, Langford, and Hawgood (2000) found that, while landmarks provided

useful cues, males utilized them significantly more often than females.

The loss of orientation and “vertigo” feeling which often accompanies learning in a

virtual-environment is minimized by the display of a traditional, two-dimensional dynamic map.

The map helps to navigate and to orient the user, and facilitates an easier learning experience

(Yair, Mintz, & Litvak, 2001). Dempsey, Haynes, Lucassen, and Casey (2002) also commented

that an overview of player position was considered an important feature in adventure games.

A number of experiments have examined the use of maps in virtual environments. Chou

and Lin (1998) and Chou, Lin, and Sun (2000) examined various map types, with some offering

access to global views of the environment and others offering more localized views, based on the

learner’s location. In their experiments using over one hundred college students, they found that

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any form of map produced more efficient navigation of the site as well as development of

cognitive maps (concept or knowledge maps), as compared to having no map. Additionally, the

global map results for navigation and concept map creation were significantly better than any of

the local map variations or the lack of map, while use of the local maps was not significantly

better than not having a map. This suggests that, while the use of maps is helpful, the nature or

scope of the map influences its effectiveness (Chou & Lin, 1998).

Interface ScaffoldingInterface metaphors are often discussed in Human-Computer Interaction (HCI) literature

as they pertain to interface design. Interface metaphors work by exploiting previous user

knowledge of a mental model (Berg, 2000). The interface of a contemporary Computer-Based

Instruction (CBI) program frequently can be likened to a control panel from which users access

information in an often sophisticated and complicated piece of software (Jones, Farquhar, &

Surry, 1995). Computers make wide use of the graphical user interface (GUI). This interface

operates on the metaphorical premise of direct manipulation and engagement by the user.

Authors of hypermedia constructs are relying heavily on the avaialblity of windows, icons,

menus, and pointer systems in producing and implementing software presentations (Brown &

Schneider, 1992). Three types of interfaces are defined by the literature, based on their

interaction style: conversational (or command), direct manipulation, and menu.

The conversational interface requires the user to read and interpret either words or

symbols which tell the computer to perform operations and processes (Brown & Schneider,

1992). In conversational interfaces, the user typically uses a keyboard to type commands telling

the computer what he or she wants to have happen.

The direct manipulation interface (DMI) is defined as one in which the user has direct

interaction with the concept world; the domain (Brown & Schneider, 1992). Broadly defined,

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direct manipulation interfaces represent the physical manipulation of a system of interrelated

objects analogous to objects found in the real world. While the object representations may take

on a variety of forms, they are most commonly represented as icons; although it is possible to

provide text-based implementation of the objects or combined text-icon presentations (Benbasat

& Todd, 1993). DMIs allow users to carry out operations as if they were working on the actual

objects in the real world. The gap between the user’s intentions and the actions necessary to

carry them out is small. These two characteristics of direct manipulation are referred to as

engagement and distance (Wiedenbeck & Davis, 1997). Engagement is defined as a feeling of

working directly with the objects of interest in the world rather than with surrogates (Frohlic,

1997; Wiedenbeck & Davis, 2001). Distance is a cognitive gap between the user’s intentions and

the actions needed to carry them out (Frohlich, 1997; Wiedenbeck & Davis, 2001). With direct

manipulation the distance is reduced by presenting the user with a predefined list of visible

options that allow the user to click and drag familiar objects in a well-understood context (e.g.,

the Windows or Macintosh desktop metaphor). High engagement with small distance leads to a

feeling of directness in a system (Wiedenbeck & Davis, 1997).

Menu interface. In a menu style of interaction, objects and possible actions are

represented by a list of choices, usually as text. Menus are similar to direct manipulation in that

they help guide the user which, a with direct manipulation, reduces mental burden. The menu

may help to structure the task and eliminate syntactic errors (Wiedenbeck & Davis, 1997).

However, menu-based systems are generally less direct than DMIs because the hierarchical

structure of the menus provide a kind of syntax that the user must learn. As a result, users do not

feel as directly connected to the objects they are manipulating through their actions (Wiedenbeck

& Davis, 1997).

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Comparing interfaces. A number of studies have been conducted comparing command,

direct manipulation, and menu interfaces; some with consistent results and some without. The

findings of studies comparing menu to command line interfaces have been relatively consistent.

Overall, recognition and categorization may be faster for pictures than text (Benbasat & Todd,

1993). Menu interfaces lead to better task performance than the command interfaces, which is

attributed to a smaller gap between user intentions and actions with menu interfaces.

(Wiedenbeck & Davis, 2001). The results of studies comparing DMI to menu or DMI to

command line have been less consistent.

Widenbeck and Davis (1997) suggested that direct manipulation interfaces lead to more

positive perceptions of ease of use than does a command interface. With elementary school

students, Brown and Schneider (1992) found DMI more comfortable and enhanced the speed of

problem solving. DMI was also less frustrating compared to the conversational interface. Sein et

al. (1993), contended that because a direct manipulation interface provides an “explicit,

comprehensible, analogical conceptual model of the computer system, it can reduce the demands

placed upon subjects to internalize system states, which in turn leads to improved performance”

(p. 615). In support of this view, de Jong et al. (1993) found direct manipulation interfaces

enhanced the efficiency of task performance for both simple and complex tasks, with the

improved performance more pronounced for complicated tasks.

Other findings for direct manipulation interfaces are mixed or unclear. In an analysis of

empirical studies into the benefits of icons, and therefore direct manipulation, Benbasat and

Dodd (1993) found no clear advantage for the use of icons. According to Kaber et al. (2002),

although direct manipulation may minimize cognitive distance and maximize engagement, the

interface is less effective from the perspective of repetitive or complex tasks, particularly those

75


where one action is to affect multiple objects. They argue that, to achieve semantic directness

(the distance between the user’s intentions and the objects and operations provided by the

system), the user should be able to communicate those intentions to the system in a simple and

concise manner at all times. The need for repetitive actions in order to affect multiple objects is

not supported by DM and, therefore, increases mental effort and the amount of time needed to

complete a task (Kaber et al., 2002).

In a comparison of the DMI to the command interface, Westerman (1997) found that the

performance strategies of novices were relatively insensitive to command complexity while

experts were aware of this factor and used the command line less frequently as complexity

increased. And with regards to experts, Frohlich (1997) found that performance slows, rather

than speeds up, with direct manipulation interfaces, for two reasons. First, as was also suggested

by Kaber et al. (2002) and Westerman (1997), the language of DM limits complex actions.

Second, use of familiar real-world metaphors may limit users to existing ways of doing things;

while this may make learning and remembering easier for novices, it is more constraining for

experts. In communications, Frohlich (1997) found that direct manipulation interfaces increase

the cognitive load on conversational partners, even though it decreased the interactional work

between them.

A number of causes have been suggested to account for the discrepancies in the findings

for direct manipulation interfaces. Eberts and Brittianda (1993) questioned the validity of

interface comparison studies. They suggested that comparing performance differences across

interface design is difficult because the predicted execution times are intrinsically different for

each interface and, therefore, difficult to compare (Eberts & Brittianda, 1993). In contrast,

Benbasat and Todd (1993) argued that direct manipulation interfaces are often compared to both

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command or menu type interfaces in studies. The menu interface eliminates the confounding

effect of time on performance found with command line. Also, since the menu interface is

usually made up of menu panels containing a list of options which may be words or icons, the

selection of menus facilitates an experimental design to test the main and interaction effects of

direct manipulation versus menus, and text versus icon-based interfaces (Benbasat & Todd,

1993).

A final possible confound in the findings with regards to direct manipulation interfaces

may be due to how specific interface implementations are defined. Many so called direct

manipulation interfaces include elements from several interface styles, and are more accurately

referred to as mixed mode interfaces (Frohlich, 1997). They include menus and windows, as well

as conversational interaction such dialog boxes, fill-in forms, and command languages (de Jong

et al., 1993; Phillips, 1995). The Macintosh operating system is one such example. While it is

typically referred to as a direct manipulation interface, it covers a range of interactions involving

a pointing device and keyboard for menu selection, dragging, and drawing, along with dialogue

boxes and text entry (Phillips, 1991). Pure direct manipulation interfaces according to the

framework would be “model-world interfaces based on Action in/Action out modality involving

only the media of sound, graphics, and motion. Dialog boxes, forms, and short-cut commands

are not part of this definition” (Frohlich, 1997, p. 478). Using this framework, many interfaces

which have traditionally been thought of as direct manipulation interfaces are in actuality mixed

mode interfaces (Frohlich, 1997).

Learner Control

Learner control gives “…learners control over elements of a computer-assisted

instructional program. (Hannafin & Sullivan, 1995, p. 19). Simple user interaction in a

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multimedia explanation refers to user control over the words and pictures that are presented in

the multimedia explanation—namely, the pace of the presentation. Simple user interaction may

affect both cognitive processing during learning and the cognitive outcome of learning (Mayer &

Chandler, 2001). There is disagreement among researchers as the the value of, and prescribed

use of, learner control. Giving learners control and autonomy over an environment can either

facilitate learning or lead to disorientation and confusion (Dias, Gomes, & Correia, 1999).

It has been said that learner control wields a double-edged sword; for some users, it can

extend their intellectual performance, while for others, it may not facilitate performance—

possibly even leaving the user lost in a maze of information (Barab, Young, & Wang, 1999). In

traditional forms of navigation, one must determine spatial position in relation to landmarks or

astral location to decide on the means of moving toward a goal. In a virtual world, the feeling of

being lost while navigating may result from a lack of connection among the physical

representations of the world (Baylor, 2001). Disorientation is defined as a user’s perception of

his or her uncertainty of location, and is a problem in terms of learning in open-ended learning

environments (Baylor, 2001).

Mayer and Chandler (2001) suggested that interactivity improved learner understanding

only when it was used in a way that minimized cognitive load and allowed for two-stage

construction of a mental model. In a study involving 30-year-old participants, Baylor (2001)

found that users were more used to and more comfortable with navigating the nonlinear format

of websites than when navigating in a linear configuration. Furthermore, the linear mode

exhibited a higher level of disorientation. This disorientation was negatively correlated with the

learner’s ability to generate examples and to define the main point of the content (Baylor, 2001).

Additionally, in support of learner control, Shyu and Brown (1995) found that learner-controlled

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instruction was superior to the program controlled instruction with regard to student performance

in a novel procedural task.

According to the results of a study by Barab, Young, and Wang (1999), increased levels

of learner control are beneficial when students are using a hypertext program to solve a specific

problem. In their study, university students were free to navigate directly to those nodes of

information they deemed appropriate. Students using non-linear navigation did significantly

better at the problem-solving task than those who proceeded through the document in a linear

manner (Barab, Young, & Wang, 1999).

In contast to these few examples, in an extensive meta-analysis of reviews involving

hundreds of studies on learner control, Niemeic, Sikorski, & Walberg (1996) found, after

removing the vast number of experiments that were empirically unsound, that learner control did

not appear to offer special benefits for any particular type of learners or under any specific kinds

of conditions. Baylor (2001) argues that a nonlinear navigation mode may not have the

coherence that would be provided when the learner is forced to process the information in a more

systematic way (from beginning to end). Specifically, in a nonlinear mode, the learner may not

be able to determine how the overall content is globally represented (Baylor, 2001).

SummaryIn their study using the Profile game, the type of guidance ranged from no guidance to

providing illustrations of possible geological features hidden in the game (i.e., pictorial scaffolding) to providing verbal descriptions of how to solve problems in the game (i.e., strategy modeling) to providing both illustrations and verbal strategy descriptions (Mayer, Mautone, & Prothero, 2002).

Students learn better from a computer-based geology simulation when they are given some support about how to visualize geological features. Consistent with the guided discovery hypothesis, the worst performing group in the study was the group that receive the least amount of support beyone basic instruction, and the best performing group was the group that receive the most support (i.e., pictorial aids and verbal strategy modeling; Mayer, Mautone, & Prothero, 2002).

Our research shows that discovery-based learning environments can be converted into productive venues for learning when appropriate cognitive scaffolding is provided; specifically,

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when the nature of the scaffolding is aligned with the nature of the task, such as pictorial scaffolding for pictorially-based tasks and textual-based scaffolding for textually-based tasks (Mayer, Mautone, & Prothero, 2002).

Paralel to research showing students learn more deeply from studying worked esamples (i.e., schema-based learning) than from actually solving the problems (i.e., search-based learning), our findings provide empirical and theoretical justification for providing appropriate cognitive support for learners engaged in learning by doing (Sweller, 1999; as cited in Mayer, Mautone, & Prothero, 2002).

On the theoretical side, this study provides a partial validation of cognitive apprentice theories within a computer-base simulation. On the practical side, it shows that students need support in how to interact with geology simulations, particularly support in bulding and using spatial representations (Mayer, Mautone, & Prothero, 2002).

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