Richard Wainess - University of Southern California · Web viewRichard Wainess Rossier School of Education University of Southern California to Dr. Harold O’Neil (Chair) Dr. Richard

Wainess PhD Qualifying Exam

Running head: 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.

This 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, with a on a number of constructs

attributed to promoting motivation, such as challenge, fantasy, and fun. Last is a discussion of

various learning outcomes associated with games.

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 games 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.

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

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& 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. Betz (1995-1996)

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.

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

1987; Dempsey, Haynes, Lucassen, and Casey, 2002; Garris et al., 2002; Ricci, 1994).

Researchers also agree that games have goals and strategies to achieve those goals (Crookall &

Arai, 1995; Crookall et al. 1987; Garris et al., 2002; Ricci, 1994). Many researchers also agree

that games have competition (Dempsey et al., 2002) and consequences such as winning or losing

(Crookall et al., 1987; Dempsey et al., 2002).

Betz (1995-1996) 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 et al. (1987) also

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

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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. Similarly,

Garris et al. (2002) wrote that key features of simulations are they represent real-world systems,

and Henderson, Klemes, and Eshet (2000) commented that a simulation attempts to faithfully

mimic an imaginary or real environment that cannot be experienced directly, for such reasons as

cost, danger, accessibility, or time. Berson (1996) also argued that simulations allow access to

activities that would otherwise be too expensive, dangerous, or impractical for a classroom. Lee

(1999) added that a simulation is defined as a computer program that relates elements together

through cause and effect relationships.

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. At the risk of introducing a bit more

ambiguity, Garris et al. (2002) proposed that simulations can contain game features, which leads

to the final definition: simulation-games.

Simulation-Games

Garris et al. (2002) argued that 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. 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.

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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

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 et al. (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 et al., 2002). Ricci et al. (1996) defined motivation as

“the direction, intensity, and persistence of attentional effort invested by the trainee toward

training” (p. 297). 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

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the most common measure of continuing motivation is whether a student returns to the same task

at a later time.

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 as to 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 McGrenere (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 &

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

fun as a criteria for motivation.

For interactive games, Stewart (1997) added the motivational importace of goals and

outcomes. Locke and Latham (1990) also commented on the robust findings with regards to

goals and performance outcomes. They argued that clear, specific goals allow 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 following

sections will focus on fantasy, control and manipulation, challenge and complexity, curiosity,

competition, feedback, and fun. The role of goals will be discussed in question 2.

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 et al., 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). Rieber (1996)

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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 described two types of

fantasies: endognenous and exogenous. Endogenous fantasy weaves relevant fantasy into a

game, while exogenous simply sugar coat a learning environment with fantasy. An example of an

endogenous fantasy would be the use of a laboratory 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) noted that endogenous fantasy, not exogenous fantasy, is important to

intrinsic motivation, yet 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) argued 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.

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

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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

et al. (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 outcomes, even if those

actions are not instructionally relevant.

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 (1996) 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

(Malone & Lepper, 1987). Clark (1999) describes this effect as a U-shaped relationship. Stewart

(1997) commented that games that are too easy will be dismissed quickly. According to Garris et

al. (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. Th researcher argued that linking activities to

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valued personal competencies, embedding activities within absorbing fantasy scenarios, or

engaging competitive or cooperative motivations could serve to make goals meaningful (Garris

et al. 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 and cognitive

curiosity is the evoked by the desire for knowledge (Garris et al. 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, which 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 et al. (2002) commented that curiosity is

internal, residing in the individual, and mystery 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 et al. 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 to predict the future (Kagan, 1972), and information

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

Competition

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

preferences and reward structures. Astudy by Porter, Bird, and Wunder (1990-1991) examining

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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 also 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 et al., 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, as compared to competition (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 be provided 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 et al. (1996), within the computer-based game

environment, feedback is provided in various forms including audio cues, score, and remediation

immediately following performance. The researchers argued that these feedback attributes can

produce significant differences in learner attitudes, resulting in increased attention to the learning

environment.

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Fun

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 (1981) three

elements (fantasy, curiosity, and challenge) contribute to the fun in games. 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 because 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 (Rieber et al., 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

the theory’s name (Rieber and Matzko, 2001). Rieber and Matzko (2001) offered a broader

definition of flow commenting 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

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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 (2001) contended that play and flow differ in one respect; learning is an

expressed outcome of serious play but not of flow.

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 (2001), this interaction or

engagement can be used along with the components of Malone and Lepper’s (1987) intrinsic

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

Garris et al. (2002) commented that training professional are interested in the intensity of

involvement and engagement that computer games can invoke, to harness the motivational

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

Learning and Other Outcomes for Games

Simulations and games have been cited as beneficial for 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, Prince, Shrestha, Oser, & Salas,

1993), military mission preparation (Spiker & Nullmeyer, n.d.), laboratory simulation (Betz,

1995-1996), chemistry and physics education (Khoo & Koh, 1998), urban geography and

planning (Adams, 1998; Betz, 1995-1996), farm and ranch management (Cross, 1993), language

training (Hubbard, 1991), disaster management (Stolk, Alexandrian, Gros, & Paggio, 2001), and

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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

(Leemkuil, de Jong, de Hoog, & Christoph, 2003), and media buying (King & Morrison, 1998).

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

skills that transfer 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 college students, that video game practice could significantly alter the

strategies of spatial attentional deployment.

According to Ricci et al. (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 (such as, the quality of the

design of video games) reinforces the attraction of the game but not necessarily its educational

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interest. 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 et al. (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 Leemkuil et al., (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

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.

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 et al., 2002). In other words, outcomes are affected by

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the instructional strategies employed (Wolfe, 1997). Leemkuil et al. (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.

In meta-analyzing a number of studies and meta-analyses of 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 (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) 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. Berson (1996) argued that, with regards to research into the effectiveness of

computers in social studies, methodological problems persist in the areas of 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).

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), which includes reflection, appears to be an essential contribution to research on play and

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gaming in education (Brougere, 1999; Leemkuil et al., 2003; Thiagarajan, 1998). According to

Garris et al. (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 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 et al. (2002) argued that

learning by doing must be coupled with the opportunity reflect and abstract relevant information

for effective learning to occur.

Summary

One of the largest issues in game and simulation research has been a lack of agreed upon

definitions. A common definition is that simulations model processes and encourage

manipulation of inputs and assessment of outputs to discover cause-effect relationships. Games

are competitive experiences which are bound by rules to achieve goals. In addition, a third

category, simulation-game combines characteristics of both media. One of the most touted

aspects of games and simulation games is motivation, which is instantiated in fantasy, control

and manipulation, challenge and complexity, curiosity, competition, feedback, and fun. As was

argued, there is little support for the concept of fun, but there is support for the related concepts

of engagement and flow.

Under the section on learning outcomes from games and simulations, the inconsistent

research results were discussed. While a number of articles have professed significant learning

outcomes, as both retention and transfer, far more articles (as evaluated through reviews and

meta-analyses) have either found negative results for games or questionable results. One

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important requirement to promote the possible educational benefits of games is the use of

reflection and debriefing. Both these practices involve the process of analyzing experiences and

outcomes to help develop relevant schema.

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References for Question 1

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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 schemas 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 describable interpretation of a problem space,

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

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

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

learning is defined as a 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 various forms of cognitive load

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

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

Cognitive Load Theory

Cognitive load theory (CLT), which began in the 1980s, 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). Brunken, Plass, and Leutner (2003) argued 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 et al.,

2003). 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 and Chandler, 1994, Yeung, 1999).

Researchers have proposed that working memory limitations can have an adverse effect on

learning (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

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construct has a causal dimension reflecting the interaction between task and learner

characteristics, and an assessment dimension reflecting the measurable concepts of mental load,

mental effort, and performance (Paas et al., 2003). Cognitive load is a theoretical construct,

describing the internal processes of information processing that cannot be observed directly

(Brunken et al., 2003).

Working Memory

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 et al. (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). 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 et al., 2003).

Long-Term Memory

According to Paas et al. (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, 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

et al., 2003). Noyes and Garland (2003) contended that information that is not held in working

memory will need to be retained by the long-term memory system. Storing more knowledge in

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long-term memory reduces the load on working memory, which results in a greater capacity

being made available for active processing.

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 et al., 1995).

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

schemas (Paas et al., 2003).

Schema Development

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

elements 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

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, et al., 1998; Mousavi et al., 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 et al., 2003). If a schema can be brought into working

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

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leaving more resources available to search for a possible solution problem (Kalyuga et al., 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 et al., 1998; Paas et al., 2003).

Mental Models

Mental models explain human 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 (Allen, 1997). Mental models may be incomplete and may even be internally

inconsistent. Models of mental models are 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

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that includes the to-be-learned items in an interaction (Kees & Davies, 1990). With reflection,

learners are encouraged to consider their problem-solving process and to try to identify ways of

improving it (Atkinson, Renkl, & Merrill, 2003). Reflection is reasoned and conceptual, allowing

the thinker to consider various alternatives (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 (Mayer & Moreno,

2003). Meaningful learning is reflected in the ability to apply what was taught to new situations;

problem solving transfer. 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 learned. However, skills gained through rote learning are not easily extensible to other

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

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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

Metacognition, or the management of cognitive processes, involves goal-setting, strategy

selection, attention, and goal checking (Jones, Farquhar, & Surry, 1995). According to Harp and

Mayer (1998), many cognitive models include the executive processes of selecting, organizing,

and integrating. Selecting involves paying attention to the relevant pieces of information.

Organizing involves building internal connections among the selected pieces of information,

such as causal chains. Integrating involves building external connections between the incoming

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

1998).

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 et al., 1995). Metacognition is a type of cognitive strategy that has executive control over

other cognitive strategies. Prior experience in solving similar tasks and using various strategies

will affect the selection of a cognitive strategy (Jones et al., 1995).

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, 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

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automatically or mindlessly (Salomon, 1983). 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). However, mental effort 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. 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

(Thompson, Meriac, & Cope, 2002; Locke & Latham, 2002).

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. 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.

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

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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). 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 will be reduced as we ‘unchoose’ the goal in order 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).

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).

Expectancy-Value Theory. Related to self-efficacy 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). 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).

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 associated with situations

dealing with previously unencountered problems, requiring the integration of knowledge to form

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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). 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. Metacognition—in the form of metaskill

—is central in problem solving because it manages and coordinates the other components

(Mayer, 1998).

O’Neil’s Problem Solving model. O’Neil’s Problem Solving model (O’Neil, 1999) 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, 2002). 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

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(metacognition), and be motivated to perform (effort and self-efficacy; O’Neil, 1999, pp. 255-

256).

In problem solving, the skeletal structures are instantiated in content domains. Domain-

specific aspects of problem solving (e.g., 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 (Baker

& O’Neil, 2002).

Games and Learning

The inclusion of game in education 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 in 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. 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). In addition to teaching domain-specific skills, games have

been used to impart more generalizable skills. Space Fortress, a 2-D, simplistic arcade-style

game, was utilized to improve spatial and motor skills that transfer far outside gameplay, such as

significantly improving the results of fighter pilot training (Day et al., 2001). Green and Bavelier

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(2003) and Greenfield, DeWinstanley, Kilpatrick, and Kaye (1994) showed the potential of video

games to significantly alter visual selection attention.

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, as well

as continued motivation. Garris et al. (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. 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. Lee (1999) also

commented that for educational game related research to be effective in informing the literature,

we need to know instructional mode, instructional sequence, knowledge domain, and learner

characteristics that were involved in the study. Thiagarajan (1998) commented that, if not

embedded with sound instructional design, games and simulations often end up truncated

exercises often mislabeled as simulations. 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).

Learner Control

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

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

environments can 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 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 et al., 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 et al., 2000).

Hypermedia environments divide information into a network of multimedia nodes

connected by various links (Barab, Bowdish, & Lawless, 1997). 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 et al. (2000) argue that navigation

becomes problematic when the whole path cannot be viewed at once but is largely occluded by

objects in the environment. Under these conditions, one cannot simply plot a direct visual course

from the start to finish locations. Rather, knowledge of the layout of the space is required

(Cutmore et al., 2000).

37


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.

One potential source for extraneous cognitive load (discussed in the next section) 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 (Bernard, et al, 2003).

Reducing Cognitive Load

Cognitive load researchers have identified up to three types of cognitive load. All agree

on intrinsic cognitive load (Brunken et al., 2003; Paas et al., 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

38


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, in contast to germane load.

These researchs see extraneous cognitive load as the load caused by any unnecessary stimuli,

such as fancy interface designs or extraneous sounds (Brunken et al., 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 germane

cognitive load (i.e., a result of activities performed on the materials that result in a high memory

load) or high extraneous load (i.e., a result of inclusion of unnecessary information or stimuli that

result in a high memory load; Brunken et al., 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 et al., 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. Reduction in intrinsic load can occur by dividing the materials into

small learning modules (Paas et al., 2003).

39


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. However, those same materials may

be important for motivation. Unnecessary items are globally referred to as extraneous. However,

another category of extraneous items, seductive details (Mayer, Heiser, & Lonn, 2001), refers to

highly interesting but unimportant elements or instructional segments. These segments usually

contain information 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).

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). A possible solution is to leave the details, but guide the learner away from them

and to the relevant information (Harp & Mayer, 1998).

While attempting to focus on a mental activity, most of us, at one time or another, have

had our attention drawn by extraneous sounds (Banbury, Macken, Tremblay, & Jones, 2001). On

the surface, seductive details and auditory adjuncts (such as sound effects or music) seem

similar. However, the underlying cognitive mechanisms are quire different. Whereas seductive

40


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 et al. (2003), both extraneous and germane cognitive load can be

manipulated by the instructional design of the learning material (Brunken et al., 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 et al., 1995; Yeung, Jin, & Sweller,

1997), contiguity effect (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 et al., 1995; Moreno & Mayer, 2002), and the coherence effect

(Mayer, Heiser, & Lonn, 2001; Moreno & Mayer, 2000). The redundancy and expertise reversal

effects are discussion under question 3.

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,

2000; Mayer & Moreno, 1998).

Contiguity effect. 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 & Moreno, 2003; Mayer et al., 1999; Moreno &

41


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 refers to having multiples elements of information

presented by the same channel, either auditory or visual, thereby overloading that channel. By

having dual modalities representing the two sensory inputs, the total load induced by this variant

of the instructional materials is distributed among the visual and the auditory system (Brunken et

al., 2003; Kalyuga, Ayers, Chandler, & Sweller, 2003). Modality effects appear to be consistent

across non-, low-, and high-immersive environments (Moreno & Mayer, 2002).

Coherence effect. 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, 2000).

Summary

Cognitive load theory defines a limited working memory with related auditory and visual

receptors, and an unlimited long-term memory that can hold a massive number of schemas that,

through practice, can be fully automated. Cognitive load refers to the amount of mental activity

imposed on working memory. This load can be control through effective instructional

interventions.

Meaningful learning refers to a deep understanding of material, and is reflected in the

ability to apply knowledge to novel situations. According to assimilation theory, rote learning,

42


which involves repetition and memorization, does not lead to meaningful learning. However,

skills gained through rote learning may be required later for meaningful learning.

Metacognition refers to the executive processes of working memory that select, organize,

and integrate information, in the process of learning; learning involves integration of new

information with existing knowledge. Rehearsal, elaboration, organization, affective, and

comprehension monitoring strategies are all related to metacognition.

Mental efforts and persistence refer to the continued application of cognitive effort that is

required for learning. A number of factors affect mental effort: goals, self-efficacy, and

expectancy-value beliefs. Problem solving can only occur with mental effort. Problem solving is

the mental activity aimed at finding a solution to a novel problem. Problem solving involves

domain-specific and domain-independent strategies, content understanding, and self-regulation,

which includes metacognition and motivation.

Games and simulations as learning tools are subject to all components of cognitive load

theory. Therefore, for an instructional game or simulation to be effective, it must be informed by

sound instructional design that takes advantage of the various cognitive strengths and limitation.

Learner control is one area involved in games and simulations where there is little agreement.

While the majority of empirically sound studies suggest that learner control imposes undue

cognitive load, there are a number of respected researchers and studies which differ from that

view.

Cognitive load can be divided into three types of load: intrinsic load, germane load, and

extraneous load. Intrinsic load is the working memory load imposed by the process of learning

new material; developing new schemas. Germane load is the working memory load imposed by

the processes required in order to learn the material. And extraneous load is the working memory

43


load caused by stimuli (e.g., auditory or visual) that are present, yet are neither required for, nor

add to, the learning experience. Related to these three loads are a number of theories on

instructional methods that cause undue load and methods for removing that extra load.

44



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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).

Due to the limited number of pages allotted for this review, not all forms of scaffolding

will be discussed. Specifically, this review will examine worked examples, graphical scaffolding,

and interface scaffolding, including scaffolding issues related to learner control. 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). In a related definition, 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.

As defined by Clark (2001), instructional methods are external representations of internal

cognitive processes that are necessary for learning but which learners cannot or will not provide

for themselves. They provide learning goals (e.g., demonstrations, simulations, and analogies:

Alessi, 2000; Clark 2001), monitoring (e.g, practice exercises: Clark, 2001), feedback (Alessi,

2000; Clark 2001; Leemkuil, 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

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providing dictionaries and glossaries. Jones, Farquhar, and Surry (1995) added advance

organizers, graphical representations of problems, and hierarchical knowledge structures. Each

of these examples is a form of scaffolding.

In learning by doing in a virtual environment, students can 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 et al.

2002). Mayer et al. (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 et al. (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.

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 examples usually consist of a

problem formulation, solution steps, and the final solution itself (Atkinson, Renkl, & Merrill,

2003; Renkl, & Atkinson, 2003; Renkl, Atkinson, Maier, & Staley, 2002). With worked

examples, 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, &

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Wortham, 2000) or, alternatively, examples are interspersed with the to-be-solved problem

(Mwangi & Sweller, 1998; Renkl & Atkinson, 2003). Worked examples provide an expert’s

problem-solving model for the learner to study and emulate (Atkinson, Derry et al., 2000).

The worked examples literature is particularly relevant to instructional programs 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 et al.,

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 et al., 2000).

Value of worked examples. Learning is a constructive process in which a student converts

words and examples generated by a teacher or presented in another format into usable skills,

such as problem solving (Chi, Bassok, Lewis, Reimann, & Glaser, 1989). During the solving of

practice problems, novices focus on goal attainment (i.e., solving the problem), leaving little

cognitive capacity for learning. In contrast, the use of various worked examples frees cognitive

capacity for more rapid knowledge acquisition, because the 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, et al. (2002), automation is mainly a function of

the amount and quality of practice provided to a learner and eventually leads to automated rules

that directly control behavior. Strong models (schemas) allow for both abstract and case-based

reasoning (van Merrienboer, Clark, et al., 2002). A schema is defined as a cognitive construct

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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

analogies (van Merrienboer, Clark, et al., 2002).

Some material can be learned element by element without relating one element to

another. Learning a vocabulary is an example. Such material is low in element interactivity and

low in intrinsic cognitive load (see question 2). 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 (Tuovinen & Sweller, 1999). Complex learning alway

involves achieving integrated sets of learning goals; It has little to do with learning separate

skills in isolation (van Merrienboer, Clark, et al., 2002). There is overwhelming evidence that

conventional problems (real-world problems) are complex and, therefore, exceptionally

expensive in terms of working memory capacity (van Merrienboer, Kirshner, et al., 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).

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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 create generalized solutions or

schemas (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

(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).

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 level 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; see question 2). Mwangi and

Sweller (1998) warned that instructional formats that require students to split their attention

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between multiple sources of information (see question 2) can interfere with learning. In an

experiment 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.

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 non-arbitrary relations are established between new information and the

learner’s prior knowledge (Atkinson, Derry, Renkl, & Wortham, 2000; van Merrienboer,

Kirshner, & Kester, 2003). According to Kees and Davies (1988), elaboration is more

spontaneous among older subjects, hypothesizing that older subjects have a richer repertoire of

schema and, therefore, it probably requires less effort on their part to elaborate.

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.

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Potential problems associated with worked examples. 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

instances in which the learned procedures can be applied, and have difficulty solving problems

without the availability of 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 of a problem space 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 did not explain the example exercises to themselves. And when they did, their

explanations did not seem to connect with their understanding of the principles and concepts the

example (Chi, Bassok, Lewis, Reimann, & Glaser, 1989). In general, higher achieving students,

during problem solving, used 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). 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

specifically designed to focus on of structural features will be more effective for lower ability

students than for higher ability students (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

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and the examples, rather than trying to extrapolate the underlying meaning (structure) from the

example to the new problem (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, to automate

them. 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 attempting to

integrate it with previously acquired schemas in 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 poor for experts. Instead solving problems, rather

than studying worked examples, might adequately facilitate further schema construction and

automation (Kalyuga et al., 2003).

Worked examples are most appropriate when presented to novices, but they should be

gradually faded out with increased levels of learner knowledge and replaced by problems

(Kalyuga et al., 2003; Renkl & Atkinson, 2003). The processes of fading involves 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

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relying on modeling (worked examples) to scaffolded problem solving (faded or partial

examples) to independent problem solving (Atkinson et al., 2003).

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 not far transfer performance. However, when backward fading was used, far transfer

was significant too (Renkl & Atkinson, 2003). According to Atkinson et al., (2003), their

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 apprenticeship approach (Collins, Brown, &

Newman, 1989). This approach suggests that learners should work on problems with close

scaffolding provided by a mentor or instructor (Atkinson et al., 2003). The backward-fading

condition may be more favorable because removing 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).

Graphical Scaffolding

According to Allen (1997), selection of appropriate text and graphics can aid the

development of mental models, and Jones et al. (1995) commented that visual cues such as maps

and menus as advance organizers help learners conceptualize the organization of the information

in a program (Jones et al., 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 also act as study aids. Jones,

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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 interactive program (Jones et al., 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 et al.

(2000) found that, while landmarks provided useful cues, males utilized them significantly more

often than females did.

According to Yair, Mintz, and Litvak (2001), 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. 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 et al. (2000) examined various map types, with some maps 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

any form of map produced more efficient navigation of the site as well as better 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

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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 Scaffolding

Interface 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). 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. 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,

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

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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. 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, 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. Also, users do not feel as directly

connected to the objects they are manipulating through their actions (Wiedenbeck & Davis,

1997).

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.

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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. 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

where one action is to affect multiple objects. The need for repetitive actions in order to affect

multiple objects is not supported by DMI and, therefore, increases mental effort and the amount

of time needed to complete a task (Kaber et al., 2002). 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.

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

65


interface design is difficult because the predicted execution times are intrinsically different for

each interface and, therefore, difficult to compare (Eberts & Brittianda, 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). 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) and would therefore alter DMI findings.

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

multimedia refers to user control over pace of the presention of the words and pictures that are

presented. 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. According to Dias et al.

(1999), giving learners control and autonomy over an environment can either facilitate learning

or lead to disorientation and confusion (Dias et al., 1999).

66


Barab, Young, and Wang (1999) argued 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. Baylor

2001) commented that, 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 accustomed to and more comfortable with navigating the nonlinear

format of websites than when navigating in a linear configuration. Surprisingly, 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).

Also, in support of learner control, Shyu and Brown (1995) found that learner-controlled

instruction was superior to the program controlled instruction with regard to student performance

in a novel procedural task. The results of a study by Barab, Young, and Wang (1999) suggested

that 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 et al., 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

67


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) argued 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.

Summary

Depending upon the research, scaffolding refers to either the methods uses to support

learning or the reduction of those methods until they disappear. Scaffolding is an instructional

methods designed to support schema development, particularly for meaningful learning. One of

the most powerful forms of scaffolding is worked examples.

Work examples provide a problem, solution steps, and the final solution. For novices,

worked examples are particularly important for schema development. Without the example, and

without experience in that problem space or a related problem space, novices tend to adopt a

means-end approach to solving the problem, working from back to front. This approach, will

effective for solving the problem, is ineffective for schema development.

While there are a number of critics to the use of worked examples, most researcher see

worked examples as viable instructional scaffolds. One caveat is the inclusion of elaboration, the

process whereby the learner, analyzes the processes in the example, to extra the underlyin

meanings, and not just the surface characteristics. Another area of agreement is fading, a process

whereby solution steps are removed one at a time, until all that is left is the problem. A backward

fading approach, where the last steps are removed first, appears to be the most effective. A third

area that all researchers agree on is the danger of giving worked examples to experts. Because

68


the expert already contains the relevant schema, worked examples can results in adding extra

cognitive load, in the attempt to relate the information presented in the problem with the links

already embedded in their schemas.

Graphical scaffolding refers to any form of scaffolding presented as imagery (pictures,

drawings, illustrations, etc.). One very useful form of graphical scaffolding with interacting in

games or other virtual environments is a map. Research supports the use of global maps over

more localized maps. Another form of graphical scaffolding is the computer applications

interface, which provides (a) metaphorical support to stimulate mental model and schema

development and (b) the opportunity for more direct interaction with the environment through

direct manipulation interfaces (DMI). As with other forms of learner control, there is great

debate over the value of DMI and there possible effect on cognitive load.

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