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: wainess@usc.edu
In partial fulfillment of the requirement for the Degree
Doctor of Philosophy in Education in
Educational Psychology and Technology
1
Wainess PhD Qualifying Exam
1.
2
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,
Wainess PhD Qualifying Exam
3
& 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
Wainess PhD Qualifying Exam
4
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.
Wainess PhD Qualifying Exam
5
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 nonlinear, 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
Wainess PhD Qualifying Exam
6
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)
Wainess PhD Qualifying Exam
7
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
Wainess PhD Qualifying Exam
8
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
Wainess PhD Qualifying Exam
9
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
Wainess PhD Qualifying Exam
10
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 intergroup 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.
Wainess PhD Qualifying Exam
11
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
Wainess PhD Qualifying Exam
12
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 allencompassing 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
Wainess PhD Qualifying Exam
13
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 computerbased 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
Wainess PhD Qualifying Exam
14
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
Wainess PhD Qualifying Exam
15
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’ problemsolving 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
Wainess PhD Qualifying Exam
16
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
Wainess PhD Qualifying Exam
17
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.
Wainess PhD Qualifying Exam
18
References for Question 1
Adams, P. C. (1998, March/April). Teaching and learning with SimCity 2000
Anderson, R. C., & Pickett, J. W. (1978). Recall of previously recallable information following a
shift in perspective. Journal of Verbal Learning and Verbal Behavior, 17, 1-12.
Asakawa, T., Gilbert, N. (2003). Synthesizing experiences: Lessons to be learned from Internetmediated simulation games.
Ausubel, D. P. (1963). The psychology of meaningful verbal learning. New York: Grune and
Stratton.
Baker, D., Prince, C., Shrestha, L., Oser, R., & Salas, E. (1993). Aviation computer games for
crew resource management training.
Berson, M. J. (1996, Summer). Effectiveness of computer technology in the social studies: A
review of the literature. Journal of Research on Computing in Education, 28(4), 486499.
Berylne, D. E. (1960). Conflict, arousal, and curiosity. New York: McGraw-Hill.
Betz, J. A. (1995-1996). Computer games: Increase learning in an interactive multidisciplinary
environment.
Brougere, G. (1999, June). Some elements relating to children’s play and adult
simulation/gaming.
Brozik, D., & Zapalska, A. (2002, June). The PORTFOLIO GAME: Decision making in a
dynamic environment.
Clark, R. E. (1999). The CANE model of motivation to learn and to work: A two-stage process
of goal commitment and effort [Electronic Version]. In J. Lowyck (Ed.), Trends in
Corporate Training. Leuven, Belgium: University of Leuven Press.
Wainess PhD Qualifying Exam
19
Clark, R. E. (Ed.).(2001). Learning from Media: Arguments, analysis, and evidence. Greenwich,
CT: Information Age Publishing.
Crookall, D., & Aria, K. (Eds.). (1995). Simulation and gaming across disciplines and cultures:
ISAGA at a watershed. Thousand Oaks, CA: Sage.
Crookall, D., Oxford, R. L., & Saunders, D. (1987). Towards a reconceptualization of
simulation. From representation to reality. Simulation/Games for Learning, 17, 147171.
Cross, T. L. (1993, Fall). AgVenture: A farming strategy computer game.
Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. San Francisco: Jossey Bass.
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal performance. New York:
Cambridge University Press.
Davis, S., & Wiedenbeck, S. (2001). The mediating effects of intrinsic motivation, ease of use
and usefulness perceptions on performance in first-time and subsequent computer users.
Interacting with Computers, 13, 549-580.
Day, E. A., Arthur, W., Jr., & Gettman, D. (2001). Knowledge structures and the acquisition of a
complex skill. Journal of Applied Psychology, 86(5), 1022-1033.
de Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computer
simulations of conceptual domains. Review of Educational Research, 68, 179-202.
deCharms, R. (1986). Personal Causation. New York: Academic Press.
Deci, E. L. (1975). Intrinsic Motivation. New York: Plenum Press.
Dekkers, J., & Donati, S. (1981). The interpretation of research studies on the use of simulation
as an instructional strategy. Journal of Educational Research, 74(6), 64-79.
Wainess PhD Qualifying Exam
20
Dempsey, J. V., Haynes, L. L., Lucassen, B. A., & Casey, M. S. (2002). Forty simple computer
games and what they could mean to educators.
Dillon, A., & Gabbard, R. (1998, Fall). Hypermedia as an educational technology: A review of
the quantitative research literature on learner comprehension, control, and style. Review
of Educational Research, 63(3), 322-349.
Druckman, D. (1995). The educational effectiveness of interactive games. In D. Crookall & K.
Aria (Eds.), Simulation and gaming across disciplines and cultures: ISAGA at a
watershed (pp. 178-187). Thousand Oaks, CA: Sage
Garris, R., Ahlers, R., & Driskell, J. E. (2002). Games, motivation, and learning: A research and
practice model.
Gredler, M.E. (1996). Educational games and simulations: a technology in search of a research
paradigm.
Green, C. S., & Bavelier, D. (2003, May 29). Action video game modifies visual selective
attention.
Greenfield, P. M., deWinstanley, P., Kilpatrick, H., & Kaye, D. (1996). Action video games and
informal education: Effects on strategies for dividing visual attention.
Hannifin, R. D., & Sullivan, H. J. (1996). Preferences and learner control over amount of
instruction. Journal of Educational Psychology, 88, 162-173.
Harter, S. (1978). Effectance motivation reconsidered: Toward a developmental model. Human
Development, 1, 34-64.
Henderson, L., Klemes, J., & Eshet, Y. (2000). Just playing a game? Educational simulation
software and cognitive outcomes.
Hubbard, P. (1991, June). Evaluating computer games for language learning.
Wainess PhD Qualifying Exam
21
Kagan, J. (1972). Motives and development. Journal of Personality and Social Psychology, 22,
51-66.
Khoo, G.-s., & Koh, t.-s. (1998). Using visualization and simulation tools in tertiary science
education [Electronic Version].
Lee, J. (1999). Effectiveness of computer-based instructional simulation: A meta analysis.
Leemkuil, H., de Jong, T., de Hoog, R., & Christoph, N. (2003). KM Quest: A collaborative
Internet-based simulation game.
Locke, E. a., & Latham, G. P. (1990). A theory of goal setting and task performance. Englewood
Cliffs, NJ: Prentice Hall.
Malone, T. W. (1981). What makes computer games fun? Byte, 6(12), 258-277.
Malone, T. W., & Lepper, M. r. (1987). Making leraning fun: A taxonomy of intrinsic
motivation for learning. In R. E. Snow & M. J. Farr (Eds.). Aptitute, learning, and
instruction: Vol. 3. Conative and affective process analyses (pp. 223-253). Hillsdale,
NJ: Lawrence Erlbaum.
Malouf, D. (1987-1988). The effect of instructional computer games on continuing student
motivation.
Mayer, R. E., Mautone, P., & Prothero, W. (2002). Pictorial aids for learning by doing in a
multimedia geology simulation game. Journal of Educational Psychology, 94(1), 171185.
McGrenere, J. (1996). Design: Educational electronic multi-player games—A literature review
(Technical Report No. 96-12, the University of British Columbia). Retrieved from
http://taz.cs.ubc.ca/egems/papers/desmugs.pdf
Wainess PhD Qualifying Exam
22
Niemiec, R. P., Sikorski, C., & Walberg, H. J. (1996). Learner-control effects: A review of
reviews and a meta-analysis. Journal of Educational Computing Research, 15(2), 157174.
Porter, D. B., Bird, M. E., & Wunder, A. (1990-1991). Competition, cooperation, satisfaction,
and the performance of complex tasks among Air Force cadets. Current Psychology:
Research & Reviews, 9(4), 347-354.
Randel, J. M., Morris, B. A., Wetzel, C. D., & Whitehill, B. V. (1992). The effectiveness of
games for educational purposes: A review of recent research. Simulation & Games, 23,
261-276.
Resnick, H., & Sherer, M. (1994). Computerized games in the human services--An introduction.
Ricci, K. E. (1994, Summer). The use of computer-based videogames in knowledge acquisition
and retention.
Ricci, K. E., Salas, E., & Cannon-Bowers, J. A. (1996). Do computer-based games facilitate
knowledge acquisition and retention?
Rieber, L. P. (1996). Seriously considering play: Designing interactive learning environments
based on the blending of microworlds, simulations, and games.
Rieber, L. P., & Matzko, M. J. (Jan/Feb 2001). Serious design for serious play in physics.
Rieber, L. P., Smith, L., & Noah, D. (1998, November/December). The value of serious play.
Rosenorn, T., & Kofoed, L. B. (1998). Reflection in learning processes through
simulation/gaming. Simulation & Gaming, 29(4), 432-440.
Salas, E., Bowers, C. A., & Rhodenizer, L. (1998). It is not how much you have but how you use
it: Toward a rational use of simulation to support aviation training. The International
Journal of Aviation Psychology, 8(3), 197-208.
Wainess PhD Qualifying Exam
23
Santos, J. (2002, Winter). Developing and implementing an Internet-based financial system
simulation game.
Spiker, V. A., & Nullmeyer, R. T. (n.d.). Benefits and limitations of simulation-based mission
planning and rehearsal. Unpublished manuscript.
Stewart, K. M. (1997, Spring). Beyond entertainment: Using interactive games in web-based
instruction.
Story, N., & Sullivan, H. J. (1986, November/December). Factors that influence continuing
motivation. Journal of Educational Research, 80(2), 86-92.
Thiagarajan, S. (1998, Sept/October). The myths and realities of simulations in performance
technology. Educational Technology, 38(4), 35-41.
Washbush, J., & Gosen, J. (2001, September). An exploration of game-derived learning in total
enterprise simulations.
Westbrook, J. I., & Braithwaite, J. (2001). The Health Care Game: An evaluation of a heuristic,
web-based simulation. Journal of Interactive Learning Research, 12(1), 89-104.
White, R. W. (1959). Motivation reconsidered: The concept of competence. Psychological
Review, 66, 297-333.
Wolfe, J. (1997, December). The effectiveness of business games in strategic management
course work [Electronic Version].
Wolfe, J., & Roge, J. N. (1997, December). Computerized general management games as
strategic management learning environments [Electronic Version].
Yair, Y., Mintz, R., & Litvak, S. (2001). 3D-virtual reality in science education: An implication
for astronomy teaching.
Wainess PhD Qualifying Exam
24
Yu, F.-Y. (2001). Competition within computer-assisted cooperative learning environments:
Cognitive, affective, and social outcomes. Journal of Educational Computing Research,
24(2), 99-117.
Wainess PhD Qualifying Exam
2.
25
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 longterm 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
Wainess PhD Qualifying Exam
26
(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
Wainess PhD Qualifying Exam
27
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
Wainess PhD Qualifying Exam
28
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 subelements of information as a single element, categorized according to the manner in which it will
be used (Kalyuga, Chandler, & Sweller, 1998). Schemas are generally thought of as ways of
viewing the world and in a more specific sense, ways of incorporating instruction into our
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,
Wainess PhD Qualifying Exam
29
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
Wainess PhD Qualifying Exam
30
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
Wainess PhD Qualifying Exam
31
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
Wainess PhD Qualifying Exam
32
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
Wainess PhD Qualifying Exam
33
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. Selfefficacy 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
Wainess PhD Qualifying Exam
34
knowledge to form new knowledge (Tennyson & Breuer, 2002). A first condition of problem
solving involves the differentiation process of selecting knowledge that is currently in storage
using known criteria. Concurrently, this selected knowledge is integrated to form a new
knowledge. Cognitive complexity within this condition focuses on elaborating the existing
knowledge base (Tennyson & Breuer, 2002). Problem solving may also involve situations
requiring the construction of knowledge by employing the entire cognitive system. Therefore, the
sophistication of a proposed solution is a factor of the person’s knowledge base, level of
cognitive complexity, higher-order thinking strategies, and intelligence (Tennyson & Breuer,
2002). 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, in press). Self-regulation is composed of
metacognition (planning and self-checking) and motivation (effort and self-efficacy). Thus, in
the specifications for the construct of problem solving, to be a successful problem solver, one
must know something (content knowledge), possess intellectual tricks (problem-solving
strategies), be able to plan and monitor one’s progress towards solving the problem
Wainess PhD Qualifying Exam
35
(metacognition), and be motivated to perform (effort and self-efficacy; O’Neil, 1999, pp. 255256).
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 domainindependent 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
Wainess PhD Qualifying Exam
36
(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
Wainess PhD Qualifying Exam
37
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).
Wainess PhD Qualifying Exam
38
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
Wainess PhD Qualifying Exam
39
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).
Wainess PhD Qualifying Exam
40
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
Wainess PhD Qualifying Exam
41
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 &
Wainess PhD Qualifying Exam
42
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,
Wainess PhD Qualifying Exam
43
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
Wainess PhD Qualifying Exam
44
working memory 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.
Wainess PhD Qualifying Exam
45
References for Question 2
Allen, R. B. (1997). Mental models and user models. In M. Helander, T. K. Landauer & P.
Prabhu (eds.), Handbook of Human Computer Interaction: Second, Completely Revised
Edition (pp. 49-63). Amsterdam: Elsevier
Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from examples:
Instructional principles from the worked examples research. Review of Educational
Research, 70(2), 181-214.
Atkinson, R. K., Renkl, A., Merrill, M. M. (2003). Transitioning from studying examples to
solving problems: Effects of self-explanation prompts and fading worked-out steps.
Journal of Educational Psychology, 95(4), 774-783.
Ausubel, D. P. (1963). The psychology of meaningful verbal learning. New York: Grune and
Stratton.
Baddeley, A. D. (1986). Working memory. Oxford, England: Oxford University Press.
Baddeley, A. D., & Logie, R. H. (1999). Working memory: The multiple-component model. In
A. Miyake & P. Shah (Eds). Models of working memory: Mechanisms of active
maintenance and executive control (pp. 28-61). Cambridge, England: Cambridge
University Press.
Baker, E. L., & Mayer, R. E. (1999). Computer-based assessment of problem solving. Computers
in Human Behavior, 15, 269-282.
Baker, E. L. & O’Neil, H. F., Jr. (2002). Measuring problem solving in computer environments:
current and future states. Computers in Human Behavior, 18, 609-622.
Wainess PhD Qualifying Exam
46
Banbury, S. P., Macken, W. J., Tremblay, S., & Jones, D. M. (2001, Spring). Auditory
distraction and short-term memory: Phenomena and practical implications. Human
Factors, 43(1), 12-29.
Barab, S. A., Bowdish, B. E., & Lawless, K. A. (1997). Hypermedia navigation: Profiles of
hypermedia users. Educational Technology Research & Development, 45(3), 23-41.
Barab, S. A., Young, M. F., & Wang, J. (1999). The effects of navigational and generative
activities in hypertext learning on problem solving and comprehension. International
Journal of Instructional Media, 26(3), 283-309.
Bong, M. (2001). Between- and within-domain relationships of academic motivation among
middle and high school students: Self-efficacy, task-value, and achievement goals.
Journal of Educational Psychology, 93(1), 23-34.
Brunken, R., Plass, J. L., & Leutner, D. (2003). Direct measurement of cognitive load in
multimedia learning. Educational Psychologist 38(1), 53-61.
Chalmers, P. A. (2003). The role of cognitive theory in human-computer interface. Computers in
Human Behavior, 19, 593-607.
Clark, R. E. (1999). The CANE model of motivation to learn and to work: A two-stage process
of goal commitment and effort [Electronic Version]. In J. Lowyck (Ed.), Trends in
Corporate Training. Leuven, Belgium: University of Leuven Press.
Clark, R. E. (2003, March). Fostering the work motivation of teams and individuals.
Performance Improvement, 42(3), 21-29.
Coffin, R. J., & MacIntyre, P. D. (1999). Motivational influences on computer-related affective
states. Computers in Human Behavior, 15, 549-569.
Wainess PhD Qualifying Exam
47
Corno, L., & Mandinach, E. B. (1983). The role of cognitive engagement in classroom learning
and motivation. Educational Psychologist, 18(2), 88-108.
Cutmore, T. R. H., Hine, T. J., Maberly, K. J., Langford, N. M., & Hawgood, G. (2000).
Cognitive and gender factors influencing navigation in a virtual environment.
International Journal of Human-Computer Studies, 53, 223-249.
Daniels, H. L., & Moore, D. M. (2000). Interaction of cognitive style and learner control in a
hypermedia environment. International Journal of Instructional Media, 27(4), 369-383.
Day, E. A., Arthur, W., Jr., & Gettman, D. (2001). Knowledge structures and the acquisition of a
complex skill. Journal of Applied Psychology, 86(5), 1022-1033.
de Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computer
simulations of conceptual domains. Review of Educational Research, 68, 179-202.
Dillon, A., & Gabbard, R. (1998, Fall). Hypermedia as an educational technology: A review of
the quantitative research literature on learner comprehension, control, and style. Review
of Educational Research, 63(3), 322-349.
Eccles, J. S., & Wigfeld, A. (2002). Motivational beliefs, values, and goals. Annual Review of
Psychology, 53, 109-132.
Gevins, A., Smith, M. E., Leong, H., McEvoy, L., Whitfield, S., Du, R., & Rush, G. (1998).
Monitoring working memory load during computer-based tasks with EEG pattern
recognition methods. Human Factors, 40(1), 79-91.
Gredler, M.E. (1996). Educational games and simulations: a technology in search of a research
paradigm.
Green, C. S., & Bavelier, D. (2003, May 29). Action video game modifies visual selective
attention.
Wainess PhD Qualifying Exam
48
Greenfield, P. M., deWinstanley, P., Kilpatrick, H., & Kaye, D. (1996). Action video games and
informal education: Effects on strategies for dividing visual attention.
Harp, S. F., & Mayer, R. E. (1998). How seductive details do their damage: A theory of
cognitive interest in science learning. Journal of Educational Psychology, 90(3), 414434.
Howland, J., Laffey, J., & Espinosa, L. M. (1997). A computing experience to motivate children
to complex performances [Electronic Version]. Journal of Computing in Childhood
Education, 8(4), 291-311.
Jones, M. G., Farquhar, J. D., & Surry, D. W. (1995, July/August). Using metacognitive theories
to design user interfaces for computer-based learning. Educational Technology, 35(4),
12-22.
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect.
Educational Psychologist, 38(1), 23-31.
Kalyuga, S., Chandler, P., & Sweller, J. (1998). Levels of expertise and instructional design.
Human Factors, 40(1), 1-17.
Lee, J. (1999). Effectiveness of computer-based instructional simulation: A meta analysis.
Locke, E. A., Latham, G. P. (2002, Summer). Building a practically useful theory of goal setting
and task motivation: A 35-year odyssey. American Psychologist, 57(9), 705-717.
Mayer, R. E. (1981). A psychology of how novices learn computer programming. Computing
Surveys, 13, 121-141.
Mayer, R. E. (1998). Cognitive, metacognitive, and motivational aspects of problem solving.
Instructional Science, 26, 49-63.
Wainess PhD Qualifying Exam
49
Mayer, R. E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When
presenting more material results in less understanding. Journal of Educational
Psychology, 93(1), 187-198.
Mayer, R. E., & Moreno, R. (1998). A split-attention effect in multimedia learning: Evidence of
dual processing systems in working memory. Journal of Educational Psychology, 90(2),
312-320.
Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning.
Educational Psychologist, 38(1), 43-52.
Mayer, R. E., Moreno, R., Boire, M., & Vagge, S. (1999). Maximizing constructivist learning
from multimedia communications by minimizing cognitive load. Journal of Educational
Psychology, 91(4), 638-643.
Mayer, R. E., & Wittrock, M. C. (1996). Problem-solving transfer. In D. C. Berliner & R. C.
Calfee (Eds.), Handbook of educational psychology (pp. 47-62). New York: Simon &
Schuster Macmillan.
Moreno, R., & Mayer, R. E. (1999). Cognitive principles of multimedia learning: The role of
modality and contiguity. Journal of Educational Psychology, 91(2), 358-368.
Moreno, R., & Mayer, R. E. (2000a). A coherence effect in multimedia learning: The case of
minimizing irrelevant sounds in the design of multimedia instructional messages. Journal
of Educational Psychology, 92(1), 117-125.
Moreno, R., & Mayer, R. E. (2000b). Engaging students in active learning: The case for
personalized multimedia messages. Journal of Educational Psychology, 92(4), 724-733.
Moreno, R., & Mayer, R. E. (2002). Learning science in virtual reality multimedia environments:
Role of methods and media. Journal of Educational Psychology, 94(3), 598-610.
Wainess PhD Qualifying Exam
50
Mousavi, S. Y., Low, R., & Sweller, J. (1995). Reducing cognitive load by mixing auditory and
visual presentation modes. Journal of Educational Psychology, 87(2), 319-334.
Niemiec, R. P., Sikorski, C., & Walberg, H. J. (1996). Learner-control effects: A review of
reviews and a meta-analysis. Journal of Educational Computing Research, 15(2), 157174.
Noyes, J. M., & Garland, K. J. (2003). Solving the Tower of Hanoi: Does mode of presentation
matter? Computers in Human Behavior, 19, 579-592.
O’Neil, H. F., Jr. (1999). Perspectives on computer-based performance assessment of problem
solving: Editor’s introduction. Computers in Human Behavior, 15, 255-268.
O'Neil, H. F., Jr. (Ed.). (in press). Computer-based assessment of problem solving [Special
Issue]. Computers in Human Behavior.
Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent
developments. Educational Psychologist, 38(1), 1-4.
Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. M. (2003). Cognitive load
measurement as a means to advance cognitive load theory. Educational Psychologist,
38(1), 63-71.
Park, O.-C., & Gittelman, S. S. (1995). Dynamic characteristics of mental models and dynamic
visual displays. Instructional Science, 23, 303-320.
Renkl, A., & Atkinson, R. K. (2003). Structuring the transition from example study to problem
solving in cognitive skill acquisition: A cognitive load perspective. Educational
Psychologist, 38(1), 13-22.
Ricci, K. E., Salas, E., & Cannon-Bowers, J. A. (1996). Do computer-based games facilitate
knowledge acquisition and retention?
Wainess PhD Qualifying Exam
51
Salomon, G. (1983). The differential investment of mental effort in learning from different
sources. Educational Psychology, 18(1), 42-50.
Schraw, G. (1998). Processing and recall differences among seductive details. Journal of
Educational Psychology, 90(1), 3-12.
Sweller, J., & Chandler, P. (1994). Why some material is difficult to learn. Cognition and
Instruction, 12, 185-233.
Tennyson, r. D., & Breuer, K. (2002). Improving problem solving and creativity through use of
complex-dynamic simulations.
Thompson, L. F., Meriac, J. P., & Cope, J. G. (2002, Summer). Motivating online performance:
The influence of goal setting and Internet self-efficacy. Social Science Computer Review,
20(2), 149-160.
van Merrienboer, J. J. G., Kirschner, P. A., & Kester, L. (2003). Taking a load off a learner’s
mind: Instructional design for complex learning. Educational Psychologist, 38(1), 5-13.
Yung, A. S. (1999). Cognitive load and learner expertise: Split attention and redundancy effects
in reading comprehension tasks with vocabulary definitions. Journal of Educational
Media, 24(2), 87-102.
Yeung, A. S., Jin, P., & Sweller, J. (1997). Cognitive load and learner expertise: Split-attention
and redundancy effects in reading with explanatory notes. Contemporary Educational
Psychology, 23, 1-21.
Wainess PhD Qualifying Exam
3.
52
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
Wainess PhD Qualifying Exam
53
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,
Wainess PhD Qualifying Exam
54
Derry, Renkl, & Wortham, 2000) or, alternatively, examples are interspersed with the to-besolved 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
Wainess PhD Qualifying Exam
55
reasoning (van Merrienboer, Clark, et al., 2002). A schema is defined as a cognitive construct
that permits problem-solvers to recognize a problem as belonging to a specific category requiring
particular moves for solutions (Tarmizi & Sweller, 1988). If a learner has acquired appropriate
automated schemas, cognitive load will be low, and substantial working memory resources are
likely to be free. Schemas enable another use of the same knowledge in a novel situation,
because they contain generalized knowledge, or concrete cases, or both, which can serve as
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
Wainess PhD Qualifying Exam
56
means-ends search and directs a learner’s attention toward a problem state and its associated
moves (Kalyuga, Ayers, Chandler, & Sweller, 2003).
Learning tasks that take the form of worked examples confront learners not only with a
given state and a desired goal state but also with an example solution. Studying those examples
as a substitute for performing conventional problem solving tasks focuses attention on problem
states and associated solution states and enables learners to 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
Wainess PhD Qualifying Exam
57
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
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
Wainess PhD Qualifying Exam
58
produced better learning for students totally unfamiliar with the new domain, but exploration
practice was just as good as this combined approach for students with some domain experience.
Potential problems associated with worked examples. 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).
Wainess PhD Qualifying Exam
59
According to van Merrienboer, Clark, and de Croock (2002), learners are very good at
inducing plausible patterns given adequate examples. However, when working from examples
alone, learners will initially look for niave direct correspondence between their current problem
and the examples, rather than trying to extrapolate the underlying meaning (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
Wainess PhD Qualifying Exam
60
challenging than they can handle on their own. Instead of solving the problems or tasks
independently, the learner must rely—at least initially—on the assistance of their more capable
peers and/or instructors to succeed. Learners will eventually make a smooth transition from
relying on modeling (worked examples) to scaffolded problem solving (faded or partial
examples) to independent problem solving (Atkinson 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
Wainess PhD Qualifying Exam
61
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, 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,
Wainess PhD Qualifying Exam
62
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 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
Wainess PhD Qualifying Exam
63
& Todd, 1993). DMIs allow users to carry out operations as if they were working on the actual
objects in the real world. The gap between the user’s intentions and the actions necessary to
carry them out is small. These two characteristics of direct manipulation are referred to as
engagement and distance (Wiedenbeck & Davis, 1997). Engagement is defined as a feeling of
working directly with the objects of interest in the world rather than with surrogates (Frohlic,
1997; Wiedenbeck & Davis, 2001). Distance is a cognitive gap between the user’s intentions and
the actions needed to carry them out (Frohlich, 1997; Wiedenbeck & Davis, 2001). With direct
manipulation the distance is reduced by presenting the user with a predefined list of visible
options that allow the user to click and drag familiar objects in a well-understood context. 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,
Wainess PhD Qualifying Exam
64
1993). Menu interfaces lead to better task performance than the command interfaces, which is
attributed to a smaller gap between user intentions and actions with menu interfaces.
(Wiedenbeck & Davis, 2001). The results of studies comparing DMI to menu or DMI to
command line have been less consistent.
Widenbeck and Davis (1997) suggested that direct manipulation interfaces lead to more
positive perceptions of ease of use than does a command interface. With elementary school
students, Brown and Schneider (1992) found DMI more comfortable and enhanced the speed of
problem solving. DMI was also less frustrating compared to the conversational interface. 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
Wainess PhD Qualifying Exam
65
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
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 “modelworld 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
Wainess PhD Qualifying Exam
66
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).
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
Wainess PhD Qualifying Exam
67
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
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
Wainess PhD Qualifying Exam
68
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
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.
Wainess PhD Qualifying Exam
69
References for Question 3
Alessi, S. M. (2000). Simulation design for training and assessment. In H. F. O’Neil, Jr. & D. H.
Andrews (Eds.), Aircrew training and assessment (pp. 197-222). Mahwah, NJ: Lawrence
Erlbaum Associates.
Allen, R. B. (1997). Mental models and user models. In M. Helander, T. K. Landauer & P.
Prabhu (eds.), Handbook of Human Computer Interaction: Second, Completely Revised
Edition (pp. 49-63). Amsterdam: Elsevier
Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from examples:
Instructional principles from the worked examples research. Review of Educational
Research, 70(2), 181-214.
Atkinson, R. K., Renkl, A., Merrill, M. M. (2003). Transitioning from studying examples to
solving problems: Effects of self-explanation prompts and fading worked-out steps.
Journal of Educational Psychology, 95(4), 774-783.
Barab, S. A., Young, M. F., & Wang, J. (1999). The effects of navigational and generative
activities in hypertext learning on problem solving and comprehension. International
Journal of Instructional Media, 26(3), 283-309.
Baylor, A. L. (2001). Perceived disorientation and incidental learning in a web-based
environment: Internal and external factors. Journal of Educational Multimedia and
Hypermedia, 10(3), 227-251.
Benbasat, I., & Todd, P. (1993). An experimental investigation of interface design alternatives:
Icon vs. text and direct manipulation vs. menus. International Journal of Man-Machine
Studies, 38, 369-402.
Wainess PhD Qualifying Exam
70
Brown, D. W., & Schneider, S. D. (1992), Young learners’ reactions to problem solving
contrasted by distinctly divergent computer interfaces. Journal of Computing in
Childhood Education, 3(3/4), 335-347.
Carroll, W. M. (1994). Using worked examples as an instructional support in the algebra
classroom. Journal of Educational Psychology, 86(3), 360-367.
Chalmers, P. A. (2003). The role of cognitive theory in human-computer interface. Computers in
Human Behavior, 19, 593-607.
Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations:
How students study and use examples in learning to solve problems. Cognitive
Science, 13, 145-182.
Chou, C., & Lin, H. (1998). The effect of navigation map types and cognitive styles on learners’
performance in a computer-networked hypertext learning system [Electronic Version].
Journal of Educational Multimedia and Hypermedia, 7(2/3), 151-176.
Chou, C., Lin, H, & Sun, C.-t. (2000). Navigation maps in hierarchical-structured hypertext
courseware [Electronic Version]. International Journal of Instructional Media, 27(2),
165-182.
Clark, R. E. (Ed.).(2001). Learning from Media: Arguments, analysis, and evidence. Greenwich,
CT: Information Age Publishing.
Cutmore, T. R. H., Hine, T. J., Maberly, K. J., Langford, N. M., & Hawgood, G. (2000).
Cognitive and gender factors influencing navigation in a virtual environment.
International Journal of Human-Computer Studies, 53, 223-249.
Wainess PhD Qualifying Exam
71
Davis, S., & Wiedenbeck, S. (2001). The mediating effects of intrinsic motivation, ease of use
and usefulness perceptions on performance in first-time and subsequent computer users.
Interacting with Computers, 13, 549-580.
de Jong, T., de Hoog, R., & de Vries, F. (1993). Coping with complex environments: The effects
of providing overviews and a transparent interface on learning with a computer
simulation. International Journal of Man-Machine Studies, 39, 621-639.
Dempsey, J. V., Haynes, L. L., Lucassen, B. A., & Casey, M. S. (2002). Forty simple computer
games and what they could mean to educators.
Dias, P., Gomes, M. J., & Correia, A. P. (1999). Disorientation in hypermedia environments:
Mechanisms to support navigation. Journal of Educational Computing Research, 20(2),
93-117.
Eberts, R. E., & Bittianda, K. P. (1993). Preferred mental models for direct-manipulation and
command-based interfaces. International Journal of Man-Machine Studies, 38, 769-785.
Frohlich, D. M. (1997). Direct manipulation and other lessons. In M. Helander, T. K. Landauer
& P. Prabhu (eds.), Handbook of Human Computer Interaction: Second, Completely
Revised Edition (pp. 463-488). Amsterdam: Elsevier
Hannifin, R. D., & Sullivan, H. J. (1996). Preferences and learner control over amount of
instruction. Journal of Educational Psychology, 88, 162-173.
Jones, M. G., Farquhar, J. D., & Surry, D. W. (1995, July/August). Using metacognitive theories
to design user interfaces for computer-based learning. Educational Technology, 35(4),
12-22.
Wainess PhD Qualifying Exam
72
Kaber, D. B., Riley, J. M., & Tan, K.-W. (2002). Improved usability of aviation automation
through direct manipulation and graphical user interface design. The International
Journal of Aviation Psychology, 12(2), 153-178.
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect.
Educational Psychologist, 38(1), 23-31.
Kee, D. W., & Davies, L. (1988). Mental effort and elaboration: A developmental analysis.
Contemporary Educational Psychology, 13, 221-228.
Leemkuil, H., de Jong, T., de Hoog, R., & Christoph, N. (2003). KM Quest: A collaborative
Internet-based simulation game.
Mayer, R. E., & Chandler, P. (2001). When learning is just a click away: Does simple user
interaction foster deeper understanding of multimedia messages? Journal of Educational
Psychology, 93(2), 390-397.
Mayer, R. E., Mautone, P., & Prothero, W. (2002). Pictorial aids for learning by doing in a
multimedia geology simulation game. Journal of Educational Psychology, 94(1), 171185.
Mwangi, W., & Sweller, J. (1998). Learning to solve compare word problems: The effect of
example format and generating self-explanations. Cognition and Instruction, 16(2), 173199.
Niemiec, R. P., Sikorski, C., & Walberg, H. J. (1996). Learner-control effects: A review of
reviews and a meta-analysis. Journal of Educational Computing Research, 15(2), 157174.
Quilici, J. L., & Mayer, R. E. (1996). Role of examples in how students learn to categorize
statistics word problems. Journal of Educational Psychology, 88(1), 144-161.
Wainess PhD Qualifying Exam
73
Renkl, A., & Atkinson, R. K. (2003). Structuring the transition from example study to problem
solving in cognitive skill acquisition: A cognitive load perspective. Educational
Psychologist, 38(1), 13-22.
Renkl, A., Atkinson, R. K., Maier, U. H., & Staley, R. (2002). From example study to problem
solving: Smooth transitions help learning. The Journal of Experimental Education, 70(4),
293-315.
Ruddle, R. A., Howes, A., Payne, S. J., & Jones, D. M. (2000). The effects of hyperlinks on
navigation in virtual environments. International Journal of Human-Computer Studies,
53, 551-581.
Shyu, H.-y., & Brown, S. W. (1995). Learner-control: The effects of learning a procedural task
during computer-based videodisc instruction. International Journal of Instructional
Media, 22(3), 217-230.
Tarmizi, R. A., & Sweller, J. (1988). Guidance during mathematical problem solving. Journal of
Educational Psychology, 80(4), 424-436.
Tuovinen, J. E., & Sweller, J. (1999). A comparison of cognitive load associated with discovery
learning and worked examples. Journal of Educational Psychology, 91(2), 334-341.
van Merrienboer, J. J. G., Clark, R. E., & de Croock, M. B. M. (2002). Blueprints for complex
learning: The 4C/ID-model. Educational Technology Research & Development, 50(2),
39-64.
van Merrienboer, J. J. G., Kirschner, P. A., & Kester, L. (2003). Taking a load off a learner’s
mind: Instructional design for complex learning. Educational Psychologist, 38(1), 5-13.
Westerman, S. J. (1997). Individual differences in the use of command line and menu computer
interfaces. International Journal of Human-Computer Interaction, 9(2), 183-198.
Wainess PhD Qualifying Exam
74
Wiedenbeck, S., & Davis, S. (1997). The influence of interaction style and experience on user
perceptions of software packages. International Journal of Human-Computer Studies, 46,
563-588.
Yair, Y., Mintz, R., & Litvak, S. (2001). 3D-virtual reality in science education: An implication
for astronomy teaching.