Cognitive Efficiency of Animated Pedagogical Agent for Learning Second Language

A Proposal Presented to theFaculty of the Graduate School

University of Southern California

Sunhee ChoiUniversity of Southern California

to

Dr. Richard E. Clark (Chair)Dr. Edward KazlauskasDr. Harold F. O’Neil, Jr.

Dr. Robert RuedaDr. Nam-kil Kim (Outside Member)

5424 Newcastel Ave. #229Encino, CA 91316

(213)407-3378

sunheech@usc.edu

1

Table of Contents

ABSTRACT....................................................................................................................................3

CHAPTER I: REVIEW OF LITERATURE...............................................................................5Introduction....................................................................................................................................5Learning Second Language Grammar........................................................................................8

Attention and Awareness in Second Language Learning........................................................8Instructional Methods to Focus Learner Attention................................................................11Studies on the Effects of Implicit and Explicit Methods.......................................................12Summary................................................................................................................................17

Cognitive Efficiency of Multimedia Learning...........................................................................20Theoretical Constructs Relevant to Cognitive Efficiency.....................................................21Mental Effort Measurement...................................................................................................22

Self-Report Measures....................................................................................................22Dual-Task Measures......................................................................................................23

Building Cognitive Efficiency in Multimedia Learning........................................................25Instructional Strategies to Reduce Cognitive Load...............................................................26

Avoiding split attention effects and Utilizing modality effects.....................................28Leaving out redundancy effects.....................................................................................29Integrating Leaner Prior Knowledge and Expertise......................................................31

Summary................................................................................................................................32Animated Pedagogical Agents....................................................................................................34

Benefits of Animated Pedagogical Agents............................................................................35Motivating Learners.......................................................................................................36Focusing Learner Attention...........................................................................................41

Summary................................................................................................................................44Research Hypotheses...................................................................................................................46

CHAPTER II: Methodology.......................................................................................................53Target English Grammar for Instruction..................................................................................53Participants..................................................................................................................................53Research Design...........................................................................................................................53Measures.......................................................................................................................................54Data Analysis................................................................................................................................55

References......................................................................................................................................59

2

ABSTRACT

An animated pedagogical agent is a lifelike animated character that inhabits a computer-

based learning environment, and provides learners with pedagogical assistance such as directing

attention and giving contextualized advice. Recent studies that compare animated pedagogical

agents with alternative media have resulted in ambiguous findings. Clark has claimed that

animated pedagogical agents do not provide instructional benefits unless they deliver essential

instructional methods and that any method can be implemented in a variety of media with equal

learning impact (1983, 1994a, 1994b, 2001, In Press). He further argues that while animated

pedagogical agents are able to present essential instructional methods, other less expensive and

less distracting implementations of instructional methods are equally effective for learning.

The present study explores the use of APA’s as well as other delivery systems in a

multimedia environment in which college level students learn English as a Second Language

(ESL). Experiments will be conducted to assess the effects of two different instructional methods

(metalinguistic rule presentation and visual enhancement) on the acquisition of English relative

clauses which is measured through the comprehension and production tests.

The study also examines the cognitive efficiency of different delivery media including

animated pedagogical agents and a simple flashing arrow used to deliver one of the instructional

methods - visual enhancement. Cognitive efficiency refers to “one medium being more or less

effortful than another, more or less likely to succeed with a particular learner, or interacting more

or less usefully with a particular prior knowledge set” (Cobb, 1997, p. 25), leading to faster

learning, or requiring less conscious effort from learners for processing learning material. The

underlying premise of cognitive efficiency is that a specific medium used to present instruction

may not produce different cognitive outcomes compared to another medium, but it still can have

3

direct impact on cognitive processes.

The main argument of this study is that what causes learning is an instructional method,

not a delivering medium such as animated pedagogical agents or flashing arrows. Therefore, the

study hypothesizes that there will be no significant difference in learner performance on the

target L2 grammar among participants who receive the same instructional methods that are

delivered through different media. In addition, the present study argues that different media

employed to deliver the same instructional method will have significant, different effect on levels

of cognitive efficiency.

The design of the study is a true experimental with pre- and post test involving five

treatment groups and one control group. The treatment groups will be different with regard to

provision of explicit explanations on the target form and the provision of visual enhancement

technique to focus learners’ attention on the target form. The participants will be randomly

assigned in equal proportion to one of the six conditions. There are total five dependent variables

in the present study which include mental effort measures, time measures, interest measures,

attention measures, and performance measures. Descriptive statistics will be used throughout the

study to estimate all measures. Additionally, Multiple Regression and MANOVA will be used to

investigate the relationship among mediating (i.e., learner prior knowledge), process (i.e., time

required to acquire the form, the amount of attention paid to the form), and outcome measures

(i.e., scores for comprehension and production tests).

4

CHAPTER I: REVIEW OF LITERATURE

Introduction

The primary purpose of the present study is to examine the claims that Animated

Pedagogical Agents (APA’s), when used in media-based instructional programs, increase learning

scores over instructional treatments that do not employ APA’s (Atkinson, 2002; Johnson, Ricke,

& Lester, 2000; Lester, Converse, Stone, Kahler, & Barlow, 1997). An APA is a lifelike animated

character that inhabits a computer-based learning environment, and provides learners with

pedagogical assistance such as directing attention and giving advice about learning strategies.

Recent studies that compare APA’s with alternative treatments (Atkinson, 2002; Craig, Gholson,

& Driscoll, 2002; Moreno, Mayer, Spires, & Lester, 2001) have resulted in ambiguous findings.

Clark has claimed that APA’s (and other media and media attribute treatments) are not

instrumental in learning unless they provide essential instructional methods and that any method

can be implemented in a variety of ways with equal learning impact (1983, 1994a, 1994b, 2001,

In Press). He further argues that while APA’s are able to present essential instructional methods

such as instructional plans, examples, and feedback, other less expensive and less distracting

implementations of instructional methods are equally effective for learning (i.e., using simple

arrows or color coding of key points rather than having an APA “point” to text or parts of

instructional graphics). Therefore, Clark concludes, it is the instructional method used, not the

specific medium or audio-visual agent used to deliver the method that leads to learning gains.

Nevertheless, this argument is not settled. On the one hand, with the wide availability of

multimedia and information technology, an ever increasing number of educational software

programs are promoting the inclusion of multimedia elements in instruction as a panacea to

5

learning problems (Kimmel & Deek, 1996; Lowe, 2002). And animated pedagogical agents are

simply the latest iteration of recent technological advances in user interface and autonomous

software agents that are being developed to aid instruction. On the other hand, a number of

researchers and educational economists are concerned that unnecessarily expensive instructional

tools are being proposed to solve critical learning and educational access problems when less

expensive options would have either equal or greater impact on learning (Erickson, 1997; Levin

& McEwan, 2001).

The present study will explore the use of APA’s as well as other delivery systems in a

multimedia learning environment in which college level students learn English as a Second

Language (ESL). Experiments will be conducted to assess the effects of two different

instructional methods (metalinguistic rule presentation and visual enhancement) on the

acquisition of English relative clauses which is measured through comprehension and production

tests. In order to address the issue, the study will adopt a 2 (metalinguistic rule explanation: rule

presentation, no rule presentation) by 3 (visual enhancement: animated pedagogical agent,

flashing arrow, no visual enhancement) true experimental design. Other dependent variables will

also be measured to estimate the relative effectiveness and efficiency of different methods and

media used in the study, which include the mental effort, learning time, learner interest, and

attention.

In addition, the study will examine the cognitive efficiency of different delivery media

including animated pedagogical agents and a simple flashing arrow used to deliver one of the

instructional methods - visual enhancement. According to Cobb (1997), who proposed to include

‘Cognitive Efficiency’ as a variable in media studies, cognitive efficiency refers to “one medium

being more or less effortful than another, more or less likely to succeed with a particular learner,

6

or interacting more or less usefully with a particular prior knowledge set” (p. 25), leading to

faster learning, or requiring less conscious effort from learner for processing learning materials

(Cobb, 1997; Clark, 1998).

The underlying principle of cognitive efficiency is that a specific medium through which

instruction is presented to learners may not produce different cognitive outcomes compared to

another, such as a superior mental representation, but it still can have direct impact on cognitive

processes with different levels of cognitive efficiency. In other words, different media in which

an instruction is delivered might end up with the same cognitive product, but they can determine

how different learners with different prior knowledge process the information presented to them.

Despite its potential for multimedia- and computer-assisted learning, cognitive efficiency is still

in the early stage of development and needs to have sound theoretical frameworks and empirical

evidence to support its hypothesis.

To lay out the foundation of the present study, the following sections will review

relevant theories and research findings and investigate how to build cognitive efficiency of

multimedia learning using an animated pedagogical agent and an alternative flashing arrow.

First, the review will look at the factors involved in learning second language (L2) grammar,

which will be the learning task as well as the dependent variable of the present study. In

particular, it will focus on instructional methods that have been found to enhance the acquisition

of L2 grammar. Secondly, the review will look at the factors relevant to cognitive efficiency and

examine the ways to improve cognitive efficiency in multimedia learning environment. Finally,

the review will summarize what and how studies have been conducted in the field of animated

pedagogical agent and closely examine APA’s from a cognitive efficiency perspective, and then

outline the challenges facing the field including the need for a sound study design to clarify to

7

what factors learning outcomes can be attributed.

Learning Second Language Grammar

The status of grammar instruction in second and foreign language (L2) education has

changed throughout history as theories about the nature of language and language acquisition

have changed (Richards & Rodgers, 1998). Over the last two decades, in particular, the question

of the role of grammar instruction in L2 acquisition, whether it facilitates L2 acquisition or not,

has been a major topic of debate, and thereby has produced a considerable number of theories

and studies (Norris & Ortega, 2000). Yet, the focus of L2 instruction research has gradually

moved from whether L2 instruction is necessary to what instructional methods are more effective

for L2 grammar acquisition (Norris & Ortega, 2000), since Long (1983) concluded that L2

grammar instruction indeed makes difference in learner performance after comparing the learner

achievement of explicit instruction with that of natural exposure or combination of two. Over the

last two decades, a number of quasi-experimental and experimental studies have been conducted

to examine the effectiveness of various instructional treatments. Despite the variety of the

instructional methods, the basic premise of these methods is the same: an instructional treatment

should attract learners’ focal attention to the target L2 form, another common term for grammar

in the field of Second Language Acquisition (SLA), within a meaningful context so that the form

is more likely to be noticed, processed, and acquired (Schmidt, 1995; Spada, 1997). The role of

attention in second language learning and measurement issues are discussed in the next section

and several instructional methods to draw learner attention in the following section.

Attention and Awareness in Second Language Learning

The role of attention and awareness in learning has been extensively studied both in

8

psychology and second language acquisition (Curran & Keele, 1993; Dulany, 1991; Reber, 1976,

1989, 1993; Schmidt, 1995; Tomlin & Villa, 1994). The general consensus in SLA is that

attention to a certain linguistic feature is required for learning to take place. However, it is still

controversial how much and what type of subjective awareness of an L2 form is necessary for

learning to occur (Izumi, 2002). There are three major positions regarding this question. First,

Schmidt (1990, 1995, 2001), based on his Noticing Hypothesis, claims that “what learners notice

in input becomes intake for learning” (1995, p. 20), Here, noticing is referred to as cognitive

registration of stimuli (e.g., meaning or form of L2 input) in conscious awareness followed by

storage in long-term memory, and intake as the portion of input which has been perceived and

processed by learners. From Schmidt’s point of view, therefore, learning cannot take place

without learners’ subjective awareness at the level of noticing.

On the contrary, Tomlin and Villa (1994) proposed a theoretical model of attention in

which attention is divided into three interrelated processes; alertness – “general readiness to deal

with incoming stimuli or data” (p. 190), orientation – “the specific aligning of attention on a

stimulus” (p. 191), and detection – “the cognitive registration of sensory stimuli” (p. 192).

Drawing from this model, they argue that conscious awareness is not necessary for learning even

though it might facilitate learning, and what is crucial for learning is the detection which does

not require learners’ conscious awareness.

The third position comes from Robinson (1995) who posits himself in the middle of the

previous two positions. He contends that learning might happen when a learner detects the form

without focal attention or subjective awareness, but the amount of learning would be very

limited. Rather, in order to store a stimulus into memory one has to detect and notice the target

form, which then should be rehearsed in short-term memory. Despite this controversy, it is now

9

widely agreed that conscious, focal attention is necessary for learning to take place and a higher

level of subjective awareness or noticing is correlated with better learning (Rosa & O'Neill,

1999). Therefore, the present study will measure learner attention by examine whether learners

have noticed the form. Then, it will be hypothesized that participants’ conscious awareness of the

target L2 form will have significant effect on the level of learner performance measured by

comprehension and production tests on the target grammar.

There is another issue that has been subject to debate in the field - the ways to measure

the amount of learner attention and awareness. Attention measurement is constructed mostly

based on Schmidt’s operational definition of noticing (Schmidt, 1995) - the availability for

verbalizing what one has experienced during or right after exposure to the input. Accordingly,

many measuring methods employ learner self-report or questionnaires that are believed to

capture what learners have experienced during the exposure to a grammatical form. Yet, it should

be noted that some experiences are not easy to report or verbalize, and hence, the lack of self-

report does not necessarily mean the lack of awareness (Schmidt, 1995). Leow (1998) also

pointed out that the indirect post-experiment methods may obtain only indirect evidence of

learners’ attention to the form. Moreover, retrospective self-report measures are restricted to a

certain degree in that that learners might have limited ability to retain in their memory what they

have experienced, and they could report what they have inferred instead of what they have

actually experienced (Rosa & O’Neill, 1999).

Recently, researchers have started to use more direct measuring methods in order to

capture what is really happening when learners attend to forms and process them for further

learning. For instance, the think-aloud protocol, through which learners are required to verbalize

whatever comes to their mind during learning process, is one of the popular direct methods used

10

in the field of SLA. Despite its popularity in the field, the use of the think-aloud protocol and its

validity have also been questioned by some researchers. They criticize that the think aloud

protocol could be posed as a secondary task on learners who have to simultaneously process the

input (Izumi, 2002). In addition, there is a possibility that the protocol could change the very

nature of the process under investigation by prompting learners to process the input in a more

systematic way than they would do without thinking aloud (Rosa & O’Neill, 1999).

Instructional Methods to Focus Learner Attention

The present study hypothesizes that different instructional methods will have different

cognitive effects on learning L2 grammar. Thus, it is necessary to review instructional methods

for drawing learners’ attention to a linguistic target and their effectiveness in enhancing learner

performance in both comprehending and using the target form. Several instructional methods

have been developed and studied including textual or visual enhancement, metalinguistic

explanation of the linguistic form (i.e., syntax or morphology) before or after exposure to input,

and frequent use of the target form in input (e.g., input flood). These methods can be categorized

depending upon the degree of their explicitness in requiring learners to focus on the form. An

explicit instruction is centered on providing metalinguistic rule explanation, and/or tasks that

explicitly require learners to pay attention to specific forms, whereas an implicit instruction

utilizes unobtrusive techniques and/or tasks (e.g., visual manipulation of forms) to lead learners

to notice forms. Implicit methods intend to lead learners to focus not only on the target form, but

also on the meaning of input (DeKeyser, 1995).

Different instructional treatments have a different selection and arrangement of the

methods listed above. Among several methods, however, the metalinguistic rule explanation and

visual enhancement of the target form have been most discussed in the field and their relative

11

effectiveness have been investigated, in particular, when they are matched up with other

moderating factors including learners’ L2 prior knowledge or proficiency level, learner

characteristics (i.e., language aptitude), and the target language forms (e.g., simple vs. complex

linguistic features) (Norris & Ortega, 2000). These two methods are also the main instructional

methods employed in the present study that will be delivered by using different forms of

multimedia. Furthermore, their effectiveness will be studied with relation to learners’ prior

knowledge of the target form to get a more complete understanding of their effects on learning.

The following section, hence, will review a body of literature regarding the effectiveness of the

explicit rule presentation and visual manipulation, keeping in mind that there could be other

factors that could affect learners’ interaction with instructional methods and subsequent results.

Studies on the Effects of Implicit and Explicit Methods

Visual input enhancement (i.e., the targeted linguistic features are italicized, bold, or

capitalized for perceptual salience), an implicit and unobtrusive attention drawing technique, is

hypothesized to help learners to focus on a specific grammatical structure contained in written

text by making the form perceptually salient. The fundamental idea behind visual enhancement is

that learners’ attention will be drawn to the highlighted grammatical structure in input, and then

the attended form will be learned because attention transforms input into intake (Izumi, 2002). It

is also based on the notion that explicitly drawing learners’ attention to linguistic forms interferes

with communication and meaning-making process. In contrast, explicit methods assume that

simply exposing learners to the target form is not sufficient to enable learners to learn most of the

L2 grammar and that this lack should be compensated for by explicitly explaining about the

target form (DeKeyser, 1998). The contents of the explicit instruction prime learners for specific

grammatical features so that they pay longer and deeper attention to the target forms and form

12

conscious, accurate knowledge of the forms.

Recently, a number of laboratory and classroom studies have revealed that learners who

are explicitly exposed to target forms and who are given explicit rule explanations outperformed

those who are implicitly directed to forms (Alanen, 1995; DeKeyser, 1995; Ellis, 1994;

Robinson, 1996, 1997a, b). On the contrary, the research on implicit methods including the

visual enhancement produced mixed results (Jourdenais, Ota, Stauffer, Boyson & Doughty,

1995). The mixed results could be due to a number of other independent and mediating factors

involved in studies, such as the length of treatment, additional instructional methods

implemented besides textual input enhancement, attention measured, and learners’ proficiency

levels.

For instance, Alanen (1995) found positive effects for metalinguistic rule presentation,

but mixed results of visual enhancement on the acquisition of semi-artificial Finnish locative

suffixes and consonant gradation. In the study, participants were asked to read a text with the

target structure embedded in it after assigned to one control and three treatment groups: (a) the

Control group only read two descriptive passages with a picture and glossary; (b) the Enhance

group received the same text with the target structures printed in italics; (c) the Rule group was

given explicit explanation about the use of the target structures before reading the text; and (d)

the Rule & Enhance group received the explicit rule explanation and the typographically

enhanced text. The first two groups were also referred to as meaning-based groups whereas the

latter two groups were referred to as rule-based groups. Participants were required to think aloud

while reading the text to find out what features of the input they paid attention to, and whether

they had noticed the form.

As hypothesized, the results of various assessments (i.e., think aloud protocol,

13

grammaticality judgment, sentence completion, comprehension, word translation, and rule

statement) revealed that the rule-based groups, the Rule and Rule & Enhance groups,

outperformed the meaning-based groups, the control and Enhance groups, in learning both

locative suffixes and consonant gradation. Interestingly, however, the data analysis did not

support the initial hypotheses that visual input enhancement would have positive impact on the

learning of the target structure. The students in the visual enhancement groups noticed the italics

in the text, but it appeared that not many of them thought about the reasons. As a consequence,

no significant difference was found between the Control and the Enhance group as well as

between the Rule and the Rule & Enhance group. Based on these findings, Alanen concluded

that it was the explicit rule explanation that made differences in learning. With regard to the

failure of the visual input enhancement, Alanen attributed it to the low degree of perceptual

saliency of italics.

The study by Jourdenais et al. (1995) is one of the few studies which found that textually

modified input facilitated the noticing of and the subsequent production of grammatical forms.

Participants, 14 college students learning Spanish, were first assigned to either visual

enhancement or comparison groups, and then given a sample text written in Spanish as a

stimulus for a writing task. However, only the enhancement group received a textually enhanced

sample text in which the target forms, Spanish preterit and imperfect, were manipulated (e.g.,

shadowed, underlined, bolded, etc.). After reading the sample text, the participants were told to

narrate a series of pictures in writing. They were also told to think aloud as they wrote. The

think-aloud protocol was employed to investigate a learner’s concurrent cognitive processing of

the forms and to measure the degree and the nature of noticing. In addition, the students’ written

products were analyzed to evaluate the learners’ use of the target forms.

14

The think-aloud protocol data revealed that the students in the visual enhancement group

made significantly more explicit mentions of the forms than those in the comparison group,

which Jourdenais and colleagues interpreted as that the enhancement group had been better

primed for processing of the target forms due to the textual enhancement. The written

productions also showed that the enhancement group used the forms more often than the

comparison group although the difference in accuracy did not significantly differ. The results,

however, should be interpreted with caution because learners were already familiar with the

forms, and had different proficiency levels in Spanish reading and writing skills. As the

researchers mentioned, it was not clear what role these individual differences played in cognitive

processing as well as outcomes.

An L2 instruction may be composed of more than one method to effectively draw

otherwise elusive learner attention to form. Ellis (1994) insists that the combination of implicit

and explicit method works better than the use of only one method, either implicit or explicit,

since learners need not only to learn the form, but also to integrate the form and the meaning to

successfully complete language acquisition process. White (1998) also suggests that the

typographically enhanced input does not provide enough information about the use of forms, and

therefore, in addition to visually enhancing input it might be more helpful to provide explicit rule

explanation or different types of visual enhancement such as arrows or color-coding which could

clarify the relationship among pertinent elements. She also maintains that individual differences

and some external factors, such as regular classroom activities, should be considered when

studying the effect of instructional methods.

Then, there arises a question of what it means that visual manipulation of text was not

perceptually salient enough. Based on the two studies discussed above, it appears that visual

15

enhancement is not qualitatively good enough to trigger further cognitive processing beyond

mere noticing of forms for acquisition to take place. Consequently, it should be sought out then

what other instructional methods could be used with or without the visual input enhancement

method to promote learners’ cognitive processing beyond mere noticing of forms. Izumi (2002)

partially addressed the issue by investigating the facilitative effects of output practice in addition

to visual enhancement on drawing learners’ attention and consequent learning of grammatical

forms in a controlled experimental study.

The total of 61 adult ESL (English as a Second Language) learners were randomly

divided into different treatment groups, each of which was exposed twice to the target form,

English relative clauses, presented in reading text through different combination of output

practice and visual input enhancement. The output practice was assumed to facilitate learners’

noticing of forms by inducing them to realize problems in their production, which gives learners

heightened awareness of the target form provided in subsequent input. Note-taking of form-

related words was used to measure learner awareness at the level of noticing while learners were

reading texts. Yet, it was also assured that the absence of note-taking did not mean the absence of

noticing of the form. The instruction given for note-taking was different for the output groups

and the input enhancement groups; while the output groups were asked to take notes of any

words which they thought important to the following text reconstruction tasks, the enhancement

groups were asked to take notes for the following reading comprehension tasks. Acquisition of

the forms was measured through various production (e.g., sentence combination, pictured-cured

sentence completion) and reception (e.g., interpretation, grammaticality judgment) tests.

Unlike Alanen (1995), who found positive correlation between the noticing and the

learning of forms in some groups, Izumi did not find such relationship for either output practice

16

or visual enhancement groups. To be detailed, the visual enhancement had a significant effect on

the number of noticing, but failed to produce any measurable gains in learning of the target

forms. On the contrary, the output practice caused great improvements on learning of the forms

despite it did not create any evident impact on noticing of the forms. Izumi explained that the

failure of visual enhancement to facilitate learning was because noticing was not automatically

led to further cognitive processing which might be necessary for acquisition. It was also argued

that what is really important for acquisition of grammatical structure is not the quantity of

attention, but the quality of attention, that is, the level and type of attention and processing. A

shallow level of attention, such as maintaining continued attention to certain forms at one level

without shifting to a deeper processing level, is not enough to prompt learners to learn the target

forms. In this study, the visual input enhancement was claimed to cause only a shallow level of

attention while the output practice led the learners to go beyond the superficial level and acquire

the form.

Izumi also called attention to the notion of integrative processing and its relevance to

SLA in that it emphasizes not only learners’ attention to individual elements, but also their

understanding of the relationship among the elements. And it is the latter which was missing in

the visual input enhancement, but present in the output practice. However, Izumi did not provide

any statistical evidence nor explain how the output practice encouraged learners to connect all

the related elements and conceive them as a coherent structure, the English relative clauses. In

addition, it is not clear why the output practice group did not notice the form, but was still able to

learn it. It could be argued that the output practice group’s note-taking for sentence

reconstruction caused qualitatively different cognitive processing than the note-taking for

reading comprehension since as Izumi himself argued that production requirements could lead

17

learners to process language differently than the simple comprehension requirements.

Summary

The current section was devoted to the review of experimental and quasi-experimental

studies with regard to learning L2 grammar. In particular, different instructional methods (e.g.,

visual input enhancement, metalinguistic rule presentation, output practice) to draw learners’

attention to the forms were examined in terms of their effectiveness and limitations as well. Most

of the instructional methods discussed above are based on the basic premise that the amount of

attention to form is positively correlated with the levels of learning the form. Interestingly,

however, the textual manipulation of forms failed to show greater impact on learner performance

despite its posited instructional benefits for drawing learners’ focal attention to forms. On the

other hand, other explicit methods, such as explicit explanation of rules and production practice,

produced measurable gains in terms of noticing and learning the target forms. In many studies,

however, learners who received combination of more than one method (e.g., visual enhancement

and output practice) outperformed other groups who received only either type of method, which

necessitates further research on what other combinations are available and which combination

works best in a specific context.

There are some other issues to be considered. First, focusing on only one instructional

factor, that is, how to draw learners’ attention, the studies did not give much consideration to

other mediating variables involved in L2 learning such as learner prior knowledge. Second, few

studies examined the effects of the learning task itself and the direction on what to do with the

task to induce learners to attend to and to learn the form, despite previous research has revealed

they might have certain impact on them (Rosa & O'Neill, 1999; Williams, 1999). According to

18

Rosa and O’Neill (1999), their subjects benefited from the problem-solving jigsaw puzzle task

and the type of directions accompanying the task in noticing and processing the form. In

particular, they claimed that regardless of the types of treatment group to which they were

assigned, all the participants improved significantly from the pretest to the posttests due to the

properties of the puzzle task (task essentialness and immediate feedback).

Finally, there is a need to develop diverse ways to deliver visual input enhancement.

Previous studies simply highlighted the target form and its components using different fonts and

other textual manipulation (i.e., shadowing, underlining, and italicizing) that did not possibly

give too much information about the relationship among the components. As White (1998)

pointed out, more elaborated methods to indicate the relationship, such as arrows or color-

coding, could help learners to understand the reasons of highlighting the target elements and their

relationships.

Based on the findings and limitations presented in this section, the present study will

examine the effect of a combination of instructional methods – visual enhancement plus

metalinguistic rule presentation as well as the effect of each alone. In particular, it will employ

more elaborated multimedia techniques (i.e., color coding, aural explanation instead of text,

electronic flashing arrow, an animated pedagogical agent) to deliver these methods in a

computer-assisted learning environment. Given the recent trend in the field of L2 instruction that

instructions are increasingly delivered through computers and multimedia, it would be worth

investigating what other multimedia delivery methods are available and which ones are useful

for drawing learners’ attention and facilitating L2 grammar learning. Furthermore, the present

study will conduct a study confirming to methodological standards by taking into consideration

learner prior knowledge and controlling instructional tasks as well as directions for carrying out

19

them.

Cognitive Efficiency of Multimedia Learning

Media researchers now agree to a certain extent that questioning about the effect of one

medium over another on cognitive products is a circular and fruitless argument, and it is time to

move our focus on media impact on learners’ cognitive process and on how to utilize media to

improve the process {Clark, 1998; Mayer, 1997, 2001}. One of the media researchers who are

concerned with how something is learned and how media can help this process is Tom Cobb who

proposed to include ‘Cognitive Efficiency’ as a variable in media studies (Cobb, 1997). As

mentioned above, cognitive efficiency refers to one medium requiring more or less mental effort

or time over another medium (Cobb, 1997; Clark, 1998).

Under the proposition of cognitive efficiency, there are two independent, but interactive

variables; the mode of presenting an instruction and individual or group differences in terms of

their prior knowledge and experience that would influence whether they process the instruction

faster and/or easier (Clark, 1998). Therefore, researchers may ask questions like how many hours

are required for a particular learner to learn a unit of learning material with one particular

medium vs. another medium or how much mental effort a learner invested in processing

information when different media applied. Hence, cognitive efficiency has varying degrees of

relevance to media selection.

Yet, cognitive efficiency is not about choosing the unique and best medium for

presenting an instruction. Rather, it is more about choosing the most cognitively efficient

medium among the alternatives for a given task and given learners. This section reviews

20

theoretical frameworks and empirical findings associated cognitive efficiency in multimedia

learning. It further looks at instructional and design principles to build cognitive efficiency in

multimedia learning environment. These will be the basis for a discussion in the next section

about animated pedagogical agents (APA’s) since an APA is one of the multimedia delivery

methods used in this study.

Theoretical Constructs Relevant to Cognitive Efficiency

As mentioned above, cognitive efficiency has not yet attracted theoretical or empirical

attention from both researchers and educators, in spite of its possibility to move forward the field

of media research. Consequently, it has not been able to build up theoretical support for

conducting empirical studies in order to confirm its promises, which necessitates for the present

study to examine relevant ideas and constructs from existing media studies. Among various

theoretical constructs found in media research, the concept of mental effort is closely related to

cognitive efficiency. Mental effort is typically defined as “the number of non-automatic

elaborations applied to processing a unit of material” (Salomon, 1983, p.44), or “the amount of

actual effort invested in the lesson” which includes “cognitive activities such as perceptual

processing, searching memory for appropriate schemata, and elaborating on the content”

(Cennamo, 1993, p.15).

There are a few concepts similar to mental effort, although there is a lack of agreement

among researchers in defining this type of mental construct (Flad, 2002). For example, Posner

and Snyder (1975) proposed the term ‘conscious attention’ compared to ‘automated attention’ to

indicate a mental process put into operation when making overt responses, retrieving information

from memory, and developing a hypothesis. On the contrary, Shiffrin and Schneider (1977) used

the term ‘conscious attention’ to describe the level of cognitive involvement beyond the

21

automated processing. There is another construct called ‘cognitive load’ which has been

extensively studied by John Sweller and his colleagues (Sweller, 1999; Sweller & Chandler,

1994, Sweller, Cooper, Tierney, & Cooper, 1990; Sweller, van Marrienboer, & Paas, 1998).

Cognitive load refers to the total amount of mental activity imposed on working memory at one

time. What is common among all these concepts is that they are expended only in processing

declarative knowledge (e.g., knowing concepts, processes, and principles), which requires

learners’ conscious attention and processing, rather than highly automated (requiring little no

conscious processing) procedural knowledge (e.g., knowing how to do something).

Mental Effort Measurement

Given its importance in cognitive efficiency, mental effort should be considered and

incorporated into designing multimedia learning. Then, measuring the exact amount of mental

effort invested by learners becomes the first step to take in helping learners learn more efficiently

with media. However, the construct of mental effort is hard to measure directly (Clark, 1999b)

since it is not directly observable, and as a consequence, there have been several methods and

techniques developed to indirectly assess the amount of mental effort. They could be grouped

into three main categories according to what kind of processing they are measuring: self-report

opinion measures, dual-task measures, and physiological measures. Each method corresponds to

the areas of introspection, information processing and neural processing respectively (Cennamo,

1992).

Self-Report Measures

Mostly in a self-report format, subjective opinion measures assume that investing mental

effort is an intentional and non-automatic process, and readily accessible by individuals

(Beentjes, 1989). It also presumes that individuals are relatively capable of reporting the mental

22

effort which they put in processing learning materials (Salomon, 1984). Opinion measures

include Inventory of Learning Processes (ILP) scales designed to measure individual differences

in the level of processing, Amount of Invested Mental Effort (AIME) questionnaire developed by

Gabriel Salomon (1983), and several scales used to assess mental workload imposed in operating

a flight simulator such as the Cooper-Harper aircraft handing rating scales and Workload

Compensation Interference/Technical Effectiveness scales (Casali, Wierwille, & Cordes, 1983).

All of these measures have a reputation of being most efficient to use and having high

reliability (Dweck, 1989). Among them, the AIME questionnaire has been frequently used in the

studies examining differential effect of media on learners’ mental effort investment (Beentjes,

1989; Salomon, 1983; Salomon, 1984; Salomon & Leigh, 1984). Typically, the AIME

instruments ask subjects to answer a set of questions on a 4 point Likert-type scale about how

much effort they think they have invested or how much they concentrated in processing a

particular unit of material (Salomon, 1983). Recently several researchers have questioned the

validity of self-report measures of mental effort and have called for the use of more precise and

multiple methods (Beentjes, 1989; Gimino, 2000).

Dual-Task Measures

Dual-task measures encompass a variety of methods that require a subject to work on a

primary and a secondary task simultaneously. Among the various secondary tasks, finger

tapping, digit shadowing, and memory-scanning tasks are used most. The dual-task method is

based on the notion that one’s cognitive capacity is limited, and when much of the cognitive

capacity is used by a conscious, non-automated primary task, there will be less cognitive

capacity available for the secondary task. The amount of invested mental effort is computed

based on the difference between the baseline performance of the secondary task and the

23

performance in the experimental condition (Cennamo, 1992) where the primary and secondary

tasks are performed at the same time. The baseline performance of the secondary task is the

reaction time to perform the secondary task alone and it is measured before the primary task is

presented. The increase in reaction time to the secondary task in the experimental condition

indicates the amount of conscious effort allocated to the primary task. Several studies that

applied various dual-task measures have found these measures sensitive to differences in

cognitive task demands, that is, different metal effort requirement of different tasks (Gimino,

2000).

When using dual-task methods, it is important for researchers to decide at which point in

the experiment learners are asked to respond to a secondary stimulus. That is because there is

individual difference in the speed of processing the primary task (Gimino, 2000). For instance,

some learners might face the secondary task when they are closer to the solution, which imposes

high cognitive load and causes longer reaction time. On the contrary, learners who are

interrupted by the secondary task closer to the beginning do not need to spend as much mental

effort. In addition, structure and complexity of learning material could influence mental effort

expenditure. That is, although the overall content of the lesson is easy, some parts of the content

may not be so. If learners have to respond to the secondary task while the presented material is

complex, the level of mental effort would be shown high. However, that does not mean the

material requires a great amount of mental effort (Cennamo, 1996).

Physiological Measure s

Researchers have been using physiological measures to measure psychomotor,

perceptual, communication and cognitive task demands. Physiological measures assume that

there are physiological reactions in subjects to increases in mental effort spending while the

24

subjects are performing a task. The difference between a baseline measurement and a

measurement taken in the experimental condition in which a leaner is working on an assigned

task indicates the amount of mental effort. The physiological measures include a range of

techniques such as monitoring heart rate, blood pressure, or respiration variability, using

electroencephalogram (EEG), and recording the number of eye blinks per minute (Wierwille,

Rahimi, & Casali, 1985). Although physiological measures are extensively used in the fields like

human factors engineering, they are not very practical to use in classrooms due to high cost

equipments (Gimino, 2000).

Each measurement technique discussed above seizes different aspects of the construct of

mental effort (Fisher & Ford, 1998), which requires researchers and educators to carefully

inspect the purpose of their study and to decide what aspects of mental effort they plan to

measure. Furthermore, research has shown that the sensitivity of different measures depend on

the type of task placed on learners (Casali et al., 1983; Wierwille et al., 1985). Therefore, it is

necessary for educators and researchers to take into consideration the type of learning tasks they

will use when designing a study in order to obtain more accurate results.

Building Cognitive Efficiency in Multimedia Learning

Cognitive load refers to the total amount of mental activity imposed on working memory

at one time (Cooper, 1998), and one of the theories that is concerned with cognitive load is John

Sweller and his colleagues’ ‘Cognitive Load Theory’ (Sweller, 1994; Sweller & Chandler, 1994;

Sweller et al., 1998). A fundamental idea of the cognitive load theory is that working memory is

limited in terms of the amount of information it can hold and process at a time, and thus, it

should not be overloaded with too much information at once (Chandler & Sweller, 1991;

Kalyuga, Chandler, & Sweller, 1998; Sweller & Chandler, 1994; Sweller et al., 1990; van

25

Gerven, Paas, & Schmidt, 2000). Another important idea of the cognitive load theory is that

working memory consists of partly independent auditory and visual buffers (Baddeley, 1992),

and each buffer processes different forms of information accordingly. With separate working

memory buffers being utilized to process information presented in auditory and visual modes, it

is hypothesized that the capacity of working memory will be increased. Consequently, the

amount of information that can be processed for the fixed amount of time will be increased,

which will in turn result in improved efficiency of cognitive processing.

Given the definition of cognitive load and its conceptual similarity to mental effort, it

can be inferred that by reducing the amount of cognitive load placed on working memory, it is

possible to impose less cognitive demand and to request less mental effort from learners, and

eventually improves the efficiency of cognitive processing. Then, how can we reduce the amount

of cognitive load? Cognitive load consists of intrinsic and extraneous loads (Chandler & Sweller,

1991). The former is solely determined by the nature of learning material itself, such as task

complexity or high interactivity of the elements of the learning material, meaning that each

element cannot be learned without reference to other elements. On the other hand, the extraneous

component of cognitive load results from the instructional format or strategy used to deliver the

instruction. The problem with extraneous cognitive loads is that they are irrelevant to learning,

thus not leading to schema acquisition and automation, two major products of human learning

(Sweller & Chandler, 1994). Since it is not possible to change the intrinsic nature of instruction

or to lower the levels of intrinsic element interactivity, it is best to keep extraneous cognitive

load minimum so that the total amount of cognitive load imposed by a task falls within the

limited mental resources of working memory (Chandler & Sweller, 1991).

Instructional Strategies to Reduce Cognitive Load

26

There are several instructional strategies that can be used to reduce the level of

extraneous cognitive load (Sweller, 1999) and consequently the amount of mental effort: (a)

using goal-free problems that do not allow learners to employ means-ends strategies and

backward reasoning. With a typical goal-fee problem unlike a goal-specific problem, a learner

does not have to keep main goals and sub goals of the problem in working memory, which in

turn saves working memory resources; (b) using worked examples in which the problems

accompanied by their worked-out solutions. Worked examples do not force learners to use

means-ends strategy which demands a considerable amount of working memory capacity; (c)

avoiding split-attention effect which results from presenting mutually referring information

separately. For example, when presented with mutually referring information separately, a

learner has to split his or her attention and again mentally integrate them, which results in

imposing a high level of extraneous cognitive load. Instead, the information should be presented

in a physically integrated format; (d) distributing information over different modalities by which

two different modal processors, a visuo-spatial sketchpad and a phonological loop, are utilized

simultaneously in working memory; and (e) avoiding providing redundant information through

different media.

Among these five instructional strategies, the last three have specific relevance to

multimedia and cognitive efficiency, and thus to the present study. Several studies have been

conducted to test if these three presentation modes have significant impact on cognitive

processes and learning outcomes (Jeung, Chandler, & Sweller, 1997; Mayer, Moreno, Boire, &

Vagge, 1999; Moreno & Mayer, 2000a, b; Mousavi, Low & Sweller, 1995; Sweller & Chandler,

1994). Yet, these studies mainly focused on measuring learners’ cognitive products or final

performance, not on their learning processes. So instead of asking questions like ‘how long did it

27

take to learn the materials presented through a specific mode?’ or ‘Was it easier to learn with one

specific medium over another?’, these studies are more interested to know about whether

learners perform better in a number of performance tests (e.g., knowledge of simple facts,

knowledge transfer) after interaction with a certain presentation mode. This lack of studies on

learning process emphasizes the importance of the present study because it will investigate

relative effect and efficiency of different media on learning process as well as final performance

when learning a second language.

Avoiding s plit a ttention effects and Utilizing modality effects

The split attention effect occurs when a lesson consists of two or more different sources

of information that are incomprehensible until they are mentally integrated by a learner (Mousavi

et al., 1995), such as a separately presented geometric diagram and accompanying sentences that

are spreading across a page or computer screen. In this situation, learners have to find referential

connections between corresponding aspects of the diagram and sentences, since they cannot be

understood in isolation. This extra mental integration process requires cognitive resource, and as

a result, there might not be enough resources left necessary to achieve more essential learning

objectives, such as schema acquisition. Several research have shown that by physically

integrating multiple sources of learning material (e.g., placing explanations in appropriate places

near a diagram), it is possible to avoid split attention and to prevent learners from investing

mental effort in unnecessary processes (Chandler & Sweller, 1991; Mayer & Moreno, 1998;

Mayer, Heiser, & Lonn, 2001, Experiment 1 & 2; Sweller et al., 1990; Sweller & Chandler,

1994; Ward & Sweller, 1990).

Split attention effects can also be reduced by utilizing modality effects. Modality effects

can be obtained when we increase the effective size of working memory by utilizing different

28

types of working memory processors (Jeung et al., 1997), derived from a popular assumption in

memory research that there are multiple working memory stores, also called multiple memory

channels or processors. A typical hypothesis of dual processing modalities is that students who

receive verbal information in narration along with animation or diagrams simultaneously or

sequentially outperform those who receive the verbal information in an on-screen text format,

when other things being equal (Moreno & Mayer, 1999, Experiment 2; Mousavi et al., 1995). By

presenting an explanation in an auditory format learners can utilize the verbal processing channel

in working memory as well as the visual channel, which eventually increase limited working

memory capacity.

Jeung et al. (1997) also agreed that mixed mode of presentations (e.g., visual diagrams

and auditory text) would have more working memory available for learning compared to a single

mode of presentations of equivalent contents (e.g., visual diagrams and written text). Yet they

took a further step that clearly has very significant implications in designing instructional

multimedia. They hypothesized that a multimodal presentation would be useful only when

learners do not have much difficulty with relating audio and visual information. Furthermore,

they also suggested that when learners have to invest a high level of cognitive resource to search

for connections between two sources of material due to the complexity of the diagram, a visual

indicator such as flashing or color would reduce learners’ search effort so that cognitive resource

could be used for learning the material.

Leaving out redundancy effects

A common assumption in multimedia learning is that different instances of the same

information, or simply stated, repeated information in different modalities would enhance

learning by allowing learners to choose the presentation mode that best fits there learning

29

preferences. However, it has been repeatedly shown that if two different segments of information

can be understood in isolation, one of them is redundant, and removing this redundant

information has positive effect on learning and performance (Bobis, Sweller, & Cooper, 1993;

Chandler & Sweller, 1991; Harp & Mayer, 1997, 1998; Mayer et al., 2001; Sweller & Chandler,

1994, Experiment 2). Moreover, Chandler and Sweller (1991) revealed, not only redundant

information is unnecessary for learning, but also it in fact interferes with the learning of core

information by leading learners to pay attention to redundant material, and by imposing

extraneous cognitive load on working memory.

Another major source for redundancy effect is adding interesting but conceptually

irrelevant material, commonly defined as seductive details, to a lesson in an attempt to induce

learner interest. Seductive details are redundant in that they are in many cases added to entertain

learners not because they are essential to the acquisition of core elements of a lesson. The

underlying premise of including entertaining elements in a multimedia presentation is that

increased learner interest will lead learners to pay more attention, to persist longer to learn, and

to exert more effort to process instructional material. Moreno and Mayer (2000a), however,

found that adding entertaining, but irrelevant to learning, music and sounds interfered with

learners’ recall and understanding of the lesson. Any additional elements that are not crucial for

understanding of core material decrease working memory capacity, and as a result, learners are

left with few cognitive resources to use for processing core elements. The result is poorer

performance.

Research also points out that including seductive details that lack conceptual relevance

to main ideas of the lesson hinders learning process since they could take learner attention away

from selecting and processing key elements of the lesson; causing split-attention effect (Mayer et

30

al., 2001). In addition, entreating but irrelevant information may prime inappropriate knowledge

about the topic and reduce the amount of cognitive resources that learners can use for processing

essential information. Entertaining elements are most seductive when they are novel, active,

concrete, and personally interesting (Garner, Brown, Sanders, & Menke, 1992) such as animated

graphics and characters embedded in computer-based lessons.

Integrating Leaner Prior Knowledge and Expertise

Although the theories and research findings discussed so far are very much

comprehensive and convincing, there is still one missing component, that is, learners. As many

educators have argued, learners should be and in fact, are active participants of their own

learning (Dalgarno, 2001; Wertsch & Bivens, 1992). They faithfully play their part in learning by

bringing varying degrees of prior domain knowledge and expertise although often ignored by

instructional designers. In effect, it is reported that differences in the levels of learner expertise

make very significant differences in cognitive processing and performance (Ericsson &

Charness, 1994). According to Kalyuga et al., (1998), there are two important concepts to

understand learner expertise and prior knowledge, ‘Schema’ and ‘Automation’. They define

schema as “a cognitive construct that permits people to treat multiple sub elements of

information as a single element, categorized according to the manner in which it will be used”

(p.1). Thanks to s schema, which is stored in our long-term memory, we can identify various

kinds of dogs as a dog, and as a consequence, avoid overburdening working memory. One of the

important characteristics of schemata is that they are transferable (van Gerven et al., 2000). That

is, we can transfer an existing schema to new problems as far as they are related to the earlier

encountered problems.

Another feature of schemata is automation. Automation decreases working memory load

31

(Kotovosky & Simon, 1985; Shiffrin & Schneider, 1977), and requires little cognitive resources

since automated knowledge can be processed unconsciously. Schemata are saved in long-term

memory with varying degrees of automaticity, and depending on the degree of automaticity,

schemas require a different amount of working memory resources. Therefore, if a learner faces

with a problem for which he or she has a fully automated schema, then the learner does not have

to spare cognitive resource for organizing and categorizing the problem. Instead, more conscious

effort can be used to search for a solution and performing a task, and thus, a better and faster

learning process can be achieved. Keeping the effects of learner expertise on learning in mind,

we may have to ask the following questions in designing multimedia learning environment: Do

seemingly redundant learning materials have the same effect on every learner regardless of their

level of expertise? Or in reality are they redundant to only some people?

Kalyuga, Chandler, and Sweller (2000) reported that while inexperienced learners

benefit from dual-mode presentation (i.e., a diagram with auditory narration), more experienced

learners do not need additional source of information since they have already acquired sufficient

knowledge to fully understand one source of information. Furthermore, it was found that

experienced learners actually were able to skip the extra information, the auditory narration,

when given the options to do so, and thereby there was no redundancy effects found. Yet, under

the condition in which experienced learners were forced to attend to both auditory and visual

information, their performance became worse because they had to process redundant

information, which probably imposed extra cognitive loads on learners’ limited working memory

and required unnecessary mental effort investment. The authors concluded that multimedia

instructions have beneficial effects on learning only under well-defined circumstances, and in

particular, learners should be given options to adapt themselves to a learning environment in

32

order to take into consideration differing levels of learner expertise.

Summary

In this section, the construct of cognitive efficiency and its importance in multimedia

learning was discussed. In addition, several instructional strategies were reviewed that can be

used to prevent working memory from being cognitively overloaded and to require less mental

effort from learners in multimedia learning environment. Sweller and Chandler (1994) suggested

that by avoiding the limitations of working memory it is possible to increase the efficiency of

learning process. In learning situations, therefore, the total amount of cognitive load imposed by

instructional materials should not exceed the limited capacity of working memory to speed up

learning process and to achieve successful performance.

Moreover, it has been shown that learner prior knowledge is also an important factor to

be considered when designing multimedia-based instruction since different degrees of learner

prior knowledge have different effects on learning processes and outcomes as well. Hence, it is

necessary to examine the role of learning prior knowledge in cognitive efficiency of multimedia

learning, and the present study hypothesizes that there will be a significant positive correlation

between the level of learner prior knowledge of L2 grammar and the level of cognitive

efficiency.

In other words, the more prior knowledge learners have of a target form, the less mental effort

and time they will spend to process learning material.

Another important point has been made in this section about adding interesting but

conceptually irrelevant material to a lesson in an attempt to induce learner interest. A body of

research has shown that any additional elements that are not crucial for understanding of core

elements decrease working memory capacity, and as a result, learners are left with few cognitive

33

resources to use for processing core elements. Research also points out that any additional

elements which are entertaining but unrelated to learning could take learner attention away from

selecting and processing key elements of the lesson; causing split-attention effect. This point is,

in particular, closely related to the next section on animated pedagogical agents because the

major reason behind using APA’s is to entertain students and direct attention to instructional

elements. Yet, given that entertaining elements could also split learner attention and simple

flashing arrows can assist learners to focus and integrate related elements (Jeung et al., 1997),

the effect of APA’s on learning processes and outcomes should be carefully examined and

compared with other alternative ways. The present study hypothesizes that there will be no

significant difference between APA’s and flashing arrow in learner performance when learning

L2 grammar, but the flashing arrow will require less time and mental effort resulting in better

cognitive efficiency.

Animated Pedagogical Agents

An animated pedagogical agent, by definition, is a lifelike character that resides in an

interactive computer-based learning environment. It is a product of recent technological

advances in user interface and autonomous software agents, and has been claimed to have great

potential in facilitating human learning by offering customized instruction, and context-specific

feedback and advice to learners (Johnson et al., 2000; Moreno, Mayer, & Lester, 2000; Samson,

Karaginaanidis, & Kinshuk, 2002). It is also one specific type of intelligent interface agents

whose main purpose is to provide learners with pedagogical assistance. In other words, an

animated pedagogical agent is a specific form of media used to present instruction, not an

instructional method itself, and thereby what an animated pedagogical agent does can be

34

delivered through other media with different degrees of efficiency and effectiveness.

The field of animated pedagogical agent has originated from two research areas:

‘Animated Interface Agent’ and ‘Intelligent Tutoring System’ (Johnson et al., 2000). The

research on animated interface agent provides a new way of human-computer interaction by

applying the features of face-to-face human communication to human-computer interaction. This

field has had great impact on the technological development and design of animated pedagogical

agents, especially, its emphasis on lifelike and believable agent behaviors, such as gesture, facial

expression, and gaze. On the other hand, the intelligent tutoring system focuses on developing

software that can adapt to individual learners and provide personalized feedback through the use

of artificial intelligence. From this, the field of animated pedagogical agent has taken on its

capability to provide personalized tutoring experience by keeping track of student progress over

time and accordingly generating pedagogical actions to meet the needs of individual students. By

incorporating these two areas of research, animated pedagogical agents are believed to enhance

computer-based learning, especially affective and motivational aspects of learning experiences

(Atkinson, 2002).

Animated pedagogical agents have a range of functions depending on the environment

which they inhabit: (a) it can provide a learner with opportunistic instructions through which

they can respond and adapt dynamically to the surrounding environment including the learner

(Moreno et al., 2002): (b) it can interactively demonstrate how to perform tasks (Samson et al.,

2002): (c) it can focus learners’ attention on certain elements or aspects of instructional systems

with common and natural methods, such as gestures, locomotion, or gaze (Atkinson, 2002): and

(d) It can provide nonverbal as well as verbal feedbacks on learners’ actions. In particular, the

capability of using nonverbal communicative behaviors allows the tutoring system to provide

35

less obtrusive feedback (i.e., head-nodding for approval, head-shaking for disapproval, jumping

up and down for congratulating students’ success, look of puzzlement for misunderstanding)

(Johnson et al., 2000).

Benefits of Animated Pedagogical Agents

A number of instructional benefits have been claimed and studied on APA’s although no

one agent has provided all of the benefits to date (Johnson et al., 2000). A review of literature

shows that animated pedagogical agents in general have three possible effects on learner-

computer interaction: (a) it can have a positive impact on learners’ motivation and perceived

experience of interaction with the system: (b) it can attract learners’ attention on the system or

tasks through the use of motion, gesture, and facial expression, and: (c) it can also improve

learning outcomes (i.e., problem solving, understanding of knowledge) by providing learners

with contextualized advice. In particular, advocates of animated pedagogical agents maintain that

agents render learning environment entertaining, which in turn motivate learners to interact more

and to stay longer in the system. Due to the increased motivation and interaction, it is assumed

that learner performance would improve. Among these three effects, however, the first two

effects have close bearing with the present study, and thus, in the following section, they will be

discussed in relation with learning process and outcomes based on empirical research findings.

Motivating Learners

One of the biggest benefits of using pedagogical agents put forward is that agents can

entertain and motivate students better than other media and consequently, lead students to exert

more efforts to make sense of instructional material. This claim is based on the interest theories

of motivation (Harp & Mayer, 1998), which suggests that learners invest more effort when they

are interested in the presented learning material. A similar effect, called “Persona Effect” has also

36

been the focus of research in the field. Precisely, the persona effect refers to learners’ positive

perception of their learning experience caused by the presence of the agent (Moreno et al., 2001).

The persona effect is derived from the hypothesis that people interpret their interaction with a

computer or a computer-mediated figure as a social interaction (Reeves & Nass, 1996) and they

form personal and emotional connection with a computerized character. This personal and

positive feeling is believed to foster interest in learning tasks and lead learners to work harder

(Lester, Converse, Kahler, Barlow, Stone, & Bhogal, 1997). Although these two effects have

originated from different theoretical backgrounds, they will be discussed together in the

following since they are concerned with similar phenomena – learners’ subjective experiences of

learning

Numerous claims have been made on the positive influence on learning brought by

increased motivation and positive perception. Yet, it should be pointed out that increased

motivation or positive perception does not necessarily have a cause and effect relationship with

students’ actual learning outcomes. In other words, although students enjoy their interaction with

an agent, it does not mean that they will learn better (Lester, Voerman, Towns, & Callaway,

1997). In fact, many studies which employed animated pedagogical agents to deliver instruction

found the persona effect, but were not able to find any significant cognitive learning benefits of

the agents. In the studies where better learning outcomes were found for the students who

interacted with agents, the benefits were more likely caused by either different levels of advice or

different instructional methods rather than higher interest or motivation triggered by the presence

of agents (Clark & Choi, 2003; Dehn & van Mulken, 2000).

The lack of increased learning outcomes in spite of high levels of interest elicited by

agents could be explained by the theories of individual and situational interests. Individual

37

interest refers to a relatively enduring predisposition towards certain objects or events, which

develops slowly overtime (Ainley, Hidi, & Berndorff, 2002). Situational interest, on the other

hand, is psychological state triggered by immediate environmental stimuli, and as a result, may

not have lasting influence on personal interest or learning (Hidi & Anderson, 1992). Given that

an animated pedagogical agent is an environmental stimuli embedded in a computerized learning

environment, not a learner-developed characteristic, it can be inferred that learner interest

generated by an agent might not last long enough to have significant impact on learning

outcome. It also could be explained by the novelty effects (Clark, 1983).

One of the studies that examined the motivational effect of an agent on learning is

Moreno et al.’s (2001). They investigated whether learners make more effort to understand

material and accordingly achieve deeper learning in a ‘social agency environment’ (Experiment 1

and 2). Participants in the experiment group interacted with a pedagogical agent, ‘Herman’, an

alien bug residing in a discovery- and design-based learning environment called ‘Design-A-

Plant. The experiment group participants were asked to design a plant based on environmental

conditions (e.g., the amount of sunlight and rainfall), and received verbal feedback from the

agent on their choices. In contrast, participants in the control group were given onscreen text

which explained about making right choices under specific environmental conditions using step-

by-step worked examples instead of the agent. But they were not allowed to design plants by

themselves unlike their counterpart in the experiment group.

In the immediate posttests, the researchers found significant learning differences in

problem solving questions between the agent and non-agent conditions while found no difference

in the retention questions. Their interpretation of the results was that students interacting with the

agent felt personal connection with the agent and interested in the task, which in turn resulted in

38

more effort and better performance. However, it should be pointed out that the two groups

received different instructional treatments in addition to presence or absence of the agent. Only

the agent group had opportunity to design plants and received contingent feedbacks, and thus the

better performance of the agent group cannot be exclusively attributed to the presence of the

agent. Moreover, although the agent group indicated higher interest in the material than the non-

agent group, the groups did not show any significant difference in their subjective ratings of how

difficult or understandable the material was, which can be related to the amount of mental effort

they put during the learning (Salomon, 1984). Moreover, it should be mentioned that the amount

of mental effort invested by the participants during learning was not measured, and their claims

were solely based on the results of simple t-test on the levels of self-reported interest of two

groups.

In a study using five clones of Herman the alien bug, Lester et al. (1997) also examined

affective impact of animated pedagogical agents. The five clones were identical in their

appearance, voice qualities, and nonverbal communicative behaviors, but were different in the

levels of instructional advice and feedback they provided (task-specific vs. principle-based), and

in modalities they employed to deliver instruction (verbal-only or verbal and animated). The

students who interacted with the agent having full functionalities and modalities produced

highest performance and gave the highest ratings to subjective assessment questions asking about

helpfulness, believability and utility of the agent’s advice. Based on the simple analyses (i.e.,

means, standard deviations), the researchers interpreted that enhanced learning was likely to

result from students’ positive perception of learning experiences and increased motivation,

despite the obvious differences in instructional treatments.

Unlike the previous two studies that failed to control the effect of methods and

39

feedbacks, Andre, List, and Muller (1999) conducted a well controlled agent study. To find

empirical support for the affective and cognitive effects of the PPP Persona on man-machine

communication, they exposed participants to a technical description (the operation of pulley

systems) and an informational presentation (names, pictures, and office locations of fictitious

employees) on the Web. Both experiment and control versions provided the same treatments

except that the control groups did not have the PPP Persona; instead, a voice was employed to

convey the same explanations as the Persona, and its pointing gestures were replaced with an

arrow. The Following the presentations, the Persona’s affective effect was measure through a

questionnaire whereas the cognitive impact was measure by comprehension and recall questions.

The results showed significant differences in only the affective measures. Participants

interacting with the Persona for the technical descriptions found the presentation less difficult

and more entertaining. These positive effects, yet, were not found for the informational

presentation about the fictitious employees; rather subjects reported that the Persona was less

appropriate for the domain and less helpful as an attention direction aid. As for the learning

effect, no significant difference was found between the Person version and the non-Persona

version both in the technical domain and the non-technical domain. The results of this study is

consistent with Dehn and van Mulken’s claim (2000) that the persona effect of an agent is

domain-specific and can improve human-computer interaction if the agent displays the

functional behaviors matching the system’s purposes. The findings also suggest that Persona

behaviors can be easily replaced by simpler means of communication, not necessarily requiring

an embodied character.

For example, Erickson (1997) argued that the adaptive functionality of an instructional

system is often enough for learners to perform a task and achieve the same outcomes without the

40

guidance of an agent. He also suggested that when including an agent, instructional designers

should think about what benefits and costs the agent would bring, and far more research be

conducted on how people experience agents. Furthermore, Nass and Steuer (1993) found that

simply using a human voice was sufficient to induce learners to use social rules when interacting

with a computer. Moreno and colleagues (2001) also noted that learners may form a social

relationship with a computer itself without the help of an agent and thus, the image of an agent

might not be necessary to invoke a social agency metaphor in a computer-based learning

environment.

Given the studies discussed above, it is premature to conclude that the enchanting

presence of an agent causes instructional benefit unless an agent condition is compared with a

non-agent condition that provides the exact same learning conditions including the types of

instructional methods, and the levels of feedback and advice. In fact, Moreno et al. (2001) found

that students who interactively participated in designing plants and received contingent feedback

on their choices still learned better than those who just passively listened to the verbal

explanation, even after the image of the agent was deleted from the screen and the voice of agent

was replaced by human voice (Experiment 3). What is more interesting is that students in two

groups did not show any significant difference in their perception of learning experiences, which

was speculated to be the main cause of learning outcomes. That is, even without the benefit of

the presence of an agent, students were still able to learn the material better if an instructional

method was right for the task. Moreno et al. also found that the presence of the agent’s visual

image did not have any impact on affective or cognitive aspect of learning, while the modalities

of the agent made significant differences in learning outcomes (Experiment 4 and 5). These

results may imply that what makes a difference in student learning is not the agent itself or

41

increased motivation or positive perception caused by the agent, but rather the level of

interactivity and contingent feedback as well as modalities, which will be empirically tested in

the present study.

Focusing Learner Attention

According to cognitive load theory, the presence of an animated pedagogical agent can

be detrimental to learning by dividing a learner’s limited cognitive resources into different visual

segments. More specifically, cognitive load theory predicts that when animation of an agent is

presented simultaneously with other visual information such as graphics or texts, learners need to

split their attention between these two sources, and as a consequence, the presence of the agent

becomes harmful to learning rather than beneficial. The split attention effect could be even worse

when the agent’s dialogues are presented as onscreen text since both the animation and text

require learners’ limited visual resources (Moreno et al., 2001). However, even replacing

onscreen text with spoken text may not be enough to overcome the split attention effect if

learners have to mentally connect the visual information the agent is presenting and aural

information delivered through the spoken text (Jeung et al., 1997).

In order to prevent the split attention effect caused by the presence of an agent, it is

suggested that animated pedagogical agents use non-verbal behaviors (i.e., pointing gesture,

jumping to the target object) in order to draw learners’ attention to relevant learning material

(Atkinson, 2002). By focusing a learner’s attention to pertinent segments of the lesson, the agent

can connect auditory and visual information for learners, thus freeing learners’ cognitive

resources to be used for solving problems or understanding material. Yet, there is a possibility

that agents’ attention-focusing behaviors can act as seductive details in that they are not

conceptually related to primary learning objectives but still catching learners’ attention.

42

Additionally, despite that agents’ behaviors are supposed to help learners pay attention to the

lesson, learners still have to spare their limited cognitive resources to understand their behaviors

and facial expressions. Nevertheless, studies have shown neutral effects of presence of agents,

neither providing negative nor positive impacts on drawing learner attention (Atkinson, 2002;

Moreno et al., 2000).

Using worked examples to instruct proportion-word problem solving, Atkinson (2002)

examined the effect of presence of an animated pedagogical agent and delivery modes (e.g.,

voice or text) on learning. In the study, the animated agent was programmed to deliver

instructional explanations which contain elaborated information regarding solution steps for

word problems, either aurally through human voice or textually through a cartoon-like word

balloon appearing above the agent’s head. Yet, the contents of explanations were identical for

both conditions. The agent was also programmed to link the explanations with the respective

visual information on the screen by utilizing nonverbal communicative behaviors (i.e., pointing

gesture, glancing toward a solution step).

The analyses of learning-process and learning-outcome measures revealed that

participants in the voice plus agent group outperformed those of the text only group in perception

of difficulty of practice problems, near- and far-transfer problems and affective measures of the

learning environment, while their performance was superior to that of the voice only group only

in far-transfer problems. Atkinson interpreted this result relatively positively insisting that

presence of an agent did not distract learners’ attention. However, the fact that the voice only

group performed as well as the voice plus agent group in most assessments suggests that we

might not need the image of an agent to achieve similar learning criterion. Moreover, given that

the voice only group did not have the benefit of visual indicators connecting aural information

43

with appropriate visual information, it is not very clear what proportion of the learning benefits

can be attributed to the agents’ attention-directing behaviors. And again what should we choose

if other visual indicators rather than the agent’s nonverbal behavior, such as electronic flashing,

could have the same level of effect on focusing learner attention, but are a lot easier and more

economical to create?

Craig, Gholson and Driscoll (2002) provide a possible answer for the question proposed

above. They designed a 3 (agent properties: agent only, agent with gestures, no agent) x 3

(picture features: static picture, sudden onset, animation) study to investigate issues concerning

the ways to capture learners’ attention. For the agent property, they investigated: 1) whether the

use of an agent leads to split attention; and 2) whether integration of the agent’s gestures with a

picture or animation helps to direct learners’ attention. For the picture features, they tested if

using parts of a picture or animation captures learners’ attention. In the experiments, participants

learned the process of lightning formation presented through an agent and multimedia material

(e.g., pictures, narration, or animation). The narrative information was synchronized prior to or

simultaneously with the agent’s pointing gestures toward, or with a sudden onset (e.g., color

singleton or electronic flashing) or animation of relevant parts of a picture.

Again, they did not find the Persona effect, that is, participants did not find learning with

an agent particularly more interesting than that without an agent. Moreover, the agent made no

difference in learners’ performance both in cognitive load assessment and performance tests

(retention, matching, and transfer). On the contrary, Craig and his colleagues found significant

effect of both a sudden onset and animation of parts of the pictures for focusing learners’

attention. This result can be taken as evidence that a well integrated pedagogical agent is not

harmful to learning, but not beneficial either. It can be also inferred that there is a far simpler and

44

easier way (coloring and flashing) to have the same effect rather than using a technically

complicated agent. To clarify the confusion about the relative effect of a pedagogical agent for

directing learner attention, the present study compares an agent with an electronic flashing

arrow.

Summary

The review reveals that while there is strong enthusiasm persisting in the field regarding

the motivational and instructional effects of animated pedagogical agents in multimedia learning

environment, very few empirical studies have been conducted so far and the results of these

studies are rather neutral or sometimes negative. The literature does not support the assumption

that the motivational effects of animated pedagogical agents lead to better performance in

learning including problem solving and knowledge retention (Dehn & van Mulken, 2000). The

optimistic perspective of the field is largely based on the hope that rapidly advancing technology

will be able to fix the existing technical limitations that prevent an agent from fully imitating a

human tutor (e.g., recognizing individual learners’ learning styles and specific characteristics and

thereby providing more contextualized and individualized feedback).

As a matter of fact, a majority of the animated pedagogical agent studies published to

date have focused on technological capacity of agents without paying much attention to theory-

and research-based principles (Bradshaw, 1997). Yet, it should be pointed out that there are some

aspects of these studies that cannot be fixed by advanced technology, including methodological

shortcomings (e.g., theoretical foundation, study design, variable manipulation, and

measurement of effects). For instance, only few studies did compare the agent-embedded

environment with the non-agent environment on an equal basis. In many cases, there were other

significant factors in addition to the agent which could have contributed to differences in

45

learners’ attitudes and learning. The instruments employed to measure the effects of agents have

posed another major problem. Several studies failed to provide the validity and reliability of the

instruments they used to measure effects, which consequently undermined researchers’

interpretation with regard to agent effects. At the point, thus, we need a more theory-based

approach to investigate agent-embedded learning environments, and more structured research

methodologies to investigate the effects of these environments.

What is clear about the field of animated pedagogical agent is that due to its infancy of

development, only a small number of studies have been conducted, and even worse, not many

studies conducted so far have employed systematic and thorough research methods (e.g., a small

sample size, confounding variables, unreliable measuring instruments). In addition, the fact that

only a small number of knowledge domains have been the focus of agent studies does not help to

understand what kind of domain could benefit most from animated pedagogical agents with what

kind of functionalities. For example, most pedagogical agent studies have employed discovery-

based learning environment and scientific materials for instruction even though there is evidence

that entertaining effect of an agent is domain specific (van Mulken, Andre, & Muller, 1998).

Consequently, it is not clear that what effects would be gained in different environments with

different subject matter. The present study is expected to contribute to the field in this aspect

since it examines the effect of pedagogical agents for learning L2 grammar, a rarely explored

subject matter.

Research Hypotheses

The review of literature in three research areas – second language learning, multimedia

learning, and animated pedagogical agents – is the foundation for this study. The primary

46

objective of this study is to solve the confusion between instructional methods and animated

pedagogical agents, and to empirically examine cognitive efficiency of different media in

learning second language (L2) grammar in a computer-assisted learning environment. Learners’

focal attention to target grammatical elements is required for learning of L2 grammar (Rosa &

O’Neill, 1999) because only the attended form can be perceived and processed by learners.

Several instructional methods have been developed to direct learner attention to target linguistic

features. The present study will use two instructional methods to focus learners’ attention on a

target L2 grammatical form, English relative clauses: explicit metalinguistic explanation about

the target form and its use, and visual enhancement of the form embedded in input text.

The first instructional method, the metalinugistic rule explanation, will be presented in

an aural mode rather than in a text mode to avoid the split attention effect between the

explanation and the main text. For the visual enhancement treatment, the present study will make

use of two computerized multimedia techniques: an animated pedagogical agent and a flashing

arrow. These two multimedia delivery methods will be compared on the extent to which they

improve cognitive efficiency in acquiring an L2 grammar, while the two instructional methods,

the explicit explanation and the visual enhancement, will be compare in terms of their effects on

learning outcomes. The research hypotheses of this study are explained in detail below.

Hypothesis 1 - There will be a significant difference in learner performance between two groups

that receive different instructional methods.

1) Participants who receive explicit explanation on the target form will significantly

perform better in comprehension and production tests on the target L2 grammar than

those who receive the visual enhancement treatment only.

2) Participants who receive explicit explanation on the target form and also interact with

47

one of visual enhancement treatments will significantly perform better in comprehension

and production tests on the target L2 grammar than those who only receive either explicit

explanation or visual enhancement treatment.

The general consensus in the field of second language acquisition (SLA) is that attention

to the target grammatical element is necessary for learning to take place. The literature identifies

two major instructional methods to draw learners’ focal attention to the target L2 grammar:

visual enhancement and explicit explanation. The former employs more unobtrusive techniques

such as underling or highlighting the target grammar, and the implicit methods are believed to

help learners focus not only on the form, but also on the meaning of the text. On the contrary, the

latter focuses more on providing metalinguistic rule explanations about the target form and its

use. Recent studies show that the explicit attention-directing method works better than the

implicit method, and accordingly, learners who are explicitly directed to the target grammar

perform better than those who are implicitly exposed to the target form. It is because the explicit

rule presentation better primes learners for a specific grammatical form (Alanen, 1995). Research

further reveals that students who receive both implicit and explicit attention-directing treatments

perform better than those who receive either treatment only because L2 learners need not only

understand the form but also they have to integrate the form with meaning (Ellis, 1994).

Hypothesis 2 - There will be no significant differences in learner performance measured by

comprehension and production tests on the target L2 grammar between participants who interact

with an animated pedagogical agent and those who interact with a flashing arrow.

What causes learning is an instructional method, not a delivering medium (Clark, 1983,

1994a, b, 2001). Therefore, there will be no significant difference in learner performance on the

target L2 grammar among participants who receive the same instructional method that are

48

delivered through different media. The instructional methods employed in this study to focus

learner attention are explicit explanation and visual enhancement of the form. When the

instructional methods are held constant, the final product of learning, the comprehension and

production of the target grammar, will be the same regardless of what medium is used to deliver

the instruction. Therefore, the two multimedia techniques used to deliver the visual enhancement

treatment, the animated pedagogical agent and the flashing arrow, will not make any significant

differences in the final learning outcomes.

Hypothesis 3 – The more prior knowledge of the target L2 grammar learners have, the less effect

of instructional methods will be obtained on learner performance.

1) Participants with less prior knowledge on the target form will benefit from both explicit

explanation and visual enhancement more than those with more prior knowledge.

2) The benefit of explicit explanation will decrease as participants’ levels of expertise in the

target L2 grammar increase.

3) The benefit of an animated pedagogical agent and a flashing arrow will decrease as

participants’ levels of expertise in the target L2 grammar increase.

The two instructional methods employed in this study will significantly enhance learner

performance when learners have a low level of prior knowledge on the target L2 grammar.

Research shows that explicit rule explanation and visual enhancement have positive effect on L2

grammar acquisition by sensitizing learners to the target linguistic features embedded in the text.

Given that explicit explanation provides information not only about the structure of the target

grammar but also about the use of the target grammar, it will help learner build a schema for the

target grammar. The visual enhancement treatment will also have significant impact on learner

performance since it shows the relationship among relevant elements of the target grammar as

49

well as it makes the target grammar perceptually salient. However, once learners acquired the or

schema about the target grammar, the benefit of the instructional methods will decrease or

disappear. The acquired schema allows learners to identify the target grammar in the text without

the information provided by the instructional methods (Kalyuga et al., 1999). Furthermore, once

learners understood the relationship among relevant elements of the target grammar, the

guidance provided by the animated agent or the flashing arrow will not be needed.

Hypothesis 4 – There will be no causal relationship between learners’ levels of interest in

instructional program they interacted with and the level of learner performance measured by the

comprehension and production tests on the target grammar.

The underlying premise of including entertaining elements such as an animated

pedagogical agent in an instructional presentation is that the increased learner interest triggered

by entertainingness of the system will lead learners to pay more attention, to persist longer in the

system, and to exert more effort to process the information. Although numerous claims have

been made about the positive influence of animated pedagogical agents on motivation and

interest, it should be noted that increased motivation or interest does not necessarily have a cause

and effect relationship with students’ actual learning outcomes. In other words, although students

enjoy their interaction with the agent, it does not guarantee that they will learn better than those

who do not interact with an agent. For instance, a number of studies which claimed any learning

effects of animated pedagogical agents in fact provided different instructional treatments to agent

groups and non-agent groups. And as a result, it is hard to attribute the benefits exclusively to

animated pedagogical agents (Clark & Choi, 2003; Dehn & van Mulken, 2000).

Hypothesis 5 - Participants’ levels of attention directed to the target L2 grammar during the

processing of learning material will have significant effect on the level of learner performance

50

measured by the comprehension and production tests on the target grammar.

It is generally agreed that attention to a certain linguistic feature, the target of

instruction, is required for learning to take place. Although there has been a controversy on how

much and what type of attention to the target form is necessary for learning (Izumi, 2002), it is

now widely recognized that conscious, focal attention is necessary for learning, and a higher

level of subjective awareness is correlated with better learning (Rosa & O'Neill, 1999).

Following Schmidt’s Noticing Hypothesis, attention is operationally defined as ‘the availability

for verbalizing what one has experienced during or right after exposure to the input’ in the

present study.

Hypothesis 6 – The flashing arrow will require less time and mental effort from participants than

the animated pedagogical agent in achieving the same level of learning performance, when both

media are used to deliver the visual enhancement treatment.

Different media employed to deliver the same instructional method will have significant,

different effect on levels of cognitive efficiency, defined as ‘the relative amount of time and

mental effort required by different media to reach a learning criterion’ in the present study. The

underlying premise of cognitive efficiency is that a specific medium used to present instruction

may not produce different cognitive outcomes compared to another medium, but it still can have

direct impact on cognitive processes. Participants receiving the flashing arrow will need to invest

less mental effort than those interacting with an animated pedagogical agent in order to reach the

same level of learning performance. Moreover, participants interacting with the flashing arrow

will achieve the same level of learning outcome significantly faster than those interacting with an

animated pedagogical agent.

An animated pedagogical agent is seductive details in that it is active, concrete, and

51

interesting but conceptually irrelevant to learning of the target form, and consequently, it takes

away learners’ visual attention from the instruction. Even though the animated pedagogical agent

in this study is used to indicate the relationship among relevant elements, its presence and eye-

catching behaviors (i.e., gestures, lip movements, locomotion) will demand learners’ conscious

attention and impose unnecessary, extra cognitive load on working memory. On the contrary, the

flashing arrow will achieve the same effect of indicating the relationship without overloading

learners’ limited working memory since it is integrated in input text and does not require

participants to draw their attention away from the text.

Hypothesis 7 – There will be a significant positive correlation between the level of learner prior

knowledge of the target grammar and the level of cognitive efficiency.

According to Cobb (1997), learner prior knowledge or schema is one of the major

contributing factors to cognitive efficiency. The more prior knowledge learners have of the target

form, the less they will invest mental effort and time to process learning material. Schema helps

learners distinguish relevant target elements from irrelevant ones and then integrate the new

information into the existing knowledge of the target form (Kalyuga et al.,1998). Therefore,

learners who have already developed appropriate schema of the target form will not need extra

help from attention-directing techniques for processing the instruction, and will be able to avoid

spending limited cognitive resources in integrating information. Instead, more conscious effort

can be used to search for a solution. As a consequence, they will be able to learn faster and/or

easier than their counterparts who have not developed appropriate schema yet. However, the

strong presence of an animated pedagogical agent could force even experienced learners to

attend to unnecessary information provided the agent. This will probably impose extra cognitive

load on learners’ limited working memory and require unnecessary mental effort investment. As

52

a result, their performance will be damaged and the level of cognitive efficiency will decrease.

CHAPTER II: Methodology

Target English Grammar for Instruction

English Relative Clauses will be used as a target form in this study. It is reported that

English relative clauses are hard to comprehend and produce for both L1 and L2 speakers, and

instruction can facilitate acquisition of the form (Izumi, 2000).

Participants

120 Participants will be recruited from students enrolled in ESL (English as a Second

Language) programs at local universities and community colleges. The selection of participants

will be determined based on a test of English relative clauses, which will be also used as a pretest

and the indication of students’ prior domain knowledge. Following Doughty (1991) and Izumi

(2002), among participating students, those who only have a basic level of knowledge of English

relative clauses (although it is expected that there will a wide range of proficiency levels among

these students) will be selected to participate in experiments. Students who have advanced

knowledge of the target form or those who have no knowledge at all will be eliminated from the

53

present study.

Research Design

The design of the study will be a true experimental with pre- and post test involving five

treatment groups and one control group, as can be seen in Table 1. The treatment groups will be

different with regard to provision of explicit explanations (+EE) on the target form and the

provision of visual enhancement technique (+VE) to focus learners’ attention on the target form.

The participants will be randomly assigned in equal proportion to one of the six conditions:

a) Explanation plus agent (+EE +PA) group will receive explicit explanation by the agent;

b) No explanation plus agent (-EE +PA) group will be provided with the visual

enhancement treatment through the agent;

c) Explanation plus flashing (+EE +FA) group will receive explicit explanation through

human voice with the flashing arrow used to focus attention;

d) No explanation plus flashing (-EE +FA) group will not receive any explanation, only the

flashing arrow will be used;

e) Explanation plus no attention drawing method group (+EE –VE) will only get explicit

explanation;

f) No explanation plus no attention drawing method group (-EE –VE), or the control group

will not get any instructional support.

Table 1 Experimental and Control Conditions

Treatment A (Rule Presentation)

Explicit Explanation No Explicit Explanation

54

Treatment B(Visual

Enhancement)

APA +EE +PA -EE +PAFlashing Arrow +EE +FA -EE +FA

No Visual Enhancement +EE -VE -EE –VE

Measures

There are the total of five dependent variables in the present study which include mental

effort measures, time measures, interest measures, attention measures, and performance

measures. These measures will be used to investigate the research hypotheses discussed above.

In order to measure the amount of mental effort that learners invest in processing the instruction,

the secondary task method will be embedded in the learning environment which will be designed

specifically for this study. Learners’ interaction with the system will be logged online including

their entry and exit time to measure how much time is required for individual learners to acquire

the target form. After interacting with the system, participants will be asked to fill out two

questionnaires; the first one consists of questions measuring their interest in the system while the

second one inquires whether students were able to focus on the target form and its relationship

with other elements. Finally, participants will be required to take the post test to find out the

amount of their learning outcomes brought by the treatments used in the study. The post test will

be composed of questions determining learners’ levels of capability to comprehend and produce

the target form.

Data Analysis

Descriptive statistics will be used throughout the study to estimate all measures.

Additionally, Multiple Regression and MANOVA will be used to investigate the relationship

among mediating (i.e., learner prior knowledge), process (i.e., time required to acquire the form,

55

the amount of attention paid to the form), and outcome measures (i.e., scores for comprehension

and production tests). Details are given in the following about how each hypothesis will be tested

and analyzed.

Hypothesis 1 - There will be a significant difference in learner performance between two groups

that receive different instructional methods.

1) Participants who receive explicit explanation on the target form will significantly

perform better in comprehension and production tests on the target L2 grammar than

those who receive the visual enhancement treatment only.

2) Participants who receive combination of explicit explanation on the target form and

visual enhancement treatments will significantly perform better in comprehension and

production tests on the target L2 grammar than those who only receive either explicit

explanation or visual enhancement treatment.

Descriptive statistics (e.g., means, standard deviations) will be adopted to assess each

group’s posttest performance measures. In addition, 2 (Explicit explanation, no explanation) x 2

(Agent, Flashing) ANOVA will be performed to test the significance of differences among

groups.

Hypothesis 2 - There will be no significant difference in learner performance measured by

comprehension and production tests on the target L2 grammar between participants who interact

with an animated pedagogical agent and those who interact with the flashing arrow.

Descriptive statistics (e.g., means, standard deviations) will be used to estimate the

outcome measures on post tests, and T-test will be used to test the significant difference between

two groups’ scores on comprehension and production tests.

Hypothesis 3 – The more prior knowledge of the target L2 grammar learners have, the less effect

56

of instructional methods on learner performance will be obtained.

1) Participants with less prior knowledge on the target form will benefit from both explicit

explanation and visual enhancement more than those with more prior knowledge.

2) The benefit of explicit explanation will decrease as participants’ levels of expertise in the

target L2 grammar increase.

3) The benefit of an animated pedagogical agent and a flashing arrow will decrease as

participants’ levels of expertise in the target L2 grammar increase.

Descriptive statistics (e.g., means, standard deviations) will be used to estimate the

participants’ performances on the pretest and the post test, and MANOVA will be run to examine

the relationship between learner prior knowledge and various outcome measures (i.e., interest,

attention, time, and performance tests). If the MANOVA is significant, individual ANOVA will

be performed.

Hypothesis 4 – There will be no causal relationship between learners’ levels of interest in

instructional program they interacted with and the level of learner performance measured by the

comprehension and production tests on the target grammar.

Descriptive statistics (e.g., means, standard deviations) will be used to estimate the

participants’ interest levels and performances on post tests, and multiple regression analysis will

be used to examine the relationship between learners’ interest and their performances on the

posttests.

Hypothesis 5 - Participants’ levels of attention directed to the target L2 grammar during the

processing of the learning material will have significant effect on the level of learner

performance measured by the comprehension and production tests on the target grammar.

Descriptive statistics (e.g., means, standard deviations) will be used to estimate the

57

participants’ attention and performances on post tests, and multiple regression analysis will be

used to examine the relationship between learners’ attention and their performances on the

posttests.

Hypothesis 6 – The flashing arrow will require less time and mental effort from participants than

the animated pedagogical agent in achieving the same level of learning performance, when both

media are used to deliver the visual enhancement treatment.

Descriptive statistics (e.g., means, standard deviations) will be run to estimate the

measures of each group’s outcomes on the amount time and mental effort required to master the

target form. A T-test will be performed to test the significance of differences on these measures

between the two groups.

Hypothesis 7 – There will be a significant positive correlation between the level of learner prior

knowledge of the target grammar and the level of cognitive efficiency.

Descriptive statistics (e.g., means, standard deviations) will be used to estimate the

measures on the levels of learner prior knowledge and the levels of cognitive efficiency. A

Pearson product correlation will be used to assess the relationship between the learner prior

knowledge and the levels of cognitive efficiency.

58

References

Ainley, M., Hidi, S., & Berndorff, D. (2002). Interest, and the psychological processes that

mediate their relationship. Journal of Educational Psychology, 94(3), 545-561.

Alanen, R. (1995). Input enhancement and rule presentation in second language acquisition.

Attention and awareness in foreign language learning. Schmidt, R. Honolulu, University

of Hawaii Press, Tech. Rep. No. 9, pp. 259-302.

Andre, E., Rist, T., & Muller, J. (1999). Employing AI methods to control the behavior of

animated interface agents. Applied Artificial Intelligence, 13, 415-448.

Atkinson, R. K. (2002). Optimizing learning from examples using animated pedagogical agents.

Journal of Educational Psychology, 94(2), 416-427.

Baddeley, A. D. (1992). Working memory. Science, 255, 556-559.

Beentjes, J. W. J. (1989). Learning from television and books: A Dutch replication study based

on Salomon's Model. Educational Technology Research and Development, 37(2), 47-58.

Bobis, J., Sweller, J., & Cooper, M. (1993). Cognitive load effects in a primary-school geometry

task. Learning and Instruction, 3, 1-21.

Bradshaw, J. M. (1997). Software agents. Cambridge, MA, MIT Press.

59

Casali, J. G. W., Wierwille, W. W., & Cordes, R. E. (1983). A comparison of rating scale,

secondary task, physiological and primary-task workload estimation techniques in a

simulated flight task emphasizing communication load. Human Factors, 25(6), 623-641.

Cennamo, K. S. (1992). Students' perceptions of the ease of learning from computers and

interactive video: An exploratory study. Journal of Educational System, 21, 251-263.

Cennamo, K. S. (1993). Learning from video, Factors influencing learners' perceptions and

invested mental effort. Educational Technology Research and Development, 41(3), 33-

45.

Cennamo, K. S. (1996). The effect of relevance on mental effort. ERIC document reproduction

service No. ED397 783.

Chandler, P., & Sweller, J. (1991). Cognitive load theory and the format of introduction.

Cognition and Instruction, 8, 293-332.

Clark, R. E. (1983). Reconsidering research on learning from media. Review of Educational

Research, 53(4), 445-459.

Clark, R. E. (1994a). Media and method. Educational Technology Research & Development,

42(3), 7-10.

Clark, R. E. (1994b). Media will never influence learning. Educational Technology Research &

Development, 42(2), 21-29.

Clark, R. E. (1998). Cognitive efficiency research on media: A rejoinder to Cobb. An

unpublished manuscript.

Clark, R. E. (1999b). Yin and Yang Cognitive Motivational Process Operating in Multimedia

Learning Environment. In J. Van Merrienboer (Ed.), Cognition and Multimedia Design.

Herleen, Netherlands: Open University Press.

60

Clark, R. E. (2001). Learning from media: Arguments, analysis and evidence. Greenwich, CT:

Information Age Publishers.

Clark, R. E. (In press, 2003), Research on web-based learning: A half-full glass. In Bruning, R.,

Horn, C. and PytlikZillig, L. (In Press) Web-Based Learning: Where do we know? Where

do we go? Greenwich, CT: Information Age Publishers.

Clark, R. E., & Choi, S. (2003, October). Six Suggestions for the Design of Experiments on the

Effects of Animated Pedagogical Agents. Paper presented at the Annual Convention of

the Association of Educational Computer & Technology, Anaheim, CA.

Cobb, T. (1997). Cognitive Efficiency, Toward a revised theory of media. Educational

Technology Research & Development, 45(4), 21-35.

Craig, S. D., Gholson, B., & Driscoll, D. M. (2002). Animated pedagogical agents in multimedia

educational environments: Effects of agent properties, picture features and redundancy.

Journal of Educational Psychology, 94(2), 428-434.

Curran, T., & Keele, S. W. (1993). Attentional and nonattentional forms of sequence learning.

Journal of Experimental Psychology, Learning, Memory and Cognition, 19, 189-202.

Dalgarno, B. (2001). Interpretations of constructivism and consequences for computer assisted

learning. British Journal of Educational Technology, 32(2), 183-194.

DeKeyser, R. (1995). Learning second language grammar rules: An experiment with a miniature

linguistic system. Studies in Second Language Acquisition, 17(3), 379-410.

DeKeyser, R. (1998). Beyond focus on form: Cognitive perspectives on learning and practicing

second language grammar. In C. Doughty and J. Williams (Eds.), Focus on Form in

Classroom Second Language Acquisition (pp. 42-63), Cambridge, MA: Cambridge

University Press.

61

Dehn, D. M. & van Mulken, S. (2000). The impact of animated interface agents: a review of

empirical research. International Journal of Human-Computer Studies, 52, 1-22.

Doughty, C. (1991). Second language does make a difference, Evidence from an empirical study

of SL relativization. Studies in Second Language Acquisition, 13, 431-469.

Dulany, D. E. (1991). Conscious representations and thought systems. Advances in social

cognition (Vol. 4). R. Wuer, J., & T. Srull. Hillsdale, NJ, Erlbaum.

Dweck, C. S. (1989). Motivation. In A. R. G. Lesgold (Ed.), Foundations for a Psychology of

Education. Hillsdale, New Jersey: Lawrence Erlbaum Associates.

Ellis, N., Ed. (1994). Implicit and explicit learning of languages. London, Academic Press.

Erickson, T. (1997). Designing agents as if people mattered. In J. M. Bradshaw (Ed.), Software

Agents (pp. 79-96). Menlo Park, CA: MIT Press.

Fisher, S. L., & Ford, J. K. (1998). Differential effects of learner effort and goal orientation on

two learning outcomes. Personnel Psychology, 51(2), 397-420.

Flad, J. A. (2002). The effects of increasing cognitive load on self-report and dual-task measures

of mental effort during problem solving. Unpublished doctoral dissertation, University of

Southern California. Los Angeles, California.

Garner, R., Brown, R., Sanders, S., & Menke, D. (1992). Seductive details and learning from

text. In K. A. Renninger, S. Hidi, & A. Krapp (Eds.), The role of interest in learning and

development (pp. 239-254). Hillsdale, NJ: Erlbaum.

Gimino, A. E. (2000). Factors that influence students' investment of mental effort in academic

tasks: A validation and exploratory study. Unpublished doctoral dissertation, University

of Southern California. Los Angeles, California.

Harp, S. F., & Mayer, R. E. (1997). The role of interest in learning from scientific text and

62

illustrations: On the distinction between emotional interest and cognitive interest.

Journal of Educational Psychology, 89(1), 92-102.

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, 414-434.

Hidi, S., & Anderson, V. (1992). Situational interest and its impact on reading and expository

writing. In K., A. Renninger, S. Hidi, and A. Krapp (Eds.), The Role of Interest in

Learning and Development (pp. 215 – 238), Hillsdale, NJ: Lawrence Erlbaum

Associates, Publishers.

Izumi, S. (2002). Output, input enhancement, and the noticing hypothesis: An experimental study

on ESL relativization. Studies in Second Language Acquisition, 24, 541-577.

Jeung, H., Chandler, P., & Sweller, J. (1997). The role of visual indicators in dual sensory mode

instruction. Educational Psychology, 17(3), 329-343.

Johnson, W. L., Ricke. J. W., & Lester, J. C. (2000). Animated pedagogical agents, Face-to-face

interaction in interactive learning environments. International Journal of Artificial

Intelligence in Education, 11, 47-78.

Jourdenais, R., Ota, M., Stauffer, S., Boyson, B., & Doughty, C. (1995). Does textual

enhancement promote noticing? A think-aloud protocol analysis. Attention & Awareness

in foreign language learning. Schmidt, R. Honolulu, University of Hawai'i Press.

Kalyuga, S., Chandler, P., & Sweller, J. (1998). Levels of expertise and instructional design.

Human Factors, 40(1), 1-17.

Kalyuga, S., Chandler, P., & Sweller, J (1999). Managing split-attention and redundancy in

multimedia instruction. Applied Cognitive Psychology, 13, 351-371.

Kalyuga, S., Chandler, P., & Sweller, J (2000). Incorporating learner experience into the design

63

of multimedia instruction. Journal of Educational Psychology, 92(1), 126-136.

Kimmer, H., & Deek, F. (1996). Instructional technology: A tool or a panacea? Journal of

Science Education and Technology, 5(1), 87-92.

Kotovosky, K. H., J. R., & Simon, H. A. (1985). Why are some problems hard? Evidence from

Tower of Hanoi. Cognitive Psychology, 17, 248-294.

Leow, R. (1998). Toward operationalizing the process of attention in SLA: Evidence for Tomlin

and Villa's (1994) fine-grained analysis of attention. Applied Psycholinguistics, 19, 133-

159.

Lester, J. C., Converse, S. A., Kahler, S. E., Barlow, S. T., Stone, B. A., & Bhogal, R. S. (1997,

March). The persona effect, Affective impact of animated pedagogical agents.

Proceedings of Computer-Human Interaction '97 (pp. 359-366), Atlanta.

Lester, J. C., Converse, S. A., Stone, B., Khaler, S., & Barlow, T. (1997). Animated Pedagogical

Agents and Problem-Solving Effectiveness: A Large-Scale Empirical Evaluation.

Proceedings of the 8th World Conference on Artificial Intelligence in Education (pp. 23-

30), Kobe, Japan.

Lester, J. C., Voerman, J., Towns, S., & Callaway, C. (1997). Cosmo, A life-like animated

pedagogical agent with deictic believability. IJCAI '97 Workshop on Animated Interface

Agents, Making them intelligent, Nagoya, Japan.

Levin, H. M., & McEwan, P. M. (2001). Cost-effectiveness analysis: Methods and applications

(2nd ed.). Thousand oaks, CA: Sage.

Long, M. H. (1983). Does second language instruction make a difference? TESOL Quarterly,

17(3), 359-382.

Lowe, J. (2002). Computer-based education: Is it a panacea? Journal of Research on Technology

64

in Education, 34(2), 163-171.

Mayer, R. E., & Moreno, R. (1998). A Split-attention effect in multimedia learning: Evidence for

dual processing systems in working memory. Journal of Educational Psychology, 90,

312-320.

Mayer, R. E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When

presenting more materials results in less understanding. Journal of Educational

Psychology, 93(1), 187-198.

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.

Moreno, R., & Mayer, R. E. (1999). Cognitive principles of multimedia learning: The role of

modality and contiguity. Journal of Educational Psychology, 91, 358-368.

Moreno, R., & Mayer, R. E. (2000a). A coherence effect in multimedia learning: The case for

minimizing irrelevant sounds in the design of multimedia instructional messages.

Journal of Educational Psychology, 97, 117-125.

Moreno, R., & Mayer, R. E. (2000b). A learner-centered approach to multimedia explanations:

Deriving instructional design principles from cognitive theory. Interactive Multimedia

Electronic Journal of Computer-Enhanced Learning, 2(2). Retrieved April 17, 2002,

from http://imej.wfu.edu/articles/2000/2/05/index.asp.

Moreno, R., Mayer, R. E., & Lester, J. C. (2000). Life-like pedagogical agents in constructivist

multimedia environments, Cognitive consequences of their interaction. The World

Conference on Educational Multimedia, Hypermedia, and Telecommunications (ED-

MEDIA), Montreal, Canada.

65

Moreno, R., Mayer, R. E., Spires, H. A., & Lester, J. C. (2001). The case for social agency in

computer-based teaching: Do students learn more deeply when they interact with

animated pedagogical agents? Cognition and Instruction, 19(2), 177-213.

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.

Nass, C., & Steuer, J. (1993). Anthropomorphism, agency, and thopoeia: Computers as social

actors. Human Communication Research, 19(4), 504-527.

Norris, J. M., & Ortega, L. (2000). Effectiveness of L2 instruction: A research synthesis and

quantitative meta-analysis. Language Learning, 50(3), 417-528.

Reber, A. S. (1976). Implicit learning of synthetic languages: The role of instructional set.

Journal of Experimental Psychology, Human Learning and Memory, 2, 88-94.

Reber, A. S. (1989). Implicit learning and tacit knowledge. Journal of Experimental Psychology,

General, 118, 219-235.

Richards, J. C., & Rodgers, T. S. (1998). Approaches and methods in language teaching, A

description and analysis. New York, Cambridge University Press.

Reeves, B., & Nass, C. (1996). The Media Equation: How people treat computers, television,

and new media like real people and places. New York: Cambridge University Press.

Robinson, P. (1995). Review article, Attention, memory, and the noticing hypothesis. Language

Learning, 45, 283-331.

Robinson, P. (1996). Learning simple and complex second language rules under implicit,

incidental, rule search, and instructed conditions. Studies in Second Language

Acquisition, 18, 27-68.

Robinson, P. (1997a). Generalizability and automaticity of second language learning under

66

implicit, incidental, enhanced, and instructed conditions. Studies in Second Language

Acquisition, 19, 223-247.

Robinson, P. (1997b). Individual differences and the fundamental similarity of implicit and

explicit adult second language learning. Language Learning, 47, 45-99.

Rosa, E., & O'Neill, M. (1999). Explicitness, intake, and the issue of awareness. Studies in

Second Language Acquisition, 21, 511-556.

Salomon, G. (1983). The differential investment of mental effort in learning from different

sources. Educational Psychologist, 18(1), 42-50.

Salomon, G. (1984). Television is easy and print is tough: The differential investment of mental

effort in learning as a function of perceptions and attributions. Journal of Educational

Psychology, 76(4), 647-658.

Salomon, G., & Leigh, T. (1984). Predispositions about learning from print and television.

Journal of Communication, 34, 119-125.

Sampson, D., Karagiannidis, C., & Kinshuk (2002). "Personalised learning: Educationla,

technological and standardisation perspective." Interactive Educational Multimedia, 4,

24-39.

Schmidt, R. (1990). The role of consciousness in second language learning. Applied Linguistics,

11, 206-226.

Schmidt, R. (1995). Consciousness and foreign language learning: A tutorial on the role of

attention and awareness in learning. In R. Schmidt (Ed.), Attention and awareness in

foreign language learning. Honolulu, Hawaii, University of Hawaii Press.

Schmidt, R. (2001). Attention. In P. Robinson (Ed.), Cognition and second language instruction

(pp. 3-32). New York: Cambridge University Press.

67

Shiffrin, R., & Schneider, W. (1977). Controlled and automatic human information processing,

II. Perceptual learning, automatic attending, and a general theory. Psychological Review,

84, 127-190.

Spada, N. (1997). Form-focused instruction and second language acquisition: A review of

classroom and laboratory research. Language Teaching, 29, 1-15.

Sweller, J. (1999). Instructional design in technical areas. Camberwell, Australia: ACER Press.

Sweller, J., & Chandler, P. (1994). Why some material is difficult to learn. Cognition and

Instruction, 12, 185-233.

Sweller, J., Cooper, G. A., Tierney, P., & Cooper, M. (1990). Cognitive load and selective

attention as factors in the structuring of technical material. Journal of Experimental

Psychology, General, 119, 176-192.

Sweller, J., van Marrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and

instructional design. Educational Psychology Review, 10, 251-296.

Tomlin, R., & Villa, V. (1994). Attention in cognitive science and second language acquisition.

Studies in Second Language Acquisition, 16, 183-203.

van Gerven, P. W. M., Paas, F. G. W. C., & Schmidt, H. G. (2000). Cognitive load theory and the

acquisition of complex cognitive skills in the elderly: Towards an integrative framework.

Educational Gerontology, 26, 503-521.

van Mulken, S., Andre, E., & Muller, J. (1998). The person effect: How substantial is it? In H.

Johnson, L. Nigay & C. Roast (Eds.), People and Computers XIII: Proceedings of

HCI’98, pp. 53-66. Berlin: Springer.

Ward, M., & Sweller, J. (1990). Structuring effective worked examples. Cognition and

Instruction, 7, 1-39.

68

Wertsch, J., & Bivens, J. A. (1992). The social origins of individual mental functioning,

Alternatives and perspectives. The Quarterly Newsletter of the Laboratory of

Comparative Human Cognition, 14, 35-44.

Wierwille, W. W., Rahimi, M., & Casali, J. G. (1985). Evaluation of 16 measures of mental

workload using a simulated flight task emphasizing mediational activity. Human

Factors, 27, 489-502.

White, J. (1998). Getting the learners' attention: A typographical input enhancement study. In C.

Doughty and J. White (Eds.), Focus on form in classroom second language acquisition

(pp.85-113). New York, NY: Cambridge University Press.

Williams, J. (1999). Memory, attention, and inductive learning. Studies in Second Language

Acquisition, 21, 1-48.

69