AN AFFECTIVE ROLE MODEL OF SOFTWARE AGENT FOR

EFFECTIVE AGENT-BASED E-LEARNING BY INTERPLA

YING

BETWEEN EMOTIONS AND LEARNING

Shaikh Mostafa Al Masum, Mitsuru Ishizuka

Department of Information and Communication Engineering

Graduate School of Information Science and Technology, University of Tokyo

7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

Keywords: Affective Model for e-Learning, Emotion and e-Learning, Agent-based learning, Affective Computing, E-

learning, Emotion Dynamics, Affective Pedagogy

Abstract: E-learning could become the major form of training and development in organizations as technologies will

improve to create a fully interactive and humanized learning environment (Tim L. Wentling, et al, 2000).

Hence to recognize this objective this paper would like to explain about an affective role model of a

software agent to facilitate interactive online learning by considering and incorporating emotional features

associated to learning with a view to strengthening the expectation of Lister (Lister, et al, 1999) that the

differences between F-to-F and purely web-based courses are rapidly disappearing. The paper first presents

the relationships between emotion and learning from different literatures and surveys. Then an affective

model for e-learning is explored. After the model the paper enlists the emotion dynamics underpinned by a

software agent. The paper concludes with the notion of future and extension of further research.

1 INTRODUCTION

With the augmentation and evolution of web

technologies, e-learning is gaining popularity day by

day. According to Michael (Michael M, 2002) web-

based courses, are characterized by a predominance

of asynchronous activities that replace the typical

face-to-face (F-to-F) classroom interaction between

the students, instructor and content. The differences

between F-to-F and purely web-based courses are

rapidly disappearing (Lister, et al, 1999) and this

spawned yet another term called “blended learning”

(Michael M, 2002). Hence we are very optimistic

towards this blended nature of learning with the

momentum and popularization of broadband internet

facilities and effective conglomeration of

Information Technology and human psychology.

The following statistical information gives raise to

this hope, at least.

• The e-learning industry is still booming and is

expected to grow from $6.3 billion in 2001 to

more than $23 billion in 2004 (International

Data Corporation).

• At the end of April 2001, the MIT announced

that, within a 10 year program, its almost 2000

courses will be put on-line, available for free

access to everybody (Virginio, et al, 2004)

• 36% of US Universities have been considering

or offering online courses and 14% have had a

declared policy rewarding instructors for online

course development. (Campus Computing

Project, 2002).

• According to (Hall, 2000 and Tom, 2000) today,

organizations are in synch with and using

content providers, authoring tools, training

management systems, portals, delivery systems

and integrated solutions to foster their e-

Learning endeavours.

In the future, the term e-Learning may be obsolete

because technology will appear invisible to both the

learner and instructor because the technology-rich

environment will fill the gap of the necessity of real

class-room environment. Hence research on

manifold issues is needed. One of the scopes could

be that e-Learning technologies will allow for a

humanized learning environment (Virginio, et al,

2004) and so this paper tends to characterize such

humanized agent.

449

Mostafa Al Masum S. and Ishizuka M. (2005).

AN AFFECTIVE ROLE MODEL OF SOFTWARE AGENT FOR EFFECTIVE AGENT-BASED E-LEARNING BY INTERPLAYING BETWEEN EMOTIONS

AND LEARNING.

In Proceedings of the First International Conference on Web Information Systems and Technologies, pages 449-456

DOI: 10.5220/0001229104490456

 SciTePress

2 LEARNING AND EMOTION:

ARE THEY INTERTWINED?

There has been little exploration of the extent

whether emotion is associated with learning online.

Martin and Briggs (Martin, et al, 1986) almost

twenty years ago now, proposed the integration of

the two domains, affective and cognitive, into a

more holistic and realistic framework for

instructional design. The proposal was not

popularized for the continuing separation of emotion

and cognition with the difficulty in defining

perspectives and a multitude of definitions of

emotions (Frijda, 1994, James, 1952, Shweder,

1994).

In this context let’s figure out the definitions

of emotion, affect, and cognition.

Emotion can be regarded as some combination

(with various emphases and sequences) of

physiological, psychological and psychomotor

components. James (James, 1952) was an early

promoter of this general approach, defining emotion

in terms of the feeling of the ‘bodily expressions’

which follow the perception of an ‘exciting fact’.

Other variations identify ‘affective’ and ‘somatic’

dimensions of emotion (Shweder, 1994),

‘experiential, behavioral and physiological’ aspects

(Frijda, 1994) or ‘corporeal’ and ‘cognitive’

dimensions (Worthman, 1999).

However, a clear,

agreed upon definition seems to be not easily

arrived. As LeDoux (LeDoux, 1999 p.23) said

“everyone knows what [emotion] is until they are

asked to define it.” To the question what are

emotions, LeDoux responds “there are many

answers and many of these are surprisingly unclear

and ill-defined”. The picture of emotions that

emerges is diverse and multifaceted. This

complexity makes the task of exploring the

relationship between emotions and learning a

difficult one, even though several attempts are

obvious in (Kerry, 2003, LeDoux, 1999). Kerry

conducted a survey with the online course- taking

students. All the participants spoke of a range of

emotions both positive and negative which had been

associated with, and had impacted on, their learning.

Affect is influenced by or resulting from the

emotions (Margaret, 1999). Affective includes

aspects such as passion, frustration, satisfaction,

distress, joy, fulfilment, gratitude, comfort,

arrogance, or disinterest arguments that create a

result.

Cognition can be defined as the mental process

of knowing or acquiring to know. Cognition

describes how people become aware of, gain,

manage, and build new knowledge about the world.

According to Margaret, a more pragmatic,

comprehensive view of learning considers the

differing influence and complex relationships

between conative, affective, cognitive, social, and

other relevant learning-related factors recognize

dominant psychological factors, other than just

cognitive aspects, that influence learning. McLeod’s

(McLeod, 1994) review of research into emotion and

learning in mathematics identifies separate cognitive

and affective domains. Shelton (Shelton, 2000), too,

writing of the importance of emotion in learning

addresses the need to develop certain ‘emotional

competencies’ before learning can proceed

satisfactorily. Similarly, Postle (Postle, 1993)

describes of the importance of ‘emotional

competence’ in relation to learning. In his terms,

learning can be inhibited by emotional

incompetence. So, with this approach, emotion is

relevant to learning in that it provides a base or

substrate out of which healthy cognitive functioning

can occur. This has led to a growing awareness and

researchers have started to admit that emotion and

learning have inextricable juxtapositions. “Cognition

is not as logical as it was once thought and emotions

are not as illogical” (LeDoux, 1999). Stock (Stock,

1996) acknowledges that ‘all sensory input is

processed through our emotional centre first before

it is sent to be processed in our rational mind’. The

centrality of emotion in many cognitive processes is

now being acknowledged.

As Goleman (Goleman, 1995) puts that the

extent to which emotional upsets can interfere with

mental life is no news to teachers. Students who are

anxious, angry, or depressed don't learn; people who

are caught in these states do not take in information

efficiently or deal with it well." (Goleman, pp. 78).

It is therefore imperative that the interaction with the

‘‘machine’’ be as ‘‘un-traumatic’’ as possible. In e-

learning, ignoring the emotional factors that come

into play may lead to total failure (Virginio, 2004).

The impact of emotions and intentions on learning,

in the real world, are an integral part of learning and

cannot be separated from learning and thinking

ability, that is, we cannot consider one without

considering the other (Margaret, 1999).

3 AN AFFECTIVE MODEL FOR E-

LEARNING

The affective role involves the personal motivation

and satisfaction of the learner. Affective behavior

has a direct positive impact on cognitive learning

(Duchastel, 1993). Lepper and Chabay (Lepper, et

al, 1998) note that "motivational components of

tutoring strategies are as important as cognitive

components, and more generally, that truly

personalized instruction must be individualized

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450

along motivational as well as cognitive dimensions"

(p. 243). Duchastel similarly stresses the need for

the affective role in the discussion of study guides

for correspondence of courses. A number of roles

played by instructors are identified by Coppola, et

al, (Coppola, et al, 2001). According to them three

particularly crucial ones are the cognitive, affective,

and managerial roles. The cognitive role determines

the actual interplay of learning/teaching. How we

present content, provide interaction, and reinforce

learning is the subject of this role. The affective role

involves motivation and satisfaction.

Hence the useage of intelligent software agents,

to simulate learning environments for teaching and

learning process, can provide a way to function and

simulate the “human” aspect of instruction in a more

cost-effectively and interactive way than other

controlled computer-based methods. Kort, et al,

(Kort, et al, 2001) thus assume that computers will,

much sooner than later, be more capable of

recognizing human behaviors that lead to strong

inferences about affective state of learner. Proposed

model of Kort, et al, describes the range of various

emotional states during learning. In this paper we

depict our own model of affective behavior for e-

learning environment, which we define as Cubic

Emotional Model (CEM) of e-learners.

We define e-learning as a synergetic action of

Motivation (m), Lesson (l) and Assessment (a).

These three also need some clarification. According

to (Williams, 1997) we also admit motivation is a

state of cognitive and emotional arousal which leads

to a conscious decision to act, and gives rise to a

period of sustained intellectual and/or physical state.

Lesson means the real educational contents or the

topic/subject matter to teach or to be taught.

Assessment is actually the evaluation. The

evaluation may be of pre-impression and/or ongoing

impression and/or post-impression of the system of

learning process (e.g. e-learning). Assessment

actually answers the questions like “how about

learning online?” or “Am I enjoying the lesson?” or

“How was it?” or “Did I do well?” Form the

literature (Angel, et al, 1998)(del Soldato,

1994),(Driscroll, 2000), (Edward, 1910) of

educational psychology we can build the following

dependencies.

• Motivation is proportional to Learning.

• Motivation is proportional to Assessment.

So we can conclude motivation is directly

proportional to both learning and assessment. Hence

we represent E-learners’ Emotional Behavior (EEB)

as the integration of motivation, lesson and

assessment with respect to time spent with the

computer for the lesson.

EEB= ……….(1)

3.1 Cubic Emotional Model (CEM) of

Online Learners

The following CEM (Figure 1) represents 3-

dimensional state-model of emotional states of a

student. We define eight emotional states at the very

corners of the cube and at a particular time a student

remain at a particular point in the 3-dimensional

space of the cube which indicates a certain value of

contentment (e.g. H′). As human mental states are

complex one in nature, we cannot strictly describe

the dynamics of mental states by simple equations.

Student’s motivation depends on his/her biological,

environmental factors and so assessment, pace and

fruitfulness of lesson varies differently over time.

Thus we can imagine almost uncountable numbers

of cubes (e.g. cube AB′C′D′F′E′G′H′), each might

represent the mental state of a learner at a particular

time, t, during learning.

∫

dttatltm

)]()()([

′

Assessment

a(t)

Lesson,

l(t)

Motivation m(t)

′

Eight CEM Point

A: Blank

B: Motivated

C: Curious

D: Hesitated

E: Disappointed

F: Failed

G: Anxious

H: Contented

Figure 1: Cubic Emotional Model (CEM

)

AN AFFECTIVE ROLE MODEL OF SOFTWARE AGENT FOR EFFECTIVE AGENT-BASED E-LEARNING BY

INTERPLAYING BETWEEN EMOTIONS AND LEARNING

451

So it will not be high-sounding if we say that at a

particular time a student’s emotional states varies

between “Blank” to “Contented” states or in other

way, literarily in a specific time, a student is certain

amount of motivated or hesitated or disappointed but

at the same time he might be anxious or curious or

failed (bored), while all these give him a certain

amount of contentment. Obviously this fuzziness of

emotional state is needed to be simplified or

regularized for a computer program (software agent)

to behave accordingly. Such simplification in terms

of emotion transitional rules will be jotted down in

the next section.

The following table (table 1) is necessary to get a

clear idea of the Eight CEM points. It indicates the

eight emotional states along with corresponding

emotional traits.

3.2 Emotional States of Software

Agent

To correspond affectively with different affective

states of e-learners we also figure out different roles,

action and emotional characteristics of our software

agent. Table 2 enlists the agent’s affective role

model. The ‘Role’ indicates the personality of the

agent; Action indicates the basic tasks that the agent

needs to perform or ponder to elicit the

corresponding ‘Characteristics’.

3.3 Transition of Emotional State of e-

learners and Behavior Semantic of

Software Agent

Koda & Maes (Koda, et al, 1996) suggest that more

expressive agents have greater motivational impact.

However, Dietz & Lang (Dietz, et al, 1999) found

that while users preferred agents showing more

emotion and performed better on a memorization

task with the emotion-showing agents, the results

were not statistically significant. One of the first

suggestions of endowing computer tutors with a

degree of empathy was made by Lepper and Chabay

(Lepper, et al, 1998). They argued that motivational

components are as important as cognitive

components in tutoring strategies and those

important benefits would arise from considering

techniques to create computer tutors that have an

ability to empathize. So we formulate Emotion State

Transition (EST) of the software agent for the

expected behavior of the agent towards the student

with appropriate affects.

Following we mention role and action pair of

software agent which we call the expected behavior

of the agent corresponding to learns’ emotional

state. The main goal of the agent’s interaction is to

provide affects to lead the learners’ emotional state

to ‘Contented’ state.

Emotional

Sate

Emotional Traits

Blank Desire, Uncertainty, Hope,

Imagination, Dull

Motivated Interest, Comfort, Motivation,

Approaching, Encouragement

Curious Thrill, Trusting, Anticipatory,

Expecting, Curiosity

Hesitated Discomfort, Confusion,

Dissatisfaction, Hesitation

Disappointed Shame, Embarrassment,

Pessimism, Worry,

Disappointment, Anger

Failed Boredom, Tired, Exhausted,

Inattentive, Inactive, Drowsy,

Sad, Disgust

Anxious Fear, Anxiety, Enthusiasm,

Excitement

Contented Pride, Confidence, Calm,

Satisfaction, lively, Happy,

Contentment

Table 2: Affective Role Model of Software Agent

Software Agent’s Affective Role Model

e-learners’ CEM

Points

Role Action Characteristics

Blank Promoter Promotion Friendly, Elaborative, Hopeful, Welcoming

Motivated Parental Stimulation Encouraging, Wishful, Approaching

Curious Pedagogue Lesson/Teach Informative, Pedagogy, Loving, Polite, Calm,

Rationale

Hesitated Advisor/Counselor Motivation Cheering, Confident, Enthusiastic, Caring

Disappointed Buddy Inspiration Amicable, Cordial, Inspiring, Optimistic,

Approving, Agreeable

Failed Entertainer Amusement Energetic, Flexible, Jocund, Funny, Animated

Anxious Coordinator Explanation Eloquent, Skillful, Agile, Helpful, Expressive

Contented Admirer Praise Excited, Proud, Happy, Suggestive, Satisfied

Table

: Affective Role Mode

l of Software Agent

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EST Rule 1: Anxious Contented

{Coordinator, Explanation} {Admirer, Praise}

EST Rule 2: Failed Contented

{Entertainer, Amusement} {Admirer, Praise}

EST Rule 3: Curious Contented

{Pedagogue, Teach} {Admirer, Praise}

EST Rule 4:

Blank

At the end of each state in EST Rule 4, any of the

rules from 1 to 3 applies to lead a student

“Contented” state.

For example, a learner’s emotional state is blank at

the outset, at that time the software agent behaves as

a promoter to promote the course by friendly and

welcoming attitude. Then the learner might feel

‘Motivated’ or ‘Hesitated’ or ‘Disappointed’ and

depending upon the emotional state of the learner

the agent will behave the student with corresponding

affects. If the student is Motivated (for example) the

agent will have parental behaviors to stimulate the

learner for lesson or to develop an assessment. If the

student is motivated to take lesson the agent then

teaches the lesson with pedant like affect and during

the lesson the learner might be anxious for the

progress or any other reasons, then the agent takes

the role of coordinator to explain the subject matter

to grow positive assessment towards the student. If

the student seems to be tired or inattentive or doing

badly in test, indicating this as failure, the agent will

try to provide amusement in terms of funny

animation or story unfolding (depending on the type

of student) to cheer up the student. So whatever the

emotional state a learner is in, at a particular time,

the agent has its corresponding role to play and

behave to lead the student to ‘Contented’ state. But

the sensing of the learner’s emotional state or

emotion (could be identified by physiological and

other phenomena) is not in the scope of this paper.

In fact there are lots of research is been done and

still being done to sense human affects by capturing

and processing various signals of human body (e.g.

ECG, BP, RP, Skin Conductance etc.)

4 BEHAVIORAL DYNAMICS OF

SOFTWARE AGENT

Before designing the behavioral dynamics of the

agent we would like to describe the emotionality or

affective characteristics of the software agent by

several functional outputs. We imagine that our

agent, for this time being at least, has the following

emotional functions that provide some values

(Prendinger,2001) as a functional output to render its

personality with respect to different roles (e.g.

counselor etc.)

a. Friendliness: This emotion value indicates how

much friendly the agent will be. The parameters

considered to define friendliness are:

Companionability, Amity, Benevolence,

Cordiality, Kindness and Agreeableness. So

Lesson

Motivation

Positive Assessment

{Promoter, Promote}

Anxious

{Coordinator, Explanation}

Anxious

{Coordinator, Explanation}

Failed

{Entertainer, Amuse

ment}

Failed

{Entertainer, Amusement}

Curious

{Pedagogue, Teach}

Assessment

Lesson

Motivation

Assessment

Motivation

Assessment

Lesson

Motivated

{Parental, Stimulation}

Hesitated

{Counselor, Motivation}

Disappointed

{Buddy, Inspiration}

Curious

{Pedagogue, Teach}

AN AFFECTIVE ROLE MODEL OF SOFTWARE AGENT FOR EFFECTIVE AGENT-BASED E-LEARNING BY

INTERPLAYING BETWEEN EMOTIONS AND LEARNING

453

what does Friendliness (Agent, Student) mean?

To us this indicates a functional value that gives

a measure indicating the friendliness factor of

the agent. While designing the function the

aforementioned parameters are used and values

of parameters get assigned according to

Student’s profile (e.g. demography, culture

etc.). For example like in (Prendinger,2001) we

assume intensities of friendliness, f ∈{1,…..,

5}, which indicates the fuzzy values like not

friendly (Strict), friendly but less cordial,

friendly and cordial, friendly but less agreeable,

friendly and agreeable(flexible) respectively.

b. Expressiveness: This emotion value indicates

how much explainable the agent will be. The

parameters considered are: Explanatory, Witty,

Eloquence, Distinct, Articulateness and

Coherent.

c. Encouragement: This emotion value indicates

how much encourage-worthy the agent will be.

The parameters considered are: Help, Support,

Assistance, Inspiration

d. Parental: This emotion value indicates how

much parents like the agent will act. The

parameters considered are: Motherly, Fatherly,

Sympathetic, Kindness, Strict

e. Optimism: This emotion value indicates how

much optimistic the agent will be appeared. The

parameters considered are: Calmness, Hopeful,

Sureness, Positivism

f. Pedagogy: This emotion value indicates how

much teacher-like the agent will act. The

parameters considered are: Enlightened,

Knowledgeable, Informative, Scientific,

Learning

g. Loving: This emotion value indicates how much

passionate the agent will express himself. The

parameters considered are: Adoring,

Affectionate, Committed, Attached, Anxious

h. Politeness: This emotion value indicates how

polite the agent will be in terms of etiquette.

The parameters considered are: Courteous,

Mannerly, Cordial, Flattering, Respectful

i. Rationale: This emotion value indicates how

much logical and analytical the agent will be in

terms of Speech and Action. The parameters

considered are: Logical, Reasonable,

Inquisitive, Induced

j. Confidence: This emotion value indicates the

level of confidence of the agent. The parameters

considered are: Trust, Faith, Self-Assurance and

Impulsiveness.

k. Animated: This emotion value indicates how

much excitement the agent will render. The

parameters considered are: Lively, Gall, Active,

Cheerful and Brisk.

l. Funny: This emotion value indicates how much

funny the agent will act. The parameters

considered are: Humorous, Comic, Laughable

and Whimsical.

m. Agility: This emotion value indicates how much

prompt and alert the agent will be. The

parameters considered are: Alertness,

Cleverness, Promptitude and Quickness.

n. Happiness: This emotion value indicates how

much happiness the agent might express. The

parameters considered are: Satisfaction, Cheer,

Enthusiasm and Contentment.

o. Recommending: This emotion value indicates

how much suggestive or recommending the

agent will be. The parameters considered are:

Suggesting, Advising, Complementing and

Admonishing.

This approach has the advantage of being relatively

simple to implement, but since most of the

emotional message seems to be non-verbal, it may

prove to be too restricted for diagnosing student's

motivation and it may be very difficult to elicit

motivation diagnosis knowledge for this type of

‘communication channel’. del Soldato performed a

preliminary study to test the accuracy of her

motivational modeler, but no conclusive results were

obtained. The detail design of aforementioned

functions of the above affective characteristics of the

agent is not kept in the scope of the paper.

Following we would like to enlist the Behavioral

Dynamics (BD) of our agent using the above

functional affective characteristics.

<Role, Action>: {Promoter, Promotion}

BD1(A,S) = Friendliness (A, S) ∧ Elaboration (T, S)

…………….. ........................................................ (2)

<Role, Action>: {Parental, Stimulation}

BD2(A,S) = Encouragement (A) ∧ Parental (T, S) ∧

Optimism (A, S) …………...………….….……. (3)

<Role, Action>: {Pedagogue, Teach}

BD3(A,S) = Elaboration (T, S) ∧ Pedagogy (A, S) ∧

Loving (A, S) ∧ Politeness (A, S) ∧ Rationale (A, S)

….……......……………………..….….…..….…. (4)

<Role, Action>: {Advisor/Counselor, Motivation}

BD4(A,S) = Encouragement (A) ∧ Confidence (A,

T) ∧ Loving (A, S) ∧ Politeness (A, S) ∧ Rationale

(A, S) …………………………..….….…..….…. (5)

<Role, Action>: {Buddy, Inspiration}

BD5 (A, S) = Friendliness (A, S) ∧ Encouragement

(A) ∧ Optimism (A, S) ∧ Loving (A, S)………... (6)

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<Role, Action>: {Entertainer, Amusement}

BD6 (A, S) = Friendliness (A, S) ∧ Animated (A, S)

∧ Funny (A, S)…………….……………………..(7)

<Role, Action>: {Coordinator, Explanation}

BD7 (A, S) = Elaboration (T, S) ∧ Friendliness (A,

S) ∧ Agility (A) ∧ Pedagogy (A, S)..…………….(8)

<Role, Action>: {Admirer, Praise}

BD8 (A, S) = Animated (A, S) ∧ Happiness (A) ∧

Recommending (A) ∧ Optimism (A, S)………….(9)

For example the output of relation 1 may

characterize the agent, with respect to Student (S)

the Agent (A), needs to be friendly but less

agreeable and Highly Elaborative for the Topic T

and at the same time the role and action of the agent

for relation 1 make the agent obliged to behave

hopeful and welcoming (by uttering some pre-stored

natural text, for example).

5 CONCLUSION

For enhancing quality and improving accessibility to

education and training the use of e-learning is

generally seen as one of the keystones for building

the knowledge society, In Europe, in particular. It is

indubitable that for an effective learning experience

the learners are required to think deeply, as shallow

thinking may lead only to shallow learning

(Duchastel, 2004). Because e-learning has an

instructor, students, and a computer, LaRose and

Whiten (LaRose, et al, 2000) proposed that

instructional immediacy in this context is comprised

of three corresponding variables: computer

immediacy, student immediacy, and teacher

immediacy. Computer immediacy refers to the

closeness that develops between learner and

computer in the course of e-learning; the need for

computer immediacy is also supported by Reeves

and Nass (Reeves, et al, 1996), who advocate giving

media personality by incorporating the same

conventions of etiquette, which characterize human

conversation. Student immediacy describes the

behaviors that create a feeling of closeness between

learners in an educational setting. Finally, teacher

immediacy refers to “teaching behaviors that

enhance closeness to and nonverbal interaction with

another” (LaRose, 2000). The efforts described here

focus on all the three immediacies. In the future

work we would like to build the agent underpinned

the aforementioned behavioral dynamics and

program the agent by Multimodal Presentation

Markup Language (MPML) (Tsutsui, et al, 2000)

that supports affective tagging of agent. The

proposed agent will express affect according to

behavioral dynamics and render its action

corresponding to roles by uttering different patterns

of pre-stored texts and animation to communicate

positive affect to the learners.

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