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
Copyright
c
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 ABCDFEGH), each might
represent the mental state of a learner at a particular
time, t, during learning.
++
t
dttatltm
0
)]()()([
D
C
Assessment
a(t)
Lesson,
l(t)
Motivation m(t)
C
B
E
H
F
G
B
D
A
G
H
F
E
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
1
: Affective Role Mode
l of Software Agent
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452
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
Lesson
Motivation
Assessment
Motivation
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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