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2010 CRC PhD Student Conference



   Effective Tutoring with Affective Embodied Conversational 
                             Agents 
                                                 
                                                 
                                    Sharon Moyo 
                                menziwa@hotmail.com 
 
Supervisors                Dr Paul Piwek 
                           Dr Neil Smith 
Department/Institute  Computing 
Status                     Part‐time 
Probation viva             After  
Starting date              Oct 2007 
 
This  natural  language  generation  project  aims  to  investigate  the  impact  of  affect 
expression using embodied conversational agents (ECAs) in computer‐based learning 
environments. Based on the idea that there is a link between emotions and learning, 
we  are  developing  an  affect  expression  strategy.  We  will  implement  the  strategy 
within a tutoring system in two domains: Information Technology (IT) and Business 
Studies.  
 
Current research has not firmly established the impact of affect expression strategies 
within  tutorial  feedback  which  supports  learners  in  computer‐based  learning 
environments  [1].  Our  approach  is  to  provide  affective  support  through  empathy. 
Empathy  is  described  as  expressing  emotion  that  is  based  on  another’s  situation 
(target) and not merely one’s own [2]. An individual can show: parallel empathy that 
mirrors  the  target’s  emotion;  or  reactive  empathy  that  might  be  different  to  the 
target’s emotion [2]. 
 
 The empathic tutor interventions will be designed to support positive emotions [3] 
and reduce negative learner emotions [4] using a range of verbal and non‐verbal (or 
multimodal) interventions. These interventions will be combined with corrective and 
meta‐cognitive feedback [5] and presented to users as a hint or summary.  
 
We  will  conduct  a  series  of  studies.  Initially,  we  intend  to  develop  implement  and 
evaluate an algorithm that generates multimodal empathic behaviours using an ECA. 
The  experiment  conditions  will  include  multimodal  channels  of  communication: 
speech  vs.  speech  and  facial  expression  vs.  speech  and  gesture  vs.  speech,  facial 
expression  and  gesture.  We  hypothesize  that  participants  will  identify  the  ECA’s 
expression  most  accurately  in  the  condition  using  three  channels  to  generate 
affective expressions in comparison to the other conditions. 
 
 
 
 
 


                                         Page 58 of 125
2010 CRC PhD Student Conference



 
Additionally we aim to evaluate when and how parallel or reactive empathy can be 
used  to  best  effect  in  learning  environments.  Subsequently,  we  will  integrate  the 
algorithm into a web‐based tutoring environment and conduct an evaluation in the 
domain of Business Studies. Finally, in the main study we will evaluate the empathic 
tutoring  system  in  a  classroom  setting  over  several  weeks  in  the  domain  of 
Information Technology (IT).  
 
We  intend  to  contribute  to  current  research  by  describing  how  an  ECA  can 
effectively  express  multimodal  [6]  empathic  behaviour  within  computer‐based 
learning.  More  specifically,  we  aim  to  create  a  framework  to  model  parallel  and 
reactive empathy and the learning contexts where they can be used in a quiz‐based 
web  environment.  We  intend  to  validate  these  results  through  evaluations  across 
two  domains:  Information  Technology  and  Business  demonstrating  that  the 
framework can be applied to other quiz‐based learning environments.  
 
References: 
 
1.      Arroyo,  I.,  et  al.  Designing  Affective  Support  to  Foster  Learning,  Motivation 
        and Attribution. in AIED 2009. 2009. Brighton, UK: IOS. 

2.      Davis,  M.,  Empathy:  A  Social  Psychological  Approach.  1994,  Madison,  WI: 
        Brown and Benchmark. 

3.      Bickmore, T. and D. Schulman, Practical approaches to comforting users with 
        relational  agents,  in  CHI  '07  extended  abstracts  on  Human  factors  in 
        computing systems. 2007, ACM: San Jose, CA, USA. 

4.      Burleson,  W.,  Affective  learning  companions:  Strategies  for  empathetic 
        agents  with  real‐time  multimodal  affective  sensing  to  foster  meta‐cognitive 
        and  meta‐affective  approaches  to  learning,  motivation,  and  perseverance.  . 
        2006, Massachusetts Institute of Technology: Cambridge, MA. 

5.      Tan,  J.  and  G.  Biswas.  The  Role  of  Feedback  in  Preparation  for  Future 
        Learning:  A  Case  Study  in  Learning  by  Teaching  Environments.  in  ITS  2006. 
        2006: Springer‐Verlag. 

6.      Cassell,  J.,  et  al.  Animated  Conversation:  Rule‐Based  Generation  of  Facial 
        Expression,  Gesture  and  Spoken  Intonation  for  Multiple  Conversational 
        Agents. . in Siggraph 94, ACM SIGGRAPH. 1994: Addison Wesley. 
 
 




                                         Page 59 of 125

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Moyo

  • 1. 2010 CRC PhD Student Conference Effective Tutoring with Affective Embodied Conversational  Agents      Sharon Moyo  menziwa@hotmail.com    Supervisors  Dr Paul Piwek  Dr Neil Smith  Department/Institute  Computing  Status  Part‐time  Probation viva   After   Starting date  Oct 2007    This  natural  language  generation  project  aims  to  investigate  the  impact  of  affect  expression using embodied conversational agents (ECAs) in computer‐based learning  environments. Based on the idea that there is a link between emotions and learning,  we  are  developing  an  affect  expression  strategy.  We  will  implement  the  strategy  within a tutoring system in two domains: Information Technology (IT) and Business  Studies.     Current research has not firmly established the impact of affect expression strategies  within  tutorial  feedback  which  supports  learners  in  computer‐based  learning  environments  [1].  Our  approach  is  to  provide  affective  support  through  empathy.  Empathy  is  described  as  expressing  emotion  that  is  based  on  another’s  situation  (target) and not merely one’s own [2]. An individual can show: parallel empathy that  mirrors  the  target’s  emotion;  or  reactive  empathy  that  might  be  different  to  the  target’s emotion [2].     The empathic tutor interventions will be designed to support positive emotions [3]  and reduce negative learner emotions [4] using a range of verbal and non‐verbal (or  multimodal) interventions. These interventions will be combined with corrective and  meta‐cognitive feedback [5] and presented to users as a hint or summary.     We  will  conduct  a  series  of  studies.  Initially,  we  intend  to  develop  implement  and  evaluate an algorithm that generates multimodal empathic behaviours using an ECA.  The  experiment  conditions  will  include  multimodal  channels  of  communication:  speech  vs.  speech  and  facial  expression  vs.  speech  and  gesture  vs.  speech,  facial  expression  and  gesture.  We  hypothesize  that  participants  will  identify  the  ECA’s  expression  most  accurately  in  the  condition  using  three  channels  to  generate  affective expressions in comparison to the other conditions.            Page 58 of 125
  • 2. 2010 CRC PhD Student Conference   Additionally we aim to evaluate when and how parallel or reactive empathy can be  used  to  best  effect  in  learning  environments.  Subsequently,  we  will  integrate  the  algorithm into a web‐based tutoring environment and conduct an evaluation in the  domain of Business Studies. Finally, in the main study we will evaluate the empathic  tutoring  system  in  a  classroom  setting  over  several  weeks  in  the  domain  of  Information Technology (IT).     We  intend  to  contribute  to  current  research  by  describing  how  an  ECA  can  effectively  express  multimodal  [6]  empathic  behaviour  within  computer‐based  learning.  More  specifically,  we  aim  to  create  a  framework  to  model  parallel  and  reactive empathy and the learning contexts where they can be used in a quiz‐based  web  environment.  We  intend  to  validate  these  results  through  evaluations  across  two  domains:  Information  Technology  and  Business  demonstrating  that  the  framework can be applied to other quiz‐based learning environments.     References:    1.  Arroyo,  I.,  et  al.  Designing  Affective  Support  to  Foster  Learning,  Motivation  and Attribution. in AIED 2009. 2009. Brighton, UK: IOS.  2.  Davis,  M.,  Empathy:  A  Social  Psychological  Approach.  1994,  Madison,  WI:  Brown and Benchmark.  3.  Bickmore, T. and D. Schulman, Practical approaches to comforting users with  relational  agents,  in  CHI  '07  extended  abstracts  on  Human  factors  in  computing systems. 2007, ACM: San Jose, CA, USA.  4.  Burleson,  W.,  Affective  learning  companions:  Strategies  for  empathetic  agents  with  real‐time  multimodal  affective  sensing  to  foster  meta‐cognitive  and  meta‐affective  approaches  to  learning,  motivation,  and  perseverance.  .  2006, Massachusetts Institute of Technology: Cambridge, MA.  5.  Tan,  J.  and  G.  Biswas.  The  Role  of  Feedback  in  Preparation  for  Future  Learning:  A  Case  Study  in  Learning  by  Teaching  Environments.  in  ITS  2006.  2006: Springer‐Verlag.  6.  Cassell,  J.,  et  al.  Animated  Conversation:  Rule‐Based  Generation  of  Facial  Expression,  Gesture  and  Spoken  Intonation  for  Multiple  Conversational  Agents. . in Siggraph 94, ACM SIGGRAPH. 1994: Addison Wesley.      Page 59 of 125