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Affective Computing and Intelligent Interaction. Ma. Mercedes T. Rodrigo Ateneo Laboratory for the Learning Sciences Department of Information Systems and Computer Science Ateneo de Manila Univeristy. Affective computing.

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affective computing and intelligent interaction

Affective Computing and Intelligent Interaction

Ma. Mercedes T. Rodrigo

Ateneo Laboratory for the Learning Sciences

Department of Information Systems and Computer Science

Ateneo de Manila Univeristy

affective computing
Affective computing

Computing that relates to, arises from or deliberately influences emotion

- Picard, 1997, Affective Computing

affective computing1
Affective computing
  • Emotion recognition
  • Emotion expression
  • Intelligent response to emotion
significance towards more humane interfaces
Significance: Towards more humane interfaces

How can we enable computers to better serve people’s needs--adapting to you, vs. treating you like some fictionalized ideal user, and recognizing that humans are powerfully influenced by emotion, even when they are not showing any emotion?

- Picard, 2003

slide5

Aplusix, Scatterplot, Ecolab, BlueJ, etc.

Observation Logs

Student Interaction Logs

Sensor data

  • Interventions
  • Intelligent agents
  • Improved error messages
  • Analysis
  • Affect detectors
  • Behavior detectors
  • Novice programmer errors
slide6

Aplusix, Scatterplot, Ecolab, BlueJ, etc.

Observation Logs

Student Interaction Logs

Sensor data

  • Interventions
  • Intelligent agents
  • Improved error messages
  • Analysis
  • Affect detectors
  • Behavior detectors
  • Novice programmer errors
slide15

Aplusix, Scatterplot, Ecolab, BlueJ, etc.

Observation Logs

Student Interaction Logs

Sensor data

  • Interventions
  • Intelligent agents
  • Improved error messages
  • Analysis
  • Affect detectors
  • Behavior detectors
  • Novice programmer errors
sample log brainfingers
Sample Log: Brainfingers

[Header V2035]

7/31/2009 7:49:05 PM

UserFile = C:\Nia Data\\__20090731194905.usr

[Data]

Sample,Event,GlanceMagJs,GlanceDirJs,A1Js,A2Js,A3Js,B1Js,B2Js,B3Js,MuscleJs

1,0,0.1,0.065,0.0195,0.20775,-1.165867E-07,0.5815,0.5311,0.6048,0.6782,0.7665,0.7074,0

1,0,-0.029,-0.122,0.021,0.2056,-1.165867E-07,0.5774,0.5251,0.5892,0.6723,0.7598,0.7125,0

1,0,0.015,-0.167,0.0205,0.203625,-1.165867E-07,0.5743,0.5187,0.5737,0.6683,0.7534,0.7157,0

1,0,-0.0595,-0.1555,0.0205,0.20165,-1.165867E-07,0.573,0.5118,0.5596,0.6656,0.7468,0.7159,0

1,0,-0.285,-0.163,0.0205,0.199625,-1.165867E-07,0.5733,0.5043,0.5477,0.6622,0.7398,0.7168,0

1,0,-0.3665,-0.206,0.022,0.197625,-1.165867E-07,0.5745,0.4966,0.5371,0.6567,0.7321,0.7221,0

1,0,-0.125,-0.158,0.0225,0.195675,-1.165867E-07,0.5772,0.4885,0.5268,0.6483,0.7242,0.7262,0

slide20

Aplusix, Scatterplot, Ecolab, BlueJ, etc.

Observation Logs

Student Interaction Logs

Sensor data

  • Interventions
  • Intelligent agents
  • Improved error messages
  • Analysis
  • Affect detectors
  • Behavior detectors
  • Novice programmer errors
sample log aplusix
Sample Log: Aplusix

%;ACTIONS;#Date=1/16/2007#Heure=14:57:59;#TypeProbleme=TpbDevelopper

0;0.0;structure;();0;();();();();();();

1;0.0;enonce;();0;-7x(7x{@^[2]}-7x+4);();(devant);rien;;N1;

2;5.1;placerCurseur;();0;-7x(7x{@^[2]}-7x+4);();(0 2 derriere);rien;;N1;

3;0.8;dupliquer;();1;-7x(7x{@^[2]}-7x+4);();(0 2 derriere);rien;V1;N1;

4;1.9;selection;();1;-7x(7x{@^[2]}-7x+4);();rien;();V1;N1;

5;5.3;-;();1;7x(7x{@^[2]}-7x+4);();rien;();V0;N0;

6;1.5;BackSpace;();1;?;();(dedans);rien;V-;N-;

7;2.0;-;();1;-?;();(0 dedans);rien;V-;N-;

8;3.7;4;();1;-4;();(0 0 derriere);rien;V0;S0;

9;0.2;9;();1;-49;();(0 1 derriere);rien;V0;S0;

10;2.7;x;();1;-49x;();(0 1 derriere);rien;V0;S0;

11;1.3;{@^[?]};();1;-49x{@^[?]};();(0 1 1 dedans);rien;V-;N-;

sample log ecolab
Sample Log: Ecolab

New Activity Toolbar Button Click 0

6

Activity 1 6

Activity Chosen: Food 4 6

Suggested Help 0 6

Suggested Challenge 1 6

Challenge Accepted 1 8

View Web change 13

View Web change 14

View Web change 14

Action Show 19

sample log scatterplot tutor
Sample Log: Scatterplot Tutor

*000:03:781 READY

.

*000:59:503 APPLY-ACTION

WINDOW; ALGEBRA-2-TRANSLATOR::VARIABLE-TYPE-MODEL,

CONTEXT; SPLOT-DB-C-0-10-0-10,

SELECTIONS; (|var-1val-1|),

ACTION; SUBSTITUTE-TEXT-INTO-BLANK,

INPUT; ("Numerical"),

.

*000:59:503 UPDATE-P-KNOW

META; META-VALUING-NUM-FEATURES,

PRODUCTION; (CHOOSE-VAR-TYPE-NUM MIDSCH-VARIABLE-TYPING),

SUCCESS?; T,

P-KNOW; 0.33333333333333326,

..

slide24

Aplusix, Scatterplot, Ecolab, BlueJ, etc.

Observation Logs

Student Interaction Logs

Sensor data

  • Interventions
  • Intelligent agents
  • Improved error messages
  • Analysis
  • Affect detectors
  • Behavior detectors
  • Novice programmer errors
others
Others
  • Neutrality
  • Surprise
others1
Others
  • Off-task, conversation
  • Inactive
  • Gaming the system
slide42

Aplusix, Scatterplot, Ecolab, BlueJ, etc.

Observation Logs

Student Interaction Logs

Sensor data

  • Interventions
  • Intelligent agents
  • Improved error messages
  • Analysis
  • Affect detectors
  • Behavior detectors
  • Novice programmer errors
talk to me
Talk to me

%;ACTIONS;#Date=1/16/2007#Heure=14:57:59;#TypeProbleme=TpbDevelopper

0;0.0;structure;();0;();();();();();();

1;0.0;enonce;();0;-7x(7x{@^[2]}-7x+4);();(devant);rien;;N1;

2;5.1;placerCurseur;();0;-7x(7x{@^[2]}-7x+4);();(0 2 derriere);rien;;N1;

3;0.8;dupliquer;();1;-7x(7x{@^[2]}-7x+4);();(0 2 derriere);rien;V1;N1;

4;1.9;selection;();1;-7x(7x{@^[2]}-7x+4);();rien;();V1;N1;

5;5.3;-;();1;7x(7x{@^[2]}-7x+4);();rien;();V0;N0;

6;1.5;BackSpace;();1;?;();(dedans);rien;V-;N-;

7;2.0;-;();1;-?;();(0 dedans);rien;V-;N-;

8;3.7;4;();1;-4;();(0 0 derriere);rien;V0;S0;

9;0.2;9;();1;-49;();(0 1 derriere);rien;V0;S0;

10;2.7;x;();1;-49x;();(0 1 derriere);rien;V0;S0;

11;1.3;{@^[?]};();1;-49x{@^[?]};();(0 1 1 dedans);rien;V-;N-;

slide49

I’m not a math genius but I’m pretty sure that

8x2-2x+6-(-5x2+8x+3) != christine+cyril=abigail

analysis methods
Analysis methods
  • Clean the data
  • Define the different features
  • Distill new features
  • Define desired range of values
  • Select an appropriate statistical test or data mining algorithm
  • Validate the findings
analysis techniques
Analysis techniques
  • Statistical methods
  • Data mining techniques
persistence of affective states
Persistence of affective states
  • Regardless of software, boredom tends to persist
  • Affect and behavior detection
affect and behavior
Affect and behavior
  • Students who attempt the most difficult problems experience flow the most
  • Students who try the lowest levels experience more boredom and confusion.
  • Students who take the longest time in solving the problems experience confusion the most
  • Students who take the shortest time experience confusion the least.
  • Students who use the most number of steps to solve a problem experience confusion and boredom the most.
  • Students who take the least number of steps experience more flow.
carelessness
Carelessness
  • Creation of models that generalize across different datasets and countries
  • Students who are bored and confused are less likely to be careless
  • Students who are engaged are more likely to be careless
novice programmer behaviors
Novice programmer behaviors
  • Students who are persistently confused do worse on the midterm
  • Students who are able to resolve their confusion do better
  • Students with low error quotients do better on the midterm
slide56

Aplusix, Scatterplot, Ecolab, BlueJ, etc.

Observation Logs

Student Interaction Logs

Sensor data

  • Interventions
  • Intelligent agents
  • Improved error messages
  • Analysis
  • Affect detectors
  • Behavior detectors
  • Novice programmer errors
closing the loop
Closing the loop
  • We continue to create new models and integrate the models with software
  • We still have a long way to go
my thanks
My thanks
  • Ryan Baker
  • Jessica Sugay
  • Thor Collin Andallaza
  • Rina Joy Jimenez
  • Jason King Li
  • Tricia Monsod
  • Diane Lee
  • MinminLagud
  • Sweet San Pedro
  • Thomas Dy
  • And all the “cast of thousands” who constitute the ALLS
thank you questions

Thank you!Questions?

mrodrigo@ateneo.edu

http://penoy.admu.edu.ph/~alls