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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.

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Affective Computing and Intelligent Interaction

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  1. 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

  2. Affective computing Computing that relates to, arises from or deliberately influences emotion - Picard, 1997, Affective Computing

  3. Affective computing • Emotion recognition • Emotion expression • Intelligent response to emotion

  4. 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

  5. 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

  6. 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

  7. Methods: Aplusix

  8. Methods: Ecolab / MEcolab

  9. Methods: Cognitive Tutor

  10. Methods: BlueJ

  11. Methods: The Incredible Machine

  12. Methods: Math Blaster

  13. SQL/EER Tutors

  14. SimStudent

  15. 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

  16. Biometrics instruments

  17. Biometrics instruments

  18. Biometrics instruments

  19. 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

  20. 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

  21. 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-;

  22. 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

  23. 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, ..

  24. 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

  25. consolidation Methods analysis

  26. Methods

  27. Methods

  28. analysis Methods

  29. Affective states

  30. Boredom

  31. Confusion

  32. Delight

  33. Frustration

  34. Engaged concentration

  35. Others • Neutrality • Surprise

  36. Behaviors

  37. On-task, solitary

  38. On-task, giving and receiving answers

  39. Other on-task conversation(probably)

  40. Off-task solitary

  41. Others • Off-task, conversation • Inactive • Gaming the system

  42. 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

  43. 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-;

  44. The problem: 5(-9x+7)+3(4x-3)

  45. The problem: 8x2-2x+6-(-5x2+8x+3)

  46. I’m not a math genius but I’m pretty sure that 8x2-2x+6-(-5x2+8x+3) != christine+cyril=abigail

  47. 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

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