Paper at: http://ceur-ws.org/Vol-997/empire2013_paper_4.pdf

1. The Meaning Behind Smileys – An
Affect Self Report Tool Based On
Empirical Data
Adam Moore (@adam__moore),
Christina M. Steiner
& Owen Conlan

2. • How to measure Affect?
• Self report avatars
• SBAI
• Online survey
• Pilot
• Main Results
• Questions . . .
Overview

3. • Facial / Behavioral Analysis
• Special equipment
• Interferes with learning experience?
• Data intense
• Textual Analysis
• Quite a lot of text affect neutral
• Requires good models of affect expression
• Self-reports
• Require an internal awareness
• Interrupts flow & fidelity?
How to measure Affect?

4. Use avatars?

5. Why not just use smileys?
Smiley
Based
Affect
Indicator
A RESTful affect self-report tool
based on empirical data

6. Survey
Demographic Info:
• Gender
• Age
• Country of birth
• Residence
• Education
• Internet usage

7. Adjustment after pilot
Smiley 5 & 6 too similar, so 6 changed to
be more neutral

8. • 996 complete replies were received in 2 months.
• The cohort was composed of 285 women, 700 men and 11 respondents
preferred not to say.
• Reported ages ranged from 15 to 103, with an average of 26.7 (SD 10.4).
• Analytics point to a large number of responses to have been made in
answer to the mailing to Trinity College; so cultural referents are skewed
as a result. For example, nearly 70% of respondents give their country of
birth as Ireland, and over 90% give Ireland as their country of current
residence.
Online survey

9. Smiley valence vs arousal
Arousal/Activity
Valence / Magnitude

10. Top Sense Word

11. Activity (Gender split)

12. Valence (Gender split)

13. • Why these smileys?
• Didn’t have the one they wanted
• Graphics too much – why not text?
• One word is not possible
• Context . . .
Comments

14. Current Usage
• Recently used in online learning simulation
• Optional – displayed alongside feedback
• Not much usage – 152 entries over 6 weeks
• Feedback:
• Not sure what it is for
• Why do you need to know?
• How will it effect my work / score?

15. • What did we do with the input?
• Supports metacognitive scaffolding
• Rule based – new rules on affect state
• Prompts categorized to be encouraging, neutral,
• Affect Text added . . .
• Next look at timing / interruptions
Current Usage

16. • Much better statistics!
• Analysis based on sense words
• nGrams
• Sense distance - wordnet
• Ekman’s basic emotions
• Interface refinement
• Offer sense words from stemmed list?
• Reflection – Mirror MoodMapApp?
• Personalization / tuning
Still to do . . .

17. • Mapping of data to cohort survey
• User trial had full characterization survey
• Demographics
• Swedish Survey of Personality
• Learning Styles (but see [1]!!!)
• Metacognitive Awareness Inventory [2]
• Social Media Attitudes (UMAP late breaking [3])
• Look at correlations
• Stereotype construction . . .
[1] Brown, E. J., Brailsford, T. J., Fisher, T., Ashman, H. L., & Moore, A. (2006). Reappraising cognitive styles in adaptive web applications. Proceedings of
the 15th international conference on World Wide Web - WWW ’06 (p. 327). New York, New York, USA: ACM Press.
[2] Schraw, G., & Sperling Dennison, R. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460–475.
[3] Adam Moore, Gudrun Wesiak, Christina M. Steiner, Claudia Hauff, Declan Dagger, Gary Donohoe, Owen Conlan (2013) Utilizing Social Networks for
User Model Priming: User Attitudes UMAP2013 Late Breaking Results
Still to do . . .

18. • The research leading to these results has received funding from the
European Community's Seventh Framework Program (FP7/2007-2013)
under grant agreement no 257831 (ImREAL project) and could not be
realized without the close collaboration between all ImREAL partners.
Acknowledgements

19. http://bit.ly/smILEY
Case sensitive!!!
Adam.moore@tcd.ie
@adam__moore
@ImREAL_project / www.imreal-project.eu
Thank-you!

20. Hello!