The Meaning Behind Smileys – An

Affect Self Report Tool Based On

Empirical Data

Adam Moore (@adam__moore),

Christina M. Steiner

& Owen Conlan

• How to measure Affect?

• Self report avatars

• SBAI

• Online survey

• Pilot

• Main Results

• Questions . . .

Overview

• 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?

Use avatars?

Why not just use smileys?

Smiley

Based

Affect

Indicator

A RESTful affect self-report tool based on empirical data

Survey

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

Adjustment after pilot

Smiley 5 & 6 too similar, so 6 changed to be more neutral

• 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

Smiley valence vs arousal

Aro

usal / A

ctivity

Valence / Magnitude

Top Sense Word

Activity (Gender split)

Valence (Gender split)

• Why these smileys?• Didn’t have the one they wanted

• Graphics too much – why not text?

• One word is not possible

• Context . . .

Comments

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?

• 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

• 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 . . .

• 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 . . .

• 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

http://bit.ly/smILEYCase sensitive!!!

Adam.moore@tcd.ie

@adam__moore

@ImREAL_project / www.imreal-project.eu

Thank-you!

Hello!