The meaning behind smileys - presentation at EMPIRE 2013 workshop at UMAP2013
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The document summarizes research on developing a self-report tool called the Smiley Based Affect Indicator (SBAI) to measure user affect using smiley faces. An online survey with over 990 responses was conducted to collect empirical data on how smileys are associated with levels of valence and arousal. The results supported using SBAI as a metacognitive scaffolding tool to add affect labels to system prompts. Further analysis of the data and interface refinements were suggested to improve SBAI for measuring affect without interrupting the user experience.
![• 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 . . .](https://image.slidesharecdn.com/slides-empire-2-130610074148-phpapp02/85/The-meaning-behind-smileys-presentation-at-EMPIRE-2013-workshop-at-UMAP2013-17-320.jpg)
The meaning behind smileys - presentation at EMPIRE 2013 workshop at UMAP2013
- 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
- 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!