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Meta-Cognition, Motivation, and Affect
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  1. Meta-Cognition, Motivation, and Affect PSY504Spring term, 2011 April 6, 2011

  2. Affect Detection • What is a user’s affective state at a specific moment?

  3. First note • It can be done • You have read a few examples of this • And the D’Mello & Calvo paper cites dozens more examples • More sources of data on affect leads to better detection, but it can be done reasonably well even with single data channels

  4. Today… • We will focus on the pragmatics of affect detection, rather than the technical details of building sensors, data processing, or detection algorithms • Though please feel free to bring these types of issues up wherever they seem relevant

  5. What are the requirements for useful detection for education? • Example: Must be usable in real-time

  6. What are the requirements for useful detection for education? • Must be usable in real-time • Must work with no human intervention • E.g. must be able to automate segmentation • Must be usable with real student data • Must be generalizable to population and situation of interest • Must be cost-effective • Users must be willing to comply • Breakage must be within affordable limitations • Privacy concerns when researchers use data • Must involve educationally-relevant affect

  7. How would we establish each of these? • Must be usable in real-time • Must work with no human intervention • E.g. must be able to automate segmentation • Must be usable with real student data • Must be generalizable to population and situation of interest • Must be cost-effective • Users must be willing to comply • Breakage must be within affordable limitations • Privacy concerns when researchers use data • Must involve educationally-relevant affect

  8. Information Used • In detecting affect, researchers have used • Brainwaves (EEG) • Physiological Response (GSR, EKG, Eye movement) • Tone of voice • Facial expression • Posture/butt sensor • Mouse movements/keystrokes • Interaction features • Dialogue features • Contextual cues

  9. What educational settings/conditions is each type of sensor feasible for? • In detecting affect, researchers have used • Brainwaves (EEG) • Physiological Response (GSR, EKG, Eye movement) • Tone of voice • Facial expression • Posture/butt sensor • Mouse movements/keystrokes • Interaction features • Dialogue features • Contextual cues

  10. Ground truth

  11. Ground truth • Expert judges • Self-report (in the moment, voluntary) • Self-report (in the moment, interruption) • Self-report (retrospective) • Advantages/Disadvantages of each?Specific challenges?

  12. Ground truth • Periodic ratings of pre-defined time windows? • Or report of onset of noticeable affective states? • Advantages? Disadvantages?Specific challenges?

  13. Ground truth • For self-report, should you ask about • specific affective states • valence/arousal • valence • Advantages? Disadvantages? Specific challenges?

  14. Applications • Within educational practice, what are some key applications of detecting a person’s affect? • Within education research, what are some key applications of detecting a person’s affect?

  15. Beyond 1-to-1 interactions • Historically, affect detection has been applied to data from a single student working 1-on-1 with a computer • Where else might affection detection be useful in education (or education research)?

  16. Settings of use • Almost all research on affect detection has been conducted in laboratory settings • What are the limitations of this? • What are the challenges in conducting affect detection research in ecologically valid settings? • What can we learn from affect detection and corresponding affect research in laboratory settings, that is still useful for changing educational practice?

  17. Next Class (APRIL 11) • Affect and Achievement Goals • Readings • Elliot, A.J., McGregor, H.A. (1998) Test Anxiety and the Hierarchical Model of Approach and Avoidance Achievement Motivation. Journal of Personality and Social Psychology, 76 (4), 628-644. • Pekrun, R., Elliot, A.J., Maier, M.A. (2006) Achievement Goals and Discrete Achievement Emotions: A Theoretical Model and Prospective Test. Journal of Educational Psychology, 98 (3), 583-597.