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Meta-Cognition, Motivation, and Affect

This presentation focuses on the pragmatic aspects of detecting a user’s affective state and its applications in education. It delves into the requirements for useful detection, such as real-time usability, lack of human intervention, and cost-effectiveness. The information used for affect detection includes brainwaves, physiological responses, tone of voice, facial expressions, and more. Challenges and advantages of different ground truth methods and sensor types are discussed, along with key applications in educational practice and research. The limitations of conducting affect detection research in laboratory settings are also explored.

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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.

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