1. Meta-Cognition, Motivation, and Affect PSY504 Spring 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. 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.