Affective learning — a manifesto (Picard et al.)

Picard, R.W., Papert, S., Bender, W. , Blumberg, C., Cavallo, D., et al. (October 2004). Affective learning: A manifesto. Technology Journal 22(4), 253-269.

AI and the use of computers to model thinking has tended to frame thinking and learning as information processing, with no attention paid to the role of affect on such processes. The authors, however, point to “new advances in understanding the human brain not as a purely cognitive information processing system, but as a system in which affective functions and cognitive ones are inextricably integrated with one another” (p. 253). They call for more work examining the importance of various affective states during the learning process, in order to build technologies that can provide both affective and cognitive support.

–A slight positive mood induces a different kind of thinking, characterized by a tendency toward greater creativity and flexibility in problem solving, as well as more efficiency and thoroughness in decision making. Positive affect may also increase intrinsic motivation (p. 254).

–Marvin Minsky even argues that “…when we change what we call our ’emotional states’, we’re switching between different ways to think (p. 254).

–There is a lack of theory regarding the role of affect in learning. For example, affective phenomena observed in natural learning situations include interest, boredom, surprise. Which emotions are most important and how do they affect learning? How does knowledge of one’s affective state influence outcomes in the learning experience?

–There has been considerably more work done with respect to motivation and learning (intrinsic vs. extrinsic influences, self-efficacy, etc.) and researchers have presented motivation theories that contain affective and cognitive components of goal directed behavior.

Emotion recognition- when the camera and computer (equipped with pattern recognition software) is used to recognize facial muscle movements and based on certain known patterns, relate the user’s expression to her affective state. Certain specific muscle movements are known as “facial actions.”

Galvactivator- a skin-conductivity sensing glove that can communicate wirelessly with a nearby hand-held computer. Skin conductivity gives a measure of psychological arousal, which is a strong predictor for both attention and memory.

***

Some have claimed that the emotional components may be, in some ways, dominant. Don Norman: “There must be a regulatory system that interacts with the cognitive component. And it may well be that it is the cognitive component that is subservient, evolved primarily for the benefit of the regulatory system, working through the motions, through affect” (p. 259).

“Thus, these pioneering theorists suggested that cognition, and by extension learning, took place within the context of emotional, motivational, perceptual and behavioural structures that shaped those very processes” (p. 259).

The authors propose that there is no unitary learning algorithm or module; rather, one must understand “the combination of underlying structures and processes that radically simplify what would otherwise be a complex learning problem. In nature, these internal structures are cognitive, behavioural, social, emotional, shallow and deep, innate and learned, purposed and repurposed. Indeed, an important way that internal structures simplify the learning task is by acting so as to bias the learning to take maximal advantage of external environmental and social-emotional interactions that serve to structure and constrain the learning task” (p.259).

(1) Curious robot

  • “Humans are natural and motivated teachers for entities that are rewarding to work with” (p. 259)
  • To learn, “a curious robot will need a deeper understanding of the learning process…when to learn (or when to get help to learn), what to learn, from whom, how (e.g., recognise success, correct errors, judge progress), and why” (p. 260)
  • Opportunity to explore cognitive-affective models of saliency and affective value

(2) Teachable interactive character [Teachable agents are considered to be under the umbrella of “affective learning”]

  • Shows promise in motivating learners and also encouraging them to reflect on attitudes about learning and other “meta-learning concerns” (p. 260).
  • [Commercial example: Nintendogs]
  • Ultimate goal: for child to gain a deeper understanding of the teaching-learning process and uses it to adapt his or her behavior and hypotheses about learning and teaching. 

(3) Learning companion

  • Serves as a “collaborator” to help the child learn, and in so doing, learn how to learn better.
  • Help keep the child’s exploration going and identify and respond appropriately to affective state (frustration or boredom; curiosity or interest; enjoyment or mastery)
  • The presence of someone who cares, or at least appears to care, can be motivating. Studies have linked interpersonal relationships btw teachers and students to motivational outcomes over the long term (p. 261)

***

Technology and Learning:

  • Level 1 — the holding power of the computer / intensity of engagement
    • Csikszentmihalyi – dynamic btw challenge and mastery. People become most engaged in activities that are challenging but not overwhelming.
    • Papert’s “hard fun”
  • Level 2 — making it personal / quality of engagement
    • Constructionism — the engagement of the builder
      • Learners feel differently about the knowledge when they experience themselves as active participants with control over (and personal involvement in) the learning process. The way they feel about the knowledge influences what they will do with it and especially how they reflect on it, which in turn influences how it grows and connects (p. 262).
    • The physical and the digital
      • Physical nature of [LEGO] construct allows children to draw on their sophisticated skills and intuitions for sensing and manipulating the environments in which they live while the digital programmability allows them to turn these intuitions into formal knowledge.
      • The child’s emotional attachment to the objects they have known, their likes and dislikes, their aesthetic judgments all come into play.
      • Researchers such as Lave & Wenger (1991) have argued that people form their strongest relationships with knowledge through concrete representations and activities.
    • Bodies of knowledge (anthropomorphisable constructs; ex: Logo Turtle)
    • Music
  • Level 3 — affective epistemology
    • Knowing how, knowing that, and getting to know you
      • The child is getting to know the mediating technology as one might get to know and like a person — it engages affect in deep and essential ways.
      • Theoretical sources: The Society of Mind, object-oriented programming, psychoanalytic theory, “internalization of objects”
    • Making mathematics that people will love to learn
      • Focus is on what is learned, i.e., how can things-to-learn be designed so as to elicit affect in ways that will facilitate learning.
  • Level 4 — the social side of affective learning
    • Roots, fruits and shoots
      • “Learning is rooted in the person and the culture; it bears fruit through the construction process; it has shoots that branch into new areas, shaping and transforming the community around the learner” (p. 264).
      • “In order for learning to become truly rooted, a person has to have a deep emotional attachment to the subject area. Rooting and possibilities for barning flow from a better understanding of emotion, motivation, attention, comfort, community, and culture” (p. 264)
    • Wear learning (Florida’s “Truth” campaign)
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