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An Affective Model of Interplay Between Emotions and Learning Reengineering Educational Ped



An Affective Model of Interplay Between Emotions and Learning: Reengineering Educational Pedagogy—Building a Learning Companion
Barry Kort, Rob Reilly, Rosalind W. Picard M.I.T. Media, Laboratory {bkort, reilly, picard}@media.mit.edu URL: http://www.media.mit.edu/affect/AC_research/lc Abstract
There is an interplay between emotions and learning, but this interaction is far more complex than previous theories have articulated. This article proffers a novel model by which to: 1). regard the interplay of emotions upon learning for, 2). the larger practical aim of crafting computer-based models that will recognize a learner’s affective state and respond appropriately to it so that learning will proceed at an optimal pace. subjects. What we fail to teach them is that all these feelings associated with various levels of failure are normal parts of learning, and that they can actually be helpful signals for how to learn better. Expert teachers are very adept at recognizing and addressing the emotional state of learners and, based upon their observation they take some action that positively impacts learning. But what do these expert teachers ‘see’ and how do they decide upon a course of action? How do students who have strayed from learning return to a productive path, such as the one that Csikszentmihalyi [1990] refers to as his “zone of flow”? Skilled humans can assess emotional signals with varying degrees of accuracy, and researchers are beginning to make progress giving computers similar abilities at recognizing affective expressions. We believe that accurately identifying a learner’s cognitive-emotional state is a critical mentoring skill. Although computers perform as well as or better than people in selected domains, they do not yet rise to human levels of mentoring. We envision that computers will soon become capable of recognizing human behaviors indicative of the user’s affective state. We have begun research that will lead to our building of a computerized Learning Companion that will track the affective state of a learner through their learning journey. It will recognize cognitive-emotive state (affective state), and respond appropriately. We believe that the first task is to evolve new pedagogical models, which assess whether or not learning is proceeding at a healthy rate and intervene appropriately; then these pedagogical models will be integrated into a computerized environment. Two issues face us, one is to research new educational pedagogy, and the other is a matter of building computerized mechanisms that will accurately and immediately recognize a learner’s state by some ubiquitous method and activate an appropriate response.

1. Looking around then moving forward
The extent to which emotional upsets can interfere with mental life is no news to teachers. Students who are anxious, angry, or depressed don’t learn; people who are caught in these states do not take in information efficiently or deal with it well. - Daniel Goleman, Emotional Intelligence Educators have emphasized conveying information and facts; rarely have they modeled the learning process. When teachers present material to the class, it is usually in a polished form that omits the natural steps of making mistakes (e.g., feeling confused), recovering from them (e.g., overcoming frustration), deconstructing what went wrong (e.g., not becoming dispirited), and starting over again (with hope and enthusiasm). Those who work in science, math, engineering, and technology (SMET) as professions know that learning naturally involves failure and a host of associated affective responses. Yet, educators of SMET learners have rarely illuminated these natural concomitants of the learning experience. The result is that when students see that they are not getting the facts right (on quizzes, exams, etc.), then they tend to believe that they are either ‘not good at this,’ ‘can’t do it,’ or that they are simply ‘stupid’ when it comes to these

Axis
Anxiety-Confidence Boredom-Fascination Frustration-Euphoria Dispirited-Encouraged Terror-Enchantment

-1. 0
Anxiety Ennui Frustration Dispirited Terror

-0. 5
Worry Boredom Puzzlement Disappointed Dread

0
Discomfort Indifference Confusion Dissatisfied Apprehension Comfort Interest Insight Satisfied Calm

+0. 5
Hopeful Curiosity Enlightenment Thrilled Anticipatory

+1. 0
Confident Intrigue Epiphany Enthusiastic Excited

Figure 1 – Emotion sets possibly relevant to learning

2. Two sets of research results
This research project will have two sets of results. This paper offers the first set of results, which consists of our model and a research method to investigate the issue. A future paper will contain the results of the empirical research—the second set of results. This paper will address two aspects of our current research. Section 3 will outline our theoretical frameworks and define our model (Figures 1 and 2). Section 4 will describe our empirical research methods.

a large number of complex factors; thus, we expect that the space of emotions presented here might be simplified and refined further as we tease out which states are most important for shaping the companion’s responses.

Constructive Learning
Disappointment Puzzlement Confusion Awe Satisfaction Curiosity

II
Negative Affect III
Frustration Discard Misconceptions

I
Positive Affect IV
Hopefulness Fresh research

3. Guiding theoretical frameworks: An ideal model of learning process
Before describing the model’s dynamics, we should say something about the space of emotions it names. Previous emotion theories have proposed that there are from two to twenty basic or prototype emotions (see for example, Plutchik, 1980; Leidelmeijer, 1991). The four most common emotions appearing on the many theorists’ lists are fear, anger, sadness, and joy. Plutchik [1980] distinguished among eight basic emotions: fear, anger, sorrow, joy, disgust, acceptance, anticipation, and surprise. Ekman [1992] has focused on a set of from six to eight basic emotions that have associated facial expressions. However, none of the existing frameworks address emotions commonly seen in SMET learning experiences, some of which we have noted in Figure 1. Whether all of these are important, and whether the axes shown in Figure 1 are the “right” ones remains to be evaluated, and it will no doubt take many investigations before a “basic emo tion set for learning” can be established. Such a set may be culturally different and will likely vary with developmental age as well. For example, it has been argued that infants come into this world only expressing interest, distress, and pleasure [Lewis, 1993] and that these three states provide sufficiently rich initial cues to the caregiver that she or he can scaffold the learning experience appropriately in response. We believe that skilled observant human tutors and mentors (teachers) react to assist students based on a few ‘least common denominators’ of affect as opposed to

Un-learning Figure 2 – Proposed model relating phases of learning to emotions in Figure 1

Nonetheless, we know that the labels we attach to human emotions are complex and can contain mixtures of the words here, as well as many words not shown here. The challenge, at least initially, is to see how our model and its hypothesis can do initially with a very small space of possibilities, since the smaller the set, the more likely we are to have greater classification success by the computer. Figures 2 attempts to interweave the emotion axes shown in Figure 1 with the cognitive dynamics of the learning process. The horizontal axis is an Emotion Axis. It could be one of the specific axes from Figure 1, or it could symbolize the n-vector of all relevant emotion axes (thus allowing multi-dimensional combinations of emotions). The positive valence (more pleasurable) emotions are on the rght; the negative valence (more i unpleasant) emotions are on the left. The vertical axis is what we call the Learning Axis, and symbolizes the

construction of knowledge upward, and the discarding of misconceptions downward. (Note: we do not view learning as being simply a process of constructing/deconstructing information or simply a process of adding/subtracting information; this terminology is merely a projection of one aspect of how people can think about learning. Other aspects could be similarly included along the Learning Axis.) The student ideally begins in Quadrant I or II: they might be curious and fascinated about a new topic of interest (Quadrant I) or they might be puzzled and motivated to reduce confusion (Quadrant II). In either case, they are in the top half of the space, if their focus is on constructing or testing knowledge. Movement happens in this space as learning proceeds. For example, when solving a puzzle in The Incredible Machine, a student gets an idea how to implement a solution and then builds its simulation. When she runs the simulation and it fails, she sees that her idea has some part that doesn’t work – that needs to be deconstructed. At this point it is not uncommon for the student to move down into the lower half of the diagram (Quadrant III) where emotions may be negative and the cognitive focus changes to eliminating some misconception. As she consolidates her knowledge—what works and what does not—with awareness of a sense of making progress, she may move to Quadrant IV. Getting a fresh idea propels the student back into the upper half of the space, most likely Quadrant I. Thus, a typical learning experience involves a range of emotions, moving the student around the space as they learn. Typically, movement would be in a counterclockwise direction If one visualizes a version of Figure 2 for each axis in Figure 1, then at any given instant, the student might be in multiple Quadrants with respect to different axes. They might be in Quadrant II with respect to feeling frustrated; and simultaneously in Quadrant I with respect to interest level. It is important to recognize that a range of emotions occurs naturally in a real learning process, and it is not simply the case that the positive emotions are the good ones. We do not foresee trying to keep the student in Quadrant I, but rather to help them see that the cyclic nature is natural in SMET learning, and that when they land in the negative half, it is only part of the cycle. Our aim is to help them to keep orbiting the loop, teaching them how to propel themselves especially after a setback. A third axis (not shown), can be visualized as extending out of the plane of the page—the Knowledge Axis. If one visualizes the above dynamics of moving from Quadrant I to II to III to IV as an orbit, then when this third dimension is added, one obtains an excelsior spiral when evolving/developing knowledge. In this diagram (which is know as a phase plane plot in systems

theory), time is parametric as the orbit is traversed in a counterclockwise direction. In Quadrant I, anticipation and expectation are high, as the learner builds ideas and concepts and tries them out. Emotional mood decays over time, either from boredom or from disappointment. In Quadrant II, the rate of construction of new concepts diminishes, and negative emotions emerge as progress flags. In Quadrant III, the learner discards misconceptions and ideas that didn't pan out, as the negative affect runs its course. In Quadrant IV, the learner recovers hopefulness and positive attitude as the knowledge set is now cleared of unworkable and unproductive concepts, and the cycle begins anew. In building a complete and correct mental model associated with a learning opportunity, the learner may experience multiple cycles around the phase plane until completion of the learning exercise. Each orbit represents the time evolution of the learning cycle. (Note: the orbit doesn't close on itself, but gradually moves up the knowledge axis.)

4. Empirical research to validate the model
The results of this part of the research will provide data that will validate our model and control the action of the automated Learning Companion. A number of 6-11 year old subjects will be video taped while individually playing the Incredible Machine or Gizmos and Gadgets. There are two video cameras and a posture sensing device gathering data. One camera is a version of IBM’s Blue Eyes Camera eye-tracking device (see URL http://www.almaden.ibm.com/cs/blueeyes). The other camera, which is a conventional camcorder, provides a split-screen view of the subject’s upper body and the other part of the split-screen will show the computer display as the subject sees it. The posture sensing device uses an array of force sensitive resistors similar to the SmartChair employed by Tan et al (1997). Blue Eyes and SmartChair data will be gathered and synchronized with the data from the split-screen video tapes and will be coded based upon three observable factors: 1) surface level behavior (e.g., facial expression, body language), 2) inferred emotional state, and 3) task/game-state. Part of our research currently involves developing and testing appropriate interventions strategies when the learner is found to be stuck. In general the Learning Companion might intervene when a learner is not focused on a relevant part of the computer screen, or is focused completely outside the task area for a certain period of time, or their eye gaze is sufficiently quick/jerky for a given period of time.

In Quadrant I a learner is happily engaged in exploratory learning and/or discovery learning, there needs to be little or no intervention (short of ensuring that all the resources that the learner will need are present and accessible as they are needed). In Quadrant II, where a learner is beginning to encounter difficulties arising from a misconception or an incomplete understanding, the intervention must serve the purpose of helping the learner recognize and identify the gaps and errors in his or her mental model. The method of intervention, ranging from subtle hints in the form of Socratic questioning to direct diagnosis and give-away hints, depends on the learning orientation of the individual. At the same time, the Learning Companion must guard against the possibility that the learner might become overly crestfallen in the process. In Quadrant III, where the learner has recognized and acknowledged that they had been working from an erroneous or incomplete model, the intervention focuses on providing the emotional support required to survive and emerge from the disappointment, chagrin, anger, anguish, self-doubt, or whatever other dispiritedness may arise during the retreat and recovery phase of the learning cycle. Again, some learners require more emotional support and spiritual coaching than others. Quadrant III intervention is arguably the most challenging and uncertain. The point of Quadrant III intervention is to successfully grieve the loss and get on with life. In Quadrant IV, where the learner has ‘gone back to the drawing board’ to construct an improved understanding of the subject at hand, requires the kind of scaffolding we find in current theories for the support of model-based learning [See e.g., Soloway, 1999]. Again, Socratic inquiry methods, hints, and direct teaching may all be appropriate, depending again on the learning orientation of the student [See e.g., Jones and Martinez, 2001]. Finally, when the student makes the breakthrough back to Quadrant I with a fresh insight and a new idea, an acknowledgement ritual may be in order to celebrate progress or success. Here we want to reinforce and celebrate the feelings of pleasure and delight that accompany successful learning, so as to fuel and recharge the spirit for the next travail around the loop.

We are presently testing and revising appropriate intervention strategies. We also expect to make use of dovetailing theories of intervention that consider individual idiosyncratic styles of learning; in particular we are impressed with the research of Martinez and Bunderson [2000] and Jones and Martinez [2001] relating to the theory of learning orientation, which carries on the work begun by Chronbach and Snow [1977]. Acknowledgements: We are indebted to Ashish Kapoor and Selene Mota, respectively, for their efforts in constructing and adapting an IBM Blue Eyes Camera and a Smart Chair for our research. This material is based upon work supported by the National Science Foundation under Grant No. 0087768. Any opinions, findings, or conclusions or recommendations expressed in this material are those of the author(s) and does not necessarily reflect the views of the National Science Foundation.

6. References
[1] Csikszentmihalyi, M. (1990). Flow: The Psychology of Optimal Experience, Harper-Row: NY. [2] Cronbach, L. & Snow, R. (1977). Aptitudes and Instructional Methods: A Handbook for Research on Interactions. New York: Irvington. Note: for a summary of Chronbach and Snow see URL http://tip.psychology.org/chronbach.html [3] Ekman, Paul. (1992). Are there basic Psychological Review, 99(3): 550-553. emotions?,

[4] Goleman, D., (1995). Emotional Intelligence. Bantam Books: New York. [5] Jones, E. R. and Martinez, M. (2001) Learning Orientations in University Web-Based Courses - a paper submitted for publication in the Proceedings of WebNet 2001, Oct 23-27, Orlando, Florida. [6] Leidelmeijer, K. (1991). Emotions: An Experimental Approach. Tilburg University Press. [7] Lewis M., (1993). Ch. 16: The emergence of human emotions. In M. Lewis and J. Haviland, (Eds.), Handbook of Emotions, pages 223-235, New York, NY. Guilford Press. [8] Martinez, Margaret and C. Victor Bunderson. (2000). Foundations for Personalized Web Learning Environments, ALN Magazine, vol. 4 no. 2. [9] Plutchik, R. ‘A general psychoevolutionary theory of emotion,’ in Emotion Theory, Research, and Experience (R. Plutchik and H. Kellerman, eds.), vol. 1, Theories of Emotion, Academic Press, 1980.

5. Assessing and Applying Our Results
The timing and the nature of the intervention strategies will depend upon a valid assessment of the learner’s cognitive-emotive state and the state of the learner’s progress in the underlying learning task.

[10] Soloway, Eliot. (1999). Scaffolded Technology Tools to Promote Teaching and Learning in Science, Available at: http://hi-ce.eecs.umich.edu/hiceinformation/papers/index.html [11] Tan H.Z., Ifung Lu and Pentland A. (1997). The Chair as a Novel Haptic User Interface. In Proceedings of the Workshop on Perceptual User Interfaces, Banff, Alberta, Canada, Oct. 1997.



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