An Affective Model of Interplay between Emotions and Learning: Reengineering Educational Pedagogy - Building a Learning Companion

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: a. conceptualize the impact of emotions upon learning, and then, b. build a working computer-based model that will recognize a learner’s affective state and respond appropriately to it so that learning will proceed at an optimal pace. 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 emphasize 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 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 student who have strayed from learning return to 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. Although computers perform as well as people only in highly restricted domains, we believe that accurately identifying a learner’s emotional/cognitive state is a critical indicator of how to assist the learner in achieving an understanding of learning process. We also assume that computers, sooner than later, will be more capable of recognizing human behaviors that lead to strong inferences about affective state. We propose to build 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 (or is stalled) 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. Axis -1. 0 -0. 5 0 +0. 5 +1. 0 Anxiety-Confidence Anxiety Worry Discomfort Comfort Hopeful Confident Boredom-Fascination Ennui Boredom Indifference Interest Curiosity Intrigue Frustration-Euphoria Frustration Puzzlement Confusion Insight Enlightenment Ephipany Dispirited-Encouraged Dispirited Disappointed Dissatisfied Satisfied Thrilled Enthusiastic Terror-Enchantment Terror Dread Apprehension Calm Anticipatory 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. 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 seem to 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 emotion 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 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 Awe Puzzlement Satisfaction Confusion Curiosity II I Negative Positive Affect Affect III IV Frustration Hopefulness Discard Fresh research Misconceptions