Intuition, Affect, and Automatic Processes in the Classroom
Why do you teach the way you do? As psychological scientists, we are trained to use high-quality evidence when making decisions. You might overhear me telling my students: “Today we are going to use a jigsaw technique, in which each of you teach a portion of the material to each other. There is solid research supporting the effectiveness of the jigsaw classroom for student learning.” Clearly, evidence-based practice has its place in teaching just as in other areas of professional practice. But is empirical evidence the only factor that led me to try the jigsaw classroom (Aronson, 1990)? Taking a logical approach to course design and to interacting with our students is useful but it does not fully make us the teachers we are.
As teachers gain experience, our teaching style evolves. Paying attention to your gut reaction of how your courses feel can be a catalyst for that growth process. For example, the next big leap in my teaching will be adding more authentic assessment strategies, because now, when I pass out a multiple choice exam, my skin crawls. I used to get the same feeling when I would lecture on material that was in the students’ textbook (which was one impetus that led me to try out the jigsaw classroom). Now I typically use active learning exercises in class, and that feels right. The information we can gain from paying attention to our affective reactions in the classroom is but one example of how our decisions and experiences as teachers can depend not just on our conscious and empirical evaluation of our pedagogy and students but also on our reactions outside of conscious awareness.
The idea that intuition runs much of human cognition and decision making seems counter to most people’s view of how our minds work. We feel like we have free will and are consciously choosing to act, and indeed, it appears that sometimes we are. However, as explained convincingly by Bargh and Chartrand (1999), actual life (and even laboratory analogs thereof) is so busy and fast-paced that we cannot possibly have the cognitive resources to truly process information actively and make judgments consciously. The contexts in which we teach — from overloaded semesters to noisy, time-pressured classrooms — lend themselves to automatization of some of our tasks and judgments.
Both positive and negative outcomes in our classrooms stem from such unconscious processes. Here I will outline several ways in which automatic processing operates in our teaching, pointing out benefits and drawbacks, and will provide some suggestions for how to best use automaticity to maximize your effectiveness.
Automaticity in Decision Making: The Role of Affect
One potential drawback of automaticity is that we are not always aware of how our decisions are really made. Often, when asked to give a verbal explanation after a decision, we engage in post hoc reasoning rather than accurately reporting the true process. This can be seen clearly in some types of complex decision making, such as in moral and ethical judgments. In line with Zajonc’s (2001) tenet that we automatically and very quickly evaluate a stimulus, person, or situation as good or bad, Haidt (2001) views ethical decision making as stemming from a gut feeling that we then later justify using rational means. We then believe that the rational explanation is why we made that decision in the first place. We make decisions because they “feel right” at the time, and later can logically construct an explanation for others (and perhaps ourselves), but the decision itself was actually automatic.
This basic idea can also be applied to teaching and our explanation of our teaching style. How do you decide how to organize a class period or respond to a classroom situation? Note to new faculty: Do not write “I felt like it” in your application for tenure. It is not an admired response, even though it may well be true. Clore’s work (e.g., Clore & Huntsinger, 2007) on “affect as information” reveals that one’s emotional response to an object or situation is more influential on relevant judgments (and can be reported faster) than are one’s thoughts. As applied to the classroom, if we feel good and satisfied, we will value whatever is going on in the classroom at the time (even if that is not actually the source of what we are happy about). This may be entirely appropriate — a productive class filled with engaged students makes us happy, so we use that activity again the next semester. Bored, sleeping students make us feel unsettled so we explore a different approach for that unit in the future. The point here is that our affective reactions can guide our decision-making processes without our knowledge.
Affective experiences can also guide our students’ perceptions and behaviors, for good or ill. Whether your classroom (and office) feel safe or threatening will be a piece of information students use when judging their experience of your course and your subject matter (see Schwarz & Clore, 2003, for a discussion of the misattribution of mood: Your students will like you and your course if they feel happy, so tell jokes and bring cookies). If students are engaged and having fun while learning, it is likely they will conclude that they are taking your course because they like it (rather than filling a requirement); that they are skilled at the subject matter (rather than just getting through); and that the topic is important (rather than busy work).
Fleischer (2008) explored the role of student affect and self-regulation. Students who feel valued and rewarded are more likely to approach their learning tasks with a sense of intrinsic motivation, whereas those who perceive less regard for their autonomy and competence are more likely to exhibit extrinsic motivation or to disengage form the learning process entirely. To boost students’ affective experiences, Fleischer suggests (1) sharing with them the higher level goals of the course, explaining how their activities will meet the goals and improve their knowledge and skills; and (2) using cooperative learning strategies which will lead to positive social interactions with the instructor and with peers, which are affectively rewarding and build trust. To boost a sense of autonomy, Knight (2008) suggests giving students a wide range of choice about assignments, such as paper topics. Your students will be much more likely to engage in learning in your course if they feel valued and can contribute meaningfully in your classroom.
Your students will also be more engaged if they feel that you are approachable and are equally warm with all students. Harris and Rosenthal (2005) reviewed the empirical evidence regarding instructors’ nonverbal behavior. They found that immediacy behaviors such as gesturing while speaking, making eye contact with and smiling at individual students, and moving around the classroom leads to more positive student reactions than do behaviors such as staying behind a desk or using a monotone speaking voice. These authors encourage instructors to get feedback from a colleague about their immediacy behaviors and then to work to increase the frequency of one behavior at a time. Once that behavior has been fully integrated into one’s style, one can add another. For office hours, arrange your office so that you are physically near your students, without an imposing desk in between you (cf. Hensley, 1982). Your nonverbal behaviors can lead students to feel more comfortable in your classroom and in speaking with you outside of class.
Automaticity and Expertise:
The Role of Experience
Automaticity in teaching operates differently among teachers with varying amount of experience, in some beneficial as well as detrimental ways. Teaching involves a highly complex set of behaviors that includes person perception, information retrieval, ability to respond to changing situations (such as sleeping students or those with frequent questions), application of instructional techniques consonant with one’s conceptual teaching style, and so on. This ‘busyness’ of the typical classroom setting can easily lead to feelings of cognitive overload. A large factor determining how an individual teacher fares in this “psychoclutter” is amount of experience (Feldon, 2007).
Expert versus novice teachers have quite different experiences in the classroom, and these differences stem in part from automaticity. Just as more experienced drivers can and do use fewer cognitive resources to drive than novices and still do it better, more experienced teachers can make automatic judgments without sacrificing quality, and they will make better decisions to boot. Expert teachers can better and more quickly understand a classroom situation because they possess the schema structure to absorb and process the vast amount of information present. Experienced teachers are better equipped with managerial skills and instructional skills, both of which are required for an effective learning environment (Yates, 2005; see also Feldon, 2007).
An example of a pedagogical issue requiring both managerial and instructional skill is the amount of structure students need in the learning process (Yates, 2005). When students first learn a new skill such as writing in APA style, they need direct instruction — the abstract is on its own page and the first line is not indented, and so on. After opportunities to practice, students can be expected to produce something resembling an APA-style paper, although feedback will be needed for improvement. Subsequent papers can receive even less support and we can expect students to just do it. Thus, removing the scaffolding (following Vgotsky, as discussed by Yates, 2005) is a central step to an effective learning process. However, how to do this is less clear — when can pieces of the scaffold be dismantled? When is it reasonable to expect students to function independently? This is one facet of teaching in which one’s gut feelings are likely to become more trustworthy with greater experience. You can conduct your own midsemester evaluations to ask students whether they perceive they are receiving enough support.
Experience also leads to automaticity of basic behaviors in our classrooms. Novice teachers often need direct instruction regarding facets of teaching that later become automatized, such as using an appropriate speaking voice and pausing an appropriate amount of time after asking a question before answering it oneself (Yates, 2005). However, over time, these skills become automatized and incorporated into a teacher’s style — the teacher may not be able to articulate why he or she structures class periods in a certain way or handles conversational lulls to lead the class to the next epiphany, and yet he or she does so.
Although both expert and novice teachers are subject to heightened automaticity in their behaviors when experiencing cognitive overload, the expert teachers are less susceptible to that overload. The automatized behaviors of experts are also more likely to be desirable behaviors. As reviewed by Feldon (2007), novices’ automatic behaviors — such as responding to a student brusquely rather than helping the student patiently — are typically undesirable. Expert teachers, who have had enough experience to automatize desired reactions, respond much more positively even when responding automatically. However, automatized behavior patterns can be highly resistant to change, so experienced teachers may find it difficult to adapt and update their practices when they want to (Feldon, 2007).
Although cognitive overload in the classroom is not entirely ameliorable, some strategies can help you to minimize it. First, student conduct is central: Whispering, eating, texting students are very distracting. The need for professional behavior can be made clear in your syllabus, in discussions with the class, and privately with offending students. Second, plan out each activity and transition that will occur during a class period to move seamlessly from one topic or activity to the next. I bring a stack of materials to class, organized to reflect the sequence in which each is needed during the period — starting with papers to hand back, announcements, first handout, lecture notes, then homework handouts, for example. This keeps the next activity on top of the pile, which minimizes scrambling and distraction. While you are integrating new techniques into your teaching, jot down on your lecture notes or a reminder card what questions you wish to raise for class response and reminders to pause long enough to encourage those responses. Minimizing distractions and maximizing student professional behavior and your own organization can leave more cognitive room for the actual content and interactional goals you bring to class on a given day (see Feldon, 2007).
You can build your database for expertise by intentionally learning about and trying out new strategies. Attend faculty development programs at your institution, join a teaching listserv, peruse scholarly journals on teaching, and keep reading this column. Observe colleagues’ classes to get exposure to alternate approaches that work with your student population. Then, you can experiment with the most interesting and best-fitting techniques based on both your deliberated evaluation of the likely efficacy of the technique (weighing any available empirical evidence) along with your more affective sense of what you would ideally like your teaching style to be and what feels like it may work well for your particular course and students. Seek out feedback in a nonevaluative context from colleagues. Practice and refine as you go along, and automaticity will come.
Automaticity and Social Interaction: Stereotypes and Other Baggage
Student–teacher interactions are subject to the normal pressures and foibles of any other human interaction. Automaticity in social perception has several roles to play in these interactions. Heuristic, automatic processing can yield rich social information. Overall impressions based on “thin slices” of behavior — such as very brief video clips — have been shown to be remarkably accurate. For example, Ambady and Rosenthal (1993) found that teacher evaluations at the end of a semester-long course could be accurately predicted by the teachers’ nonverbal behavior in video clips as short as 2 seconds. Students would be surprised at how much information is available from a few seconds on the first day of class and likely would not trust their gut instincts. Thin slicing implicates impression formation as an automatic process.
However, automaticity can be a detriment in our perceptions of our students. Stereotypes may lead to automaticity in impression formation. When we meet our students, their appearance or behavior may evoke nonconscious stereotypes that then guide our behavior outside of our awareness or control (Bargh & Chartrand, 1999). Sometimes student comments in class are not what I expected, and it takes me a moment to consider whether the student is wise or missing the point. In ambiguous situations, an evoked stereotype may guide our interpretation of our students’ responses, leading either to correcting their foolishness or asking them to expand upon their unanticipated insight. As Bargh and Chartrand (1999) discussed, however, stereotyping is inappropriate, as what triggers the stereotype is an actual person who is not in fact that race, or gender, or age, but is an individual who you really don’t know anything about. And unfortunately, stereotyping is more likely to occur under conditions of cognitive overload (Galinsky & Moskowitz, 2007).
Our evaluation of student excuses for missing class or assignments is subject to the same bias. Surely we follow our gut as to whether a given excuse is good or bad, and that may lead us to different paths in handling the situation (granting an extension versus assigning a zero). Ample research has shown that stereotypes can lead to self-fulfilling prophecies, such as white interviewers treating black applicants differently, which leads to actual behavioral differences during interviews (Word, Zanna, & Cooper, 1974). College professors are people too: Is it inconceivable that our own stereotypes may sometimes be activated and guide our perceptions of our students?
Other facets of our idiosyncratic histories could also automatically affect judgments about our students. Have you ever had a competent and appropriately behaved student who rubs you the wrong way, perhaps because she looks like someone who betrayed you in college or cheated on a paper last semester? Teaching is rife with social perception and interaction. The automaticity of thin slicing, stereotyping and other facets of our idiosyncratic schemas may lead to differential treatment of students and is surely why some instructors use blanket policies (everyone can miss an exam and the make up is May 2). Common blanket policies can apply to attendance (such as a maximum number of absences permitted), late assignments (such as a given percentage off for each day late for any reason), and academic misconduct (such as automatic failure of an assignment or the course), in which the instructor creates a policy which attempts to address the vagaries of student lives while maintaining appropriate standards in the course. Such policies apply equally to all students, and this reduces instructors’ unintentionally capricious interpretation of a situation.
Strategies to Maximize the Benefits and Minimize the Hazards of Automaticity
So, what made me try and then continue to use the jigsaw classroom for some of my class sessions? Perhaps it was a hunch that it would work, keeping student interest and building a sense of responsibility. Perhaps I kept using it because it felt so rewarding to see the students working collaboratively to build a common knowledge. And perhaps in the future, I should trust those intuitions and affective reactions more.
Automaticity brings both gifts and curses to our teaching. Try some strategies to take the good and avoid the bad. Be open to trying new activities and strategies in your classroom to build the database on which your better, more automatic decisions can be made in the future. Use your affective reactions as information; the distaste you feel in response to a classroom situation can motivate change. To the extent possible, minimize cognitive overload so that undesired automatic response patterns do not creep in. Reject the application of stereotypes that may be automatically evoked in response to members of a given group by evoking egalitarian ideals instead (cf. Galinsky & Moskowitz, 2007). Additionally, getting to know your students well as individuals will help to motivate them, make them feel valued (Marzano & Marzano, 2003), and will reduce your vulnerability to treating them as members of a stereotyped group (cf. Shook & Fazio, 2008). As much as possible, consider your students wise rather than as missing the point. Show them how you value them as individuals, and give them some autonomy in the learning process. Allow them to engage with you and their student colleagues, and everyone will feel good.
Ambady, N., & Rosenthal, R. (1993). Half a minute: Predicting teacher evaluations from thin slices of nonverbal behavior and physical attractiveness. Journal of Personality and Social Psychology, 64, 431-441.
Aronson, E. (1990). Applying social psychology to desegregation and energy conservation. Personality and Social Psychology Bulletin, 16, 118-132.
Bargh, J. A., & Chartrand, T. L. (1999). The unbearable automaticity of being. American Psychologist, 54, 462-479.
Clore, G. L., & Huntsinger, J. R. (2007). How emotions inform judgment and regulate thought. Trends in Cognitive Science, 11, 393-399.
Feldon, D. F. (2007). Cognitive load and classroom teaching: The double-edged sword of automaticity. Educational Psychologist, 42, 123-137.
Fleischer, S. C. (2008). Affect and self-regulation: Keeping it positive in the classroom. National Teaching and Learning Forum, 17, 6-7.
Galinsky, A. D., & Moskowitz, G. B. (2007). Further ironies of suppression: Stereotype and counterstereotype accessibility. Journal of Experimental Social Psychology, 43, 833-841.
Haidt, J. (2001). The emotional dog and its rational tail: A social intuitionist approach to moral judgment. Psychological Review, 108, 814-834.
Harris, M. J., & Rosenthal, R. (2005). No more teachers’ dirty looks: Effects of teacher nonverbal behavior on student outcomes. In R. E. Riggio & R. S. Feldman (Eds.), Applications of Nonverbal Communication. Mahwah, NJ: Erlbaum.
Hensley, W. E. (1982). Professor proxemics: Personality and job demands as factors of faculty office arrangement. Environment and Behavior, 14, 581-591.
Knight, D. D. (2008). Empowering students through choice. The Teaching Professor, 22, 3.
Marzano, R. J., & Marzano, J. S. (2003). The key to classroom management. Educational Leadership, 61, 6-13.
Schwarz, N., & Clore, G. L. (2003). Mood as information: 20 years later. Psychological Inquiry, 14, 296-303.
Shook, N. J., & Fazio, R. H. (2008). Interracial roommate relationships: An experimental field test of the Contact Hypothesis. Psychological Science, 19, 717-723.
Word, C. O., Zanna, M. P., & Cooper, J. (1974). The nonverbal mediation of self-fulfilling prophesies in interracial interaction. Journal of Experimental Social Psychology, 10, 109-120.
Yates, G. C. R. (2005). ‘How obvious’: Personal reflections on the database of educational psychology and effective teaching research. Educational Psychology, 25, 681-700.
Zajonc, R. B. (2001). Feeling and thinking: Closing the debate over the independence of affect. In J. P. Forgas (Ed.), Feeling and thinking: The role of affect in social cognition (pp. 31-58). Cambridge: Cambridge University Press.
TEACHING TIPS provides the latest in practical advice on the teaching of psychology and is aimed at current and future faculty of two-and four-year colleges and universities. Teaching Tips informs teachers about the content, methods, and profession of teaching. Send article ideas or draft submissions directly to Raymond Green, Teaching Tips Editor, Texas A&M University-Commerce, PO Box 468-3002 or Raymond_green@tamu-commerce.edu. Co-editors: Jennifer Schroeder and Mary Hendrix, Texas A&M University- Commerce
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