Behavior Management #2: Core concepts in behavior management
More conceptual background to help educators plan for managing behavior and instruction.
This is one of the series of posts about behavior management to which I alluded in the introductory post of 23 September 2021. This one fits into the “principles” rather than the self-guiding “maxims” category. Please understand that I do not intend this or the subsequent posts to serve as academic treatises; I shall not provide copious references as I would were I writing chaptes for a text book. When I do refer to a specific finding, I’ll usually create a name-date citation and provide a correlated reference at the end of the post. Mostly, though, in keeping with the relaxed nature of Special Education Today, I’ll simply explain ideas. To be sure, I’ll also add some sources for additional readings.
In this post, I present principles forming the foundation for the analysis of behavior that I shall explain in subsequent posts. The foundation is composed of notes about (a) a scientific orientation, (b) types of knowledge, (c) big ideas, (d) big ideas about behavior, and (f) teaching.
I plan to present a scientifically oriented perspective on behavior, and also on teaching. A scientific perspective requires explanations that we can document objectively; the same explanatory factors must be accessible to multiple individuals, that is, to be available via independent observation and experimentation. (This is at the heart of the so-called “replication crisis” that has been sweeping science; I have a little pride in contributing to the resulting open science movement.) The scientific view stands in contrast to a belief-based approach in which explanations depend on subjective factors.
From a scientific perspective, we say we know something when we can explain it; explaining something requires that we can describe it, predict it, and control it.
Describing behavior means that we can objectively define it and directly assess when it is and is not occurring. Descriptive research answers questions such as “how often do toddlers pitch a fit?” or “what is the average age by which children learn to regroup in subtraction?”
Predicting behavior means that we can objectively say when it is more and less likely to occur. Importantly, predicting behavior pre-supposes that we can describe it. Note, too, that predicting behavior requires objective specification of conditions when it is more or less likely to occur. Lots of correlational research examines predictions: “Are children more likely to act up on windy days?” or “does the rate at which children can rapidly count objects correlate with how early they will learn multiplication?”
Controlling behavior requires that we objectively demonstrate that we can arrange environments (situations, conditions, etc.) when behavior is more or less likely to occur. We have to show, in a sense, that we can turn behavior on and turn it off. That is, we need to prove that when Condition A is in effect, the behavior will occur more (or less) often, but when Condition B is in effect, the behavior will occur less (or more) often. Note that controlling behavior pre-supposes that we can describe and predict behavior. Note, also, that simply observing that behavior occurs more under Condition A than it does under Condition B provides insufficient evidence of control; that's just predicting. To control behavior, we have to manipulate the conditions in experimental ways. Outright experiments help explain control by answering questions such as “When we place Johnny in a structured classroom, does he bit his wrists more often or less often than when we place him in a situation where there are few requirements?” or “Under which of these three conditions does Johnny complete more subtraction problems: Teacher monitoring, self-monitoring, or no monitoring?”
For now, provided that readers can accept these features of a scientific approach, we are good to go, we can proceed with the exposition of principles. For readers who would like a more nuanced explanation of the scientific approach, please see the accompanying post about foundations of science and research (currently being developed).
Let me stipulate early that learning is reflected in behavior. We want our students to behave like they (a) know stuff and (b) know how do stuff. I think about these two ideas as the superordinate goals of education. If students know stuff, they can answer questions about that stuff. If they know how do do stuff, they can not only answer questions about how to do that stuff but, more importantly, they can actually perform-act-behave that stuff.
In the world of cognitive psychology, these concepts are called “declarative” and “procedural” knowledge; in Siegfried Engelmann's instructional analyses, they are called “concepts” and “operations.” It's important that we understand that people have procedural and declarative knowledge because of what they can say and what they can do. (As I hope to develop later, declarative and procedural knowledge interact; not only are the categorizations too simple, they are not absolutely independent.) Importantly, people can and often do perform activities that they can't explain...and, thank goodness I don't have to employ declarative knowlege to coach myself through making my heart beat, walking, chewing, and so forth!
Allow me to illustrate with some typical educational behaviors. Do we want our students to be able to read with comprehension, write with clarity, compute accurately, and get along with peers appropriately? Those sound like good goals to me.
Achieving these goals requires both declarative and procedural knowledge on the part of students and on educators’ part. In arithmetic, it's pretty easy to understand these two ideas. One can think of declarative knowledge as including relationships between numerals and number names (e.g., when shown a printed 3, the students say, “Three!”; when shown a 7, they say “Seven!,” and when shown a +, they say “plus!”) It may be tempting to think of math “facts” (3 + 4 = 7) as declarative knowledge, but facts can also be computed (counting on from the “four...five, six, seven”) which requires procedural knowledge. More complicated computation is clearly procedural (e.g., “first, outside, inside, last”).
How do we achieve those goals? I submit that, starting at the top, we need to have fundamental agreement about “Big Ideas” regarding teaching and learning. (Time for me to nod in the direction of Doug Carnine, the guru about “big ideas.”) In this post—and in those that follow it—you will find I return repeatedly to this perspective regarding “big ideas for teaching.”
Big ideas about behavior are the “principles” that educators should use to understand their and their students actions.
A starting principle, before we turn to guiding instructional ideas, is that students’ and their families’ outcomes are of paramount importance. Results for kids and parents should be foremost among any educator’s foci. What’s more important than our students’ success? I’d say, “Nothing.”
Sure, the conditions under which an educator works (salary, bosses, incompetent colleagues, medling outsiders, etc.) are terrifically influential, but what happens to our kids has to be #1. Even if we work with poopheads, it’s our job to help our kids have optimal outcomes.
Although we should honor the views of kids and parents, honoring their views doesn’t mean that their opinions or expressions of their experiences should be the final driver. Those experiences usually are subjective; we need to focus on the objective outcomes. The main point here is that we should agree about goals and objectives (sound familiar?). And, we should recognize that determining whether we are achieving those goals requires that we assess students' declarative and procedural knowledge…in scientifically defensible ways.
Behavior Big Ideas
Here are four important concepts that undergird the forth-coming analyses. They have to do with behavior, environments and people, and learning.
Behavior and environment mutually influence each other. I often think about this as behavior and environment being locked into a continuous dance. The dance is reciprocal. Environments affect behavior and behavior changes environments. We do something and that action changes the world around us; those changes in the world affect the chances that we'll act in a similar way again. (It follows, as I'll be explaining that to change behavior we must change the environment.)
Behavior is a function of the person and the environment. As a simplified equation, this function might be expressed in this way:
B = P * E <== “behavior is a function of the person times the environment”
I know some will wonder about interactions and “error” in this simplified equation; OK, but let's just agree that error is negligible in comparison to the two main effects...and that the interaction term (different strokes for different folks) could be examined scientifically. Here's what is important to understand: If you want to assess the influence of “the person” on behavior, you need to study shiploads of people while holding the environment constant. If you want to understand the contribution of “the environment” to behavior, you need to hold the person constant and examine what happens when you experimentally change environments. Option A leads to personality psychology and its siblings (e.g., social psychology). Option B leads to behavioral psychology and its siblings (e.g., teaching). At the least, remember the idea of function, as it is very important in understanding behavior: Behavior has functions; it operates on the environment.
People learn behaviors. Those reciprocal interactions with the environment teach behavior. As educators, we can modify the environment to teach behavior. Behavior doesn’t just pop into being. People learn to behave in certain ways under certain circumstances. We need to arrange those ways and those circumstances. Many aspects of psychology (e.g., reinforcement, stimulus control, etc.) are in play here, and educators should know them and how to deploy them to cause learning.
People learn from examples. Another way to say this is that, as many people have said, experience is a great teacher. But we have to be careful here. We have to understand that learning requires many examples (and not examples, but that’s a story for later). Now, to be sure, near-random experience will surely teach something, but if you want to teach something effectively (i.e., efficiently and with lasting benefits), you should arrange the experiences carefully; that is, you should select and sequence the examples the learner experiences so that those examples clearly lead to a conclusion. As Engelmann and Carnine (1982 and in a revision) argued, ideally the instructional sequence of experiences will lead to one and only one conclusion.
Now, to be sure, some of my colleagues will disclaim this view as too focused on Main Effects and argue for Interactions. I understand. But if one wants to know “what works” start with Option B. Once you have established the big picture, see whether there are moderators or mediators.
Others will surely dismiss the perspective as too behavioral. Although those dismissals may include language about humanism, the unpredictable, and even religion, the dismissals are mostly pleas for a softer, less deterministic perspective. I consider myself a humanist, and I very much want educators to take a humanistic approach to identifying and selecting educational goals. Examples of humanistic goals are teaching academic competencies and pro-social behaviors. Examples of non-humanistic goals would include teaching teaching academic incompetence and delinquent behavior!
Learning is Evolutionary
Here is an important parallel. Think about Darwin's evolution (even if you reject it on religious or other grounds; just think about the mechanisms). In simple terms, when a mutation occurs in a species’ genome, that mutation either leads to greater survival of the members of the species who carry the mutation, or to their demise. If the mutation helps members of the species who carry it succeed more often in reproducing (perhaps by getting more food, living longer, attracting more mates, and so forth), then the mutation in the genome will persist and even flourish, being carried on to successive generations.
Similarly, but at a much reduced time scale (moments, minutes, hours, or days rather than generations), when a new behavior occurs for an individual, that behavior will either persist or atrophy. Imagine a primitive organism that has sensory organs and mobility. Suppose it behaves in this way: It (a) reorients the sensory organs (looks from side to side) and when the sensory organs identify something in the environment (object, sound, odor, etc.), it (b) moves toward the identified feature environment, and it (c) performs basic functions (eats, mates, etc.). Provided that some basic function “succeeds,” the probability that the organism will reorient sensory organs, move toward features, and engage with those features in the future increases. Scientists have know for years that these principles apply to learning in infra-humans, all the way from planaria (Lee, 1963) to porpoises (Pryor et al., 1969).
At the level of the species, mutations that “work” will persist from generation to generation. At the level of the individual, behaviors that “work” will recur over time.
What Does all This Have to do with Teaching?
As educators, our job is to encourage beneficial behaviors in a learner's repertoire or to prompt-elicit new behaviors and to make both types persist. That is, the task of educators is to promote the evolution of learners' survival behaviors.
In later posts, I'll elaborate on and employ the four principles described in the previous section as well as the earlier perspective about science and knowledge. I'll show how knowing the principles helps educators describe, predict, and (yes) control behavior.
Engelmann, S., & Carnine, D. (1982). Theory of instruction: Principles and applications. Irvington Publishers. https://www.researchgate.net/profile/Doug-Carnine/publication/303721842_Theory_of_Instruction_Principles_and_Applications/links/574f661a08aef199238ef8b6/Theory-of-Instruction-Principles-and-Applications.pdf
Lee, R. M. (1963). Conditioning of a free operant response in planaria. Science, 139(3559), 1048-1049. https://doi.org/10.1126/science.139.3559.1048
Pryor, K. W., Haag, R., & O'Reilly, J. (1969). The creative porpoise: Training for novel behavior 1. Journal of the Experimental Analysis of Behavior, 12(4), 653-661. https://doi.org/10.1901/jeab.1969.12-653
Alberto, P. A., & Troutman, A. C. (2017). Applied behavior analysis for teachers (9th ed.). Pearson.
Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied behavior analysis (3rd ed.). Pearson.
Ladyman, J. (2012). Understanding philosophy of science. Routledge.