ArticlePDF AvailableLiterature Review

Considerations of Baseline Classroom Conditions in Conducting Functional Behavior Assessments in School Settings

May 2018
Behavior Analysis in Practice 12(2)

DOI:10.1007/s40617-018-0269-1

Authors:

Kathryn Kestner

West Virginia University

Stephanie M. Peterson

Western Michigan University

Rebecca Eldridge

Becky Eldridge Consulting LLC

Research has shown that environmental classroom variables affect academic performance and student behavior, and appropriate behavior is often related to the presence of effective teaching practices and classroom management (Moore Partin, Robertson, Maggin, Oliver, & Wehby Preventing School Failure, 54, 172–178, 2010). For behavior analysts consulting in elementary education, some referrals for assessment and treatment of individual student behavior can be resolved by helping teachers establish effective class-wide practices. For this reason, some researchers suggest that behavior analysts should assess baseline classroom conditions as part of a functional behavior assessment (FBA; Anderson & St. Peter Behavior Analysis in Practice, 6(2), 62, 2013; Sutherland & Wehby Journal of Emotional and Behavioral Disorders, 11, 239–248, 2001). Through a literature review on effective classroom practices, we identified four specific classroom variables that have large effects on both learning outcomes and student behavior; we suggest consultants consider these four variables in baseline classroom assessments: (a) rates of active student responding (ASR), (b) appropriateness of the curriculum, (c) feedback and reinforcement, and (d) effective instructions and transitions. In this article, we will discuss each of these variables, describe how they can affect classroom behavior, and provide recommended targets from the research literature. We also provide a data-collection form for practitioners to use in their assessments of baseline classroom ecology, and for situations when these practices are not in place, we suggest potential resources for antecedent- and consequence-based interventions to decrease challenging classroom behavior.

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DISCUSSION AND REVIEW PAPER

Considerations of Baseline Classroom Conditions in Conducting

Functional Behavior Assessments in School Settings

Kathryn M. Kestner

&Stephanie M. Peterson

&Rebecca R. Eldridge

&Lloyd D. Peterson

#Association for Behavior Analysis International 2018

Abstract

Research has shown that environmental classroom variables affect academic performance and student behavior, and appropriate

behavior is often related to the presence of effective teaching practices and classroom management (Moore Partin, Robertson,

Maggin, Oliver, & Wehby Preventing School Failure, 54,172–178, 2010). For behavior analysts consulting in elementary

education, some referrals for assessment and treatment of individual student behavior can be resolved by helping teachers

establish effective class-wide practices. For this reason, some researchers suggest that behavior analysts should assess baseline

classroom conditions as part of a functional behavior assessment (FBA; Anderson & St. Peter Behavior Analysis in Practice,

6(2), 62, 2013;Sutherland&WehbyJournal of Emotional and Behavioral Disorders, 11,239–248, 2001). Through a literature

review on effective classroom practices, we identified four specific classroom variables that have large effects on both learning

outcomes and student behavior; we suggest consultants consider these four variables in baseline classroom assessments: (a) rates

of active student responding (ASR), (b) appropriateness of the curriculum, (c) feedback and reinforcement, and (d) effective

instructions and transitions. In this article, we will discuss each of these variables, describe how they can affect classroom

behavior, and provide recommended targets from the research literature. We also provide a data-collection form for practitioners

to use in their assessments of baseline classroom ecology, and for situations when these practices are not in place, we suggest

potential resources for antecedent- and consequence-based interventions to decrease challenging classroom behavior.

Keywords Functional behavior assessment .Schools .Consultation .School-based consultation .Classroom management

Classroom management and teaching practices influence both

academic performance and classroom behavior (Moore Partin,

Robertson, Maggin, Oliver, & Wehby, 2010;Repp,1994;

Talbott & Coe, 1997). For behavior analysts consulting in

elementary-education settings, helping teachers to establish

effective classroom practices can improve learning outcomes

and decreaseundesired behavior. Class-wide interventionsnot

only are important for referrals requesting assistance with

classroom management but also can be considered as frontline

interventions when consultants receive referrals for individual

students who engage in disruptive problem behavior (e.g.,

noncompliance, interrupting, distracting peers, negative peer

interactions, off-task behavior, and minor to moderate levels

of property destruction). Despite their importance, variables

related to classroom ecology may be overlooked when con-

sultants are called in to focus on a single student (e.g., when a

student is referred for a functional behavior assessment

[FBA]). Given that a lack of effective classroom practices

can contribute to the occurrence of challenging behavior, it

can be helpful to evaluate the “baseline classroom conditions”

as part of the FBA process. In some cases, disruptive behavior

can be reduced by making class-wide changes to the

environment.

Classroom management and teaching practices impact ac-

ademic performance and behavior (Moore Partin et al., 2010;

Repp, 1994; Talbott & Coe, 1997) and, thus, should be eval-

uated. For example, how teachers allocate attention can have

Electronic supplementary material The online version of this article

(https://doi.org/10.1007/s40617-018-0269-1) contains supplementary

material, which is available to authorized users.

*Kathryn M. Kestner

kmkestner@mail.wvu.edu

Department of Psychology, West Virginia University, P. O. Box

6040, Morgantown, WV 26506, USA

Department of Psychology, Western Michigan University,

Kalamazoo, MI, USA

Compass, A Positive Direction in Behavior Intervention, LLC,

Schoolcraft, MI, USA

Behavior Analysis in Practice

https://doi.org/10.1007/s40617-018-0269-1

positive or negative effects on learning outcomes and student

behavior. Low rates of teacher praise correlate with higher

rates of inappropriate classroom behavior; as such, research

also shows that increasing praise for appropriate behavior re-

sults in an increase in desired behavior (e.g., one of the earliest

studies demonstrating this effect is Madsen, Becker, &

Thomas, 1968). The number of opportunities to respond

(OTRs; e.g., a teacher asking a question and creating the op-

portunity for an observable response from the students) and

rate of active student responding (ASR) are two variables

correlated with effective learning outcomes and appropriate

behavior. Low rates of OTRs (and therefore low rates of

ASR) result in less learning (Fisher & Berliner, 1985;

Greenwood, Delquadri, & Hall, 1984; Heward, 2003) and

increased problem behavior (Sutherland & Wehby, 2001). A

third significant variable is the amount of time spent in tran-

sition. Students lose learning time during long periods of tran-

sition. Ineffective instructions and transitions can also produce

problem behavior (Colvin, Sugai, Good, & Lee, 1997).

Finally, a curriculum not matched to the current skill level of

the students can result in problem behavior and poor academic

outcomes (Anderson & St. Peter, 2013; Dunlap, Kern-Dunlap,

Clarke, & Robbins, 1991). When effective baseline practices

in these areas are absent, the behavior analyst should consider

the possibility of mitigating challenging behavior through

classroom-level changes rather than individual-level

interventions.

Classroom Ecology and Disruptive Behavior

There is a robust body of literature in behavior analysis

supporting the assumption that both desired and undesired

behavior is established through an individual’s learning histo-

ry and is maintained by environmental contingencies (Mace,

1994). In other words, the antecedents and consequences em-

bedded in the natural environment of the classroom affect

behavior, and where there is disruptive behavior, there are

variables influencing its occurrence. Challenging behavior

can be maintained by positive or negative reinforcement,

and the consequences affecting challenging behavior can be

socially mediated or automatic (i.e., reinforcement is a direct

product of the behavior). Common social functions of chal-

lenging behavior include social attention, escape from de-

mands or other aversive events, and access to tangible items

or activities (Beavers, Iwata, & Lerman, 2013). When work-

ing with individuals who engage in disruptive behavior, be-

havior analysts will often conduct an individual FBA to deter-

mine the function the behavior, which they use in the

intervention-planning process. Individual-level interventions

generally involve components aimed to change specific ante-

cedents and consequences for that individual (e.g., creating a

token economy for the student to reinforce desired behavior).

If antecedents and consequences that support appropriate

class-wide behavior are absent, then implementing individual

interventions for one student may not be the most effective

approach. Instead, consultants may want to recommend

implementing class-wide practices that could be efficient and

have more robust effects. The research-based class-wide prac-

tices we review in this article are similar to those implemented

for individual interventions, just on a larger scale. Some of

these interventions function as antecedents that tend to in-

crease the likelihood of appropriate behavior and skill acqui-

sition; other interventions function as consequences that are

less likely to reinforce inappropriate behavior while reinforc-

ing appropriate or incompatible behavior. For example, if a

child engages in disruptive behavior maintained by teacher

attention, the child’s behavior might be effectively decreased

by a class-wide intervention where the teacher increases atten-

tion and positive feedback contingent on students’appropriate

behavior while minimizing reprimands and attention for inap-

propriate behavior.

In some cases, less intrusive interventions at the classroom

level can effectively resolve a referral for disruptive behavior

without needing to advance to a more intensive FBA tech-

nique and individually targeted behavior plan. Furthermore,

if baseline classroom conditions are less than optimal, the

naturalistic reinforcement conditions may not sufficiently

maintain behavior when attempting to fade the reinforcement

schedule associated with individualized interventions (e.g.,

differential reinforcement of alternative behavior). Increasing

optimal classroom conditions may have positive results for all

students, not just the target child. For these reasons, behavior

analysts should assess baseline classroom conditions in the

early stages when working on a new case (e.g., when asked

to perform an FBA). When behavior analysts identify limita-

tions while assessing classroom ecology, they should consider

first-line interventions focused on class-wide practices. The

rationale for this approach is to ensure that a therapeutic envi-

ronment is in place before recommending more intensive or

restrictive assessments and interventions (Van Houten et al.,

1988). In classroom settings, a therapeutic environment em-

ploys practices that are reasonably expected to promote learn-

ing and appropriate behavior. When these conditions are lack-

ing, the behavior analyst should work with the teaching team

to put them in place.

Formal Assessment of Baseline Classroom

Conditions

The extent to which behavior analysts in elementary education

routinely conduct formal assessments of baseline classroom

conditions is not clear; however, a lack of standard practice

may be evidenced by the corresponding lack of specific rec-

ommendations to do so in popular textbooks on FBA (e.g.,

Behav Analysis Practice

Chandler & Dahlquist, 2014; Cipani & Schock, 2010;O’Neil,

Horner, Albin, Storey, & Sprague, 1997; Repp & Horner,

1999). Although we suggest that these assessments of class-

room ecology are not yet part of standard practice, we are

certainly not the first to recommend them. For example,

Anderson and St. Peter (2013)wrote,

In the school districts in which we consult, functional

assessments are sometimes requested when a simple

behavior-modification program would be sufficient to

improve behavior. At other times, teachers would bene-

fit from broader assistance with classroom management

or delivery of instruction. (p. 70)

In one study of 64 classrooms, researchers found a positive

correlation between ratings of appropriate curricular activities

and students’appropriate behavior (Ferro, Foster-Johnson, &

Dunlap, 1996). We believe one way to stimulate adoption of

baseline classroom assessments is to provide behavior ana-

lysts with recommendations and resources for doing so.

There exist few assessment protocols of baseline classroom

conditions that are based on direct observations and empirical

measures of teacher–student interactions (as opposed to

interviews, rating scales, and checklists; e.g., Lewis, 2007a,

b).

We suggest collecting objective observational data on four

variables as part of assessments of classroom ecology; all have

been shown to substantially impact behavioral and academic

student performance (see cited research in each respective

section). Many would view the recommendations in these

areas as “best practices”in education. These variables are (a)

OTRs and ASR, (b) appropriateness of the curriculum, (c) the

use of feedback and reinforcement, and (d) the delivery of

effective instructions and transitions. These variables are ob-

servable and measurable interactions between teacher and stu-

dent behavior (see Table 1). Furthermore, there is research

demonstrating that these variables are malleable—that is, de-

ficiencies can be modified through intervention to improve

behavior and learning (see Table 2). As such, we provide

possible interventions to improve baseline classroom condi-

tions when limitations in these practices are identified through

classroom observations (see Table 2). We provide a data-

collection form to assist clinicians in measuring variables re-

lated to the practices listed previously with the hope of pro-

viding a useable resource for school-based behavior analysts

who do not already incorporate baseline classroom assess-

ments in their practice.

Rates of ASR

ASR is an observable response to instruction (Barbetta,

Heron, & Heward, 1993). Baseline classroom conditions

should consist of frequent ASR, which necessitates frequent

OTRs. Measurement of ASR requires the observer to note the

pace of instruction, as well as student responses to instruction.

The pace of instruction is a key variable for both academic

learning and behavior management; pacing should be brisk

and appropriate for all individuals. Fast-paced lessons result

in more responding and increased learning (Carnine, 1976;

Heward, 2003). Research shows that the pace of instruction

can have academic and social benefits. In addition to promot-

ing effective learning (Gardner, Heward, & Grossi, 1994;

Skinner, Belfiore, Mace, Williams-Wilson, & Johns, 1997;

Skinner, Smith, & McLean, 1994), fast-paced instruction de-

creases off-task and disruptive behavior (Carnine, 1976;

Gunter, Denny, Jack, Shores, & Nelson, 1993;West&

Sloane, 1986). Effective, brisk-paced instruction often in-

cludes high rates of OTRs. Further, evaluating these responses

can be a useful measure of the pace of instruction. Research

reliably shows that increasing OTRs results in more correct

responses and on-task behavior and less disruptive behavior

(Sutherland, Alder, & Gunter, 2003; Sutherland & Wehby,

2001). One recommendation is four to six response opportu-

nities per minute with 80% accuracy for new material

(Council for Exceptional Children [CEC], 1987, as cited in

Gunter, Reffel, Barnett, Lee, & Patrick, 2004). Other research

suggests that a target of 12 responses per minute is more

effective for promoting accurate responding (Engelmann &

Becker, 1978). When conducting classroom assessments, be-

havior analysts may need to adjust targets based on contextual

variables (e.g., students with physical or development disabil-

ities may require different pacing for some activities than stu-

dents without disabilities).

Despite the research showing positive outcomes with ef-

fective pacing, observational studies indicate that instruction

often fails to meet the recommended criteria (Gunter et al.,

Table 1 Indicator Recommendations for Each Strategy Area

Area Recommendations

Pacing of Activities (ASR &

OTR)

12 responses per minute

(May be adjusted based on activity

type and context)

Appropriateness of Curriculum 70%–80% accuracy for new material

90% accuracy for review material

Feedback & Reinforcement 5:1 ratio of positive to corrective

feedback

Instructions Should be specific

Phrased as “do”requests

Phrased as an instruction (not as a

question)

Transitions Should have a clear beginning

and end

Should be signaled

Reinforcement for noncompliance

should be avoided

Reinforcement for compliance

should be provided

Behav Analysis Practice

2004; Shores et al., 1993; Van Acker, Grant, & Henry, 1996;

Wehby, Symons, & Shores, 1995). Rates of response oppor-

tunities range from as low as 1 per hour (Van Acker et al.,

1996) to 4.1 per minute (Gunter et al., 2004).

To evaluate ASR, behavior analysts should measure both

rate of OTRs and responses to instruction (see Appendix:

Questions/Commands;Student Responses). Greenwood et al.

(1984) defined an OTR as the interaction between (a) teacher-

formulated instruction and (b) its success in establishing ob-

servable academic responding. Thus, both teacher questions

or prompts (OTRs) and ASR must be measured. OTRs are

often best captured as a rate measure. The behavior analyst

can tally the number of teacher questions and prompts, such

as “What is the capital of Michigan?”The number of OTRs can

be divided by the total time observed to obtain the rate of OTRs.

Furthermore, the number of observable student responses can

be recorded. If OTRs and/or ASR are significantly below the

recommended target of 4 to 12 per minute (depending on con-

text), the baseline classroom conditions may not provide a ther-

apeutic environment. In this case, the behavior analyst may

want to work with the teacher on increasing ASR.

Increasing OTRs is an antecedent intervention for increas-

ing the likelihood of appropriate behavior (e.g., increasing

ASR) and promoting effective learning. When antecedent in-

terventions set the occasion for appropriate behavior, they can

subsequently lead to consequence-based changes by increas-

ing reinforceable, appropriate responses, such as on-task be-

havior (Christle & Schuster, 2003). For students who engage

in socially mediated disruptive behavior, this may function as

differential reinforcement of responding that is incompatible

with disruptive behavior.

If the teacher and behavior analyst agree to intervene on

ASR and pacing, there are several technologies and methods

to consider. Many interventions have been shown to improve

academic performance and to decrease off-task behavior.

Arranging for choral and written responding are two easy

ways for teachers to increase ASR (Archer & Hughes, 2011;

MacSuga-Gage & Simonsen, 2015). Choral responding is

when all students respond orally and simultaneously (in cho-

rus) to a cue from the teacher (Wolery, Ault, Doyle, Gast, &

Griffen, 1992). For example, if the teacher says, “What type of

problem is 10 plus 4? Get ready”(with an added gestural cue

of a finger snap), then the students should respond in unison

following the finger snap by saying, “Addition.”Response

cards can provide another method of choral responding.

Instead of oral responses, students indicate their answers by

holding up boards, cards, or other objects (Archer & Hughes,

2011). Preprinted response cards are a limited set of cards that

may include options such as A,B,C, and Dor agree and

disagree. For example, suppose the teacher says, “The capital

of the United States of America is New York City,”provides a

few seconds of think time, and then says, “Get ready, cards

up”(plus an added gestural cue). Following this cue, the class

members should respond in unison by holding up their

disagree cards. Prewritten response cards can also include

multiple-choice options in which the teacher projects ques-

tions on a screen with corresponding multiple-choice options.

Write-on response cards, such as small whiteboards, can be

used similarly but allow the teacher to ask open-ended ques-

tions. For example, if the teacher says, “What is the answer to

10 plus 4? Get ready, boards up”(plus an added gestural cue),

then the students should write “14”on their boards and hold

them up as soon as the teacher gives the cue.

Class-wide oral and written responses are a great way to

increase student responding because every student answers

every question rather than only one student getting to say

Table 2 Recommended Interventions to Improve Baseline Classroom Conditions

Strategy Area Tools to Improve Effectiveness References

Pacing of Activities

(ASR & OTR)

Implement choral responding (oral).

Implement written responding (response cards,

response boards, student response systems [SRS]).

MacSuga-Gage and Simonsen (2015)

Archer and Hughes (2011)

Curricular Revision Conduct curriculum-based assessments (CBAs; e.g.,

DIBELS, Pearson aimsweb).

Intersperse easy tasks with difficult tasks, keep difficult

tasks brief and task content functional, and offer choices

when possible.

Anderson and St. Peter (2013)

Dunlap et al. (1991)

Feedback & Reinforcement Use a MotivAider® to promote teacher praise.

Provide performance feedback, coaching, goal setting, and

video modeling to increase behavior-specific praise.

Rivera et al. (2015)

Duchaine et al. (2011); Hawkins

and Heflin (2011)

Effective Demands &

Transitions (Response Error)

Use explicit instruction (i.e., “I do, we do, you do”).

Use modeling (e.g., in vivo or video models).

Archer and Hughes (2011)

Cihak et al. (2010); Flannery and

Horner (1994)

Effective Demands & Transitions

(Signal Error)

Select a clear, consistent signal that is perceptible to

all students, and remind students to respond on signal,

then repeat instruction and signal until all students respond.

Archer and Hughes (2011)

Behav Analysis Practice

the answer, as is traditionally done. When implemented at a

brisk pace, it has the benefit of promoting on-task behavior

(Christle & Schuster, 2003; Wood, Mabry, Kretlow, Lo, &

Galloway, 2009). Choral responding has the added benefit

of allowing the teacher to assess the performance of the entire

class, provide feedback, and adjust instruction if necessary

(Archer & Hughes, 2011). In addition, recent advances in

technology have enabled the use of electronic response cards.

Electronic clickers and web-based apps, such as Kahoot®, can

be used in classrooms and have been shown to improve learn-

ing outcomes (Lantz & Stawiski, 2014; Yourstone, Kraye, &

Albaum, 2008). Conceivably, teachers can modify instruction

in real time based on student responses, as well as analyze

recorded data at a later point. Further, information on the ac-

curacy of student responding can inform decisions on whether

reteaching or curricular revision may be needed.

Appropriateness of Curriculum

As noted previously, 80% accuracy is often recommended as a

target for new material (CEC, 1987, as cited in Gunter et al.,

2004). Assessing student performance is important, and high

rates of responding allow the teacher to assess performance

frequently during instruction. Ellis, Worthington, and Larkin

(1994) identified 10 principles that should govern effective in-

struction. One of these principles is achieving moderate to high

levels of accurate responding during instruction. High accuracy

is positively correlated with student learning outcomes, whereas

low levels of accuracy are negatively correlated with student

learning outcomes. Thus, measuring student accuracy can be an

important consideration during the assessment process.

Instruction should be neither too easy nor too difficult.

Students should have the necessary prerequisite skills to be

successful in the lesson but should not yet have mastered the

current content (Marchand-Martella, Slocum, & Martella,

2004). Lessons should be challenging, but they should also

set up the students for success. There are varying opinions on

the acceptable level of accuracy during instruction. Engelmann

(1999) suggests that student responses should be correct at least

70% of the time when lesson content is first introduced, 90% if

the lesson involves skills previously taught, and 100% when

students have mastered the skill. Archer and Hughes (2011)

suggest students should respond correctly 80% of the time

when material is first introduced and 90% at mastery. As men-

tioned previously, the CEC (1987, as cited in Gunter et al.,

2004) recommends that students be correct 80% of the time

when learning new material. Taking all of these recommenda-

tions into consideration, we suggest a good rule of thumb is to

aim for at least 80% correct student responding.

Correct and incorrect responses are observable responses

that the behavior analyst can measure to determine whether

the task is appropriate for students’instructional levels (see

Appendix: Correct Student Responses;Incorrect Student

Responses). This can be done in conjunction with data collec-

tion on ASR. The number of correct responses can be divided

by the total number of responses to determine the percentage

correct. If the percentage of correct responses is significantly

below the suggested levels, this may be an indication that

instruction is not at the appropriate level (Archer & Hughes,

2011). Low levels of accuracy can indicate that the content is

too difficult and may be too frustrating for the students. If the

behavior analyst identifies this as a problem, he or she could

work with the teaching team to conduct a formal assessment

of the students’skills relative to the curriculum. Results of

such an assessment can be used to make curricular adjust-

ments as necessary.

Curriculum-based assessments (CBAs) are one way to

measure whether instruction matches students’skill reper-

toires. CBAs can aid instructors in placing students at the

appropriate level in the curriculum. They can also be used to

identify supplemental instruction and monitor progress

through the curriculum (Anderson & St. Peter, 2013). Some

examples of currently available CBAs include the Dynamic

Indicators of Basic Early Literacy Skills (DIBELS; Good &

Kaminski, 2002), the Scholastic Reading Inventory (SRI;

Scholastic 2007), and aimsweb (Pearson, 2012). These

CBAs assess individual student skills in the core instruction

areas of reading, writing, and math (Anderson & St. Peter,

2013; Johnson & Street, 2013).

Teachers can make modifications if CBAs show that the

curriculum is out of line with student needs. Curricular revi-

sion is an antecedent intervention that can help decrease dis-

ruptive behavior and increase academic success. Presenting

material that is at the appropriate level of difficulty can de-

crease the averseness of the task, thus decreasing the motiva-

tion to engage in disruptive behavior to escape the task. Like

increasing ASR, presenting tasks that are within students’

ability level (to which they are likely to respond effectively)

can lead to increased social reinforcement (e.g., praise and

attention). Several studies suggest that using assessment-

based curricular revision can decrease problem behavior in

both special and general education settings (Dunlap & Kern,

1996; Kern, Childs, Dunlap, Clarke, & Falk, 1994). As part of

this process, it can be helpful for the teacher and behavior

analyst to identify the components of the curriculum associat-

ed with problem behavior(Dunlap & Kern, 1996) and develop

a plan to modify them. The behavior analyst can then measure

whether the curricular changes sufficiently diminished the

problem behavior. If not, further modifications or a different

kind of intervention may be necessary. For example, addition-

al variables, such as task length (short vs. long) and choice of

task activities, may be incorporated to further improve class-

room behavior. Dunlap et al. (1991) suggest that easy tasks

should be interspersed with difficult tasks, choice should be

offered when possible, and that difficult tasks should be short

in duration and have functional relevance for the students.

Behav Analysis Practice

Their findings suggest that problem behavior during instruc-

tional tasks could be reduced and learning increased by revis-

ing curriculum presentation in this manner.

Feedback and Reinforcement

One reason why higher levels of ASR promote appropriate

behavior and learning is that ASR creates an opportunity for

feedback (Van Acker et al., 1996). Reinforcement of correct

responding is one form of feedback; correction of incorrect

responding is another. Perhaps not surprisingly, rates of teach-

er praise are highly correlated with ASR and OTRs. The more

opportunities teachers provide students to engage in reinforce-

able units of behavior, the more praise they tend to provide

(Cantrell, Stenner, & Katzenmeyer, 1977;Sutherland,Wehby,

& Yoder, 2002; Van Acker et al., 1996). Often, interventions

to increase either teacher praise or OTRs also affect rates of

praise (Lacy Rismiller, 2004).

Delquadri, Greenwood, Whorton, Carta, and Hall (1986)

point out that in many educational settings, the requisite

sources of reinforcement may be too impoverished to support

appropriate behavior. Researchers recommend that teachers

provide positive and corrective feedback in a ratio of at least

5:1 (Cook et al., 2017; Flora, 2000). The positive effects of

praise have been shown across age groups and across a spec-

trum of social and academic behavior (Blaney, 1983;Broden,

Bruce, Mitchell, Carter, & Hall, 1970; Connell, Carta, & Baer,

1993; Hall, Lund, & Jackson, 1968; Madsen et al., 1968;

Martella, Marchand-Martella, Young, & MacFarlane, 1995;

Poulson & Kymissis, 1988). Praise is most effective when it

is behavior specific (Brophy, 1981; Smith & Rivera, 1993;

Sutherland, Wehby, & Copeland, 2000). Increasing

behavior-specific praise has a positive effect on academic per-

formance and classroom behavior; for example, studies on

increasing behavior-specific praise have demonstrated in-

creases in on-task behavior (Sutherland et al., 2000), compli-

ance (Marchant & Young, 2001; Matheson & Shriver, 2005)

and academic engagement and achievement (Martens,

Hiralall, & Bradley, 1997) and decreases in disruptive behav-

ior (Reinke, Lewis-Palmer, & Merrell, 2008).

As with OTRs, naturalistic research has demonstrated in-

frequent use of praise in classrooms (Craft, Alber, & Heward,

1998). For example, White (1975) found that praise rates

ranged from 0.39 to 1.3 per minute. After second grade, praise

ratesdeclinedrapidly;praiseoccurredonlyevery5to10min

in high school (Thomas, Presland, Grant, & Glynn, 1978).

Other observational studies have shown praise rates ranging

from as low as 0.02 to 1.4 per hour (Shores et al., 1993;Van

Acker et al., 1996).

Another important aspect of feedback in the classroom is

error correction. Appropriate error corrections are immediate

and direct (Marchand-Martella et al., 2004). Effective error

correction provides information to promote correct

responding on subsequent attempts. Marchand-Martella et

al. (2004) suggest that teachers first demonstrate the correct

answer (otherwise known as providing a model), ask the stu-

dent to respond again to the original cue (otherwise known as

a test), and return to the item after providing OTRs on other

items (otherwise known as a retest). Another form of error

correction that can be useful in nonacademic tasks is a most-

to-least or least-to-most prompting hierarchy. This should also

be implemented immediately and directly. Context determines

which kinds of error corrections are most appropriate at the

given time.

To evaluate praise and feedback, the behavior analyst can

track teacher behavior following each active student response

(see Appendix: Followed by Positive Feedback;Followed by

Corrective Feedback). As described previously, the behavior

analyst can count the number of correct and incorrect student

responses. Each correct response is an opportunity for the

teacher to offer praise. Incorrect responses are opportunities

for error correction. The behavior analyst can use event re-

cording to measure the number of praise statements and error

corrections. These can be totaled and divided by the number

of correct and incorrect responses to obtain the percentages of

correct and incorrect responses followed by praise and error

correction, respectively. Alternatively, the behavior analyst

can count praise statements and divide this by the total obser-

vation time to determine the rate of praise. If the teacher is to

maintain the recommended 5:1 ratio of praise to error correc-

tions, the percentage of appropriate responses that should re-

ceive reinforcement is not always clear. A good rule of thumb

is to follow 80% of correct responses with praise. If a teacher

is doing this, he or she will likely meet the 5:1 ratio, especially

if correct responding is at 80% or better. To be maximally

effective, the praise rate should be somewhat close to the rate

of correct student responding. We suggest that teachers at-

tempt to make at least four praise statements per minute during

active instruction. Marchand-Martella et al. (2004) recom-

mend that 100% of errors be followed by an error correction.

Negative interactions between teachers and students are

correlated with problematic behavior. A reprimand is “acom-

ment or gesture by the teacher indicating disapproval of stu-

dent behavior”(Sprick, Knight, Reinke, McKale-Skyles, &

Barnes, 2010, p. 195). We conceptualize “don’t”requests as

reprimands (e.g., “Don’t run in the classroom!”and “Don’t

throw your pencil.”). Reprimands are different from corrective

feedback because they lack information about appropriate be-

havior. When assessing classroom variables, it can be helpful

to examine the ratio of praise statements to reprimands.

If a teacher wants to maintain appropriate social behavior, it

is helpful to praise social behavior in addition to academic

responding (Sutherland et al., 2000). It is often recommended

that teachers “catch students being good”to maintain good

classroom behavior. The behavior analyst may want to collect

data specific to this recommendation. This may not be

Behav Analysis Practice

captured in data collected on praise following ASR, as de-

scribed previously. Thus, we find it helpful to specifically

count praise for social behavior. The ratio of praise for social

versus academic behavior can be calculated by dividing the

number of praise statements for social behavior by the total

number of praise statements. If the teacher is rarely praising

social behavior, it may be helpful to recommend increasing

this.

Finally, because praise is most effective when it is behavior

specific, the behavior analyst may want to count the number

of praise statements that are behavior specific versus general

(i.e., non-behavior specific). Behavior-specific praise is a pos-

itive statement including the behavior for which the statement

is given. For example, “Great job! You’re right! The capital of

Michigan is Lansing!”and “Awesome job, André! I love the

way you got your book out right away!”are behavior-specific

praise statements. “Great job!”and “Awe s o m e jo b ! ”are gen-

eral. When the number of behavior-specific and general praise

statements has been counted, the percentage of behavior-

specific praise statements can be calculated. If this percentage

is low, the behavior analyst may want to work with the teach-

ing team to increase the specificity of their praise.

Implementing effective praise and feedback is a

consequence-based intervention that can influence both atten-

tion and escape-maintained disruptive behavior. For example,

a teacher increasing her praise rates for appropriate behavior

may decrease disruptive behavior by reinforcing alternative or

incompatible behavior, especially if she simultaneously de-

creases social attention following disruptive behavior.

Interventions to increase effective feedback can also influence

escape-maintained behavior by affecting motivating opera-

tions, particularly when effective feedback decreases the rela-

tive difficulty or averseness of the task. Although feedback

and praise are traditionally considered consequence-based in-

tervention, feedback can also function as an antecedent inter-

vention when it functions as a prompt for the next perfor-

mance and thus increases contact with positive reinforcement

(Aljadeff-Abergel, Peterson, Wiskirchen, Hagen, & Cole,

2017).

There are many ways to increase teacher praise rates, both

general and behavior specific. Effective interventions include

variations of performance feedback (visual and vocal), goal

setting, coaching, and video modeling (Duchaine, Jolivette, &

Fredrick, 2011; Hawkins & Heflin, 2011). For example,

Duchaine et al. (2011) used coaching, goal setting, and per-

formance feedback to increase behavior-specific praise in high

school teachers in an inclusion classroom. All three teachers in

the study increased behavior-specific praise, and two of the

three met their target goals. The teachers not only reported

seeing a positive change in their students but also maintained

behavior-specific praise rates after the coaching and feedback

were removed. Results from Duchaine et al. (2011)suggest

that this could be a very effective and powerful package for

increasing and maintaining teacher praise rates, even in the

absence of a long-term intervention.

Effective Demands and Transitions

The manner in which teachers deliver noninstructional de-

mands can affect the probability that students will comply.

Instructions should be specific, clear, phrased in a “do”for-

mat, and stated with precision. When teachers place demands,

specific is better than nonspecific (Harding, Wacker, Cooper,

Millard, & Jensen-Kovalan, 1994) and phrasing them as “do”

requests is better than “don’t”requests (Neef, Shafer, Egel,

Cataldo, & Parrish, 1983). Research has shown that formulat-

ing instructions as “precision requests,”where the instruction

is clearly phrased as an instruction (not a question), produces

less problem behavior and more compliance with task de-

mands (e.g., Mackay, McLaughlin, Weber, & Derby, 2000;

Yeager & McLaughlin, 2008). For example, the instruction

“Please sit down”is preferable to “Would you please sit

down?”or “Don’t run across the room.”Behavior analysts

should evaluate task demands as part of the overall classroom

context. As part of the assessment process, the number of

appropriately phrased noninstructional demands can be divid-

ed by the total number of noninstructional demands to obtain a

percentage of effective deliveries.

Transitions between activities often constitute complex de-

mands for students. This is because students are required to

complete several tasks in a row, often without discrete instruc-

tions before each task (Rosenkoetter & Fowler, 1986).

Classroom transitions between activities and locations are op-

portunities for problematic behavior, including off-task and

disruptive behavior. Observational studies suggest that stu-

dents spend between 18% and 25% of their time in transition

(Carta, Greenwood, & Robinson, 1987; Rosenkoetter &

Fowler, 1986; Sainato, Strain, Lefebvre, & Rapp, 1987;

Schmit, Alper, Raschke, & Ryndak, 2000). Teachers can pro-

vide both antecedent and consequence interventions to pro-

mote effective transitions.

Researchers recommend that transitions be predictable and

have a clear beginning and end signaled by auditory or visual

cues (Arlin, 1979; Embry & Biglan, 2008; Flannery & Horner,

1994;Schmitetal.,2000; Tustin, 1995). Some populations

may perform well with an advanced warning stimulus (e.g., 2

min prior to the transition; Cote, Thompson, & McKerchar,

2005). On the other hand, some individuals may not respond

well to early warnings (McCord, Thomson, & Iwata, 2001).

Effective transitions also minimize potential reinforcement of

noncompliance and reinforce compliance. For example, in the

case of transitions that take students away from leisure activ-

ities, it is important to ensure that access to toys or other

preferred items (potential reinforcement) is not maintained

during periods of noncompliance. Teachers should avoid

allowing escape from the transition demand by providing

Behav Analysis Practice

additional prompting if off-task behavior is observed, and re-

inforcement should be delivered for appropriate transitions

(e.g., praise or tokens; Cote et al., 2005).

Effective delivery of transitions can be directly observed

(see Appendix: Tra nsi tion s). For example, the behavior ana-

lyst can record each transition demand and tally whether it was

phrased as a specific “do”request. When noting an ineffective

request, the behavior analyst may want to record whether in-

structions were phrased in a general manner or as a “don’t”

request. By tracking transition instructions in this way, the

behavior analyst can pinpoint problems with how these re-

quests are delivered, thereby clarifying what changes may be

warranted. To do so, a task analysis of effective transition

steps can be created, and the behavior analyst can observe

transitions and count the number of effective transition strate-

gies in place. The duration of transitions can also be tracked to

determine whether they are too long. Depending on the tran-

sition activity (e.g., putting away a math book and taking out a

reading book while sitting at a desk vs. getting up, taking a

paper to the mailbox, finding a reading book on the shelf, and

sitting back down), the transition time should vary. The teach-

er and the behavior analyst should discuss how long each

transition should take and then measure the current duration

of the transition. Interventions to improve transitions are pri-

marily antecedent based in nature (see the following discus-

sion on response errors vs. signal errors). Like many of the

variables discussed thus far, an antecedent intervention that

results in more effective student behavior may also derive

the benefit of increasing reinforcement for behavior that is

incompatible with problem behavior. Ideally, teachers should

provide reinforcement for appropriate transitions and avoid

reinforcing inappropriate behavior during these times.

If teacher demands and transitions are not effective, and

students are displaying problem behavior during those times,

the behavior analyst and teacher must determine whether the

error is a response error (i.e., the student lacks the skill to

follow the instruction) or a signal error (i.e., the student is

not responding to a signal; Archer & Hughes, 2011). For re-

sponse errors, direct instruction on “how to follow directions”

or “how to transition”may be needed. Some research has

shown the positive effects of modeling, both in vivo and using

video models, on student compliance behavior while decreas-

ing problem behavior (Cihak, Fahrenkrog, Ayres, & Smith,

2010; Flannery & Horner, 1994). In these studies, students

are provided with instructions and a model of each action in

the sequence. Following the model, students have the oppor-

tunity to practice the transition sequence. Although creating

video models sometimes takes more time up front, teachers

rated the intervention as preferable to other behavioral inter-

ventions because individual students can get direct instruction

on transitions without removing the teacher from whole-class

instruction (Cihak et al., 2010). Flannery and Horner (1994)

also suggest that modeling provides predictability to students.

For signal errors, behavior analysts should work with

teachers to develop a consistent and predictable schedule of

tasks and consistent cues for when students should begin and

end work. Predictable schedules can either follow the same

routine every day or be posted in the classroom for the stu-

dents and teacher to see (e.g., text or pictorial). Signals can be

auditory (e.g., audible timers, bells, whistles), visual (e.g., a

flashing timer or hand signal), tactile (e.g., a vibrating timer),

or a combination (Archer & Hughes, 2011). Teachers should

select a signal that they will readily be able to use in most

environments and that all students can see, hear, or sense

and should provide reinforcement for immediate responses

to signals. Once a signal is selected, the teacher must ensure,

following the signal, that she or he is reinforcing responding.

This way, the signal will acquire discriminative stimulus (S

)

properties. If a clear signal is taught, consistently provided,

and followed by praise or other reinforcement, and students

are still not responding, there may be a motivational compo-

nent to the response error. In other words, the transition diffi-

culty may be what is commonly referred to as a “won’tdo”

rather than a “can’tdo”issue. Further analysis of the function

of problem behavior in these situations may be warranted. If

transitions are taking students too long, there are several strat-

egies in the literature that may prove useful, including modi-

fications to the environment and using one-way rotations,

clear signals, and reinforcement contingencies such as the

Timely Transitions Game (Guardino & Fullerton, 2014;

Yarbrough, Skinner, Lee, & Lemmons, 2004).

Conducting a Baseline Assessment

of the Classroom

In the preceding sections, we discussed each of the variables

we recommend consultants assess as part of baseline assess-

ments of the classroom. Next, we will introduce a data-

collection form for measuring these key variables through

objective observation. We have been using a version of this

form for years in making teacher observations and for giving

teachers feedback on their instruction and classroom manage-

ment. Parts of this form were adapted for general classroom

use from the direct instruction observation instrument by

Marchand-Martella, Martella, and Lignugaris Kraft (1997).

Although we have found it to be helpful for guiding clinical

decisions in our own cases, the validity of the data-collection

form has not been previously assessed in the context of a

controlled research study.

The data-collection form is included in the Appendix. This

data form allows for the recording of multiple variables simul-

taneously. The behavior analyst records demographic infor-

mation at the top of the form and includes the duration of

the observation. This is important to include, as it will aid in

calculating rate measures at the end of the observation. We

Behav Analysis Practice

have labeled each of the cells in this data form for discussion

purposes here. A blank copy of the data-collection form and

operational definitions for all the targets can be downloaded

for personal use in supplemental materials respectively. On the

left side of the form is a column with the labels Academic

Behavior,Social Behavior,andNoninstructional Demands.

Academic Behavior is subdivided into Group Questions &

Commands and Individual Questions & Commands. These

represent teacher behavior. Across the top, there are columns

for Correct Student Responses and Incorrect Student

Responses. These represent student responses to the

academic prompts (i.e., group and individual questions from

the teacher). Below the student responses, columns are

subdivided into Followed by Positive Feedback,Followed

by Corrective Feedback,andNot Followed by Corrective

Feedback. These represent the teacher’s response to the

students’correct and incorrect responses. Followed by

Positive Feedback is subdivided into Specific,General,and

Behavior to represent the different ways a teacher could

respond to a correct student response (specific praise,

general praise, or praising behavior rather than the academic

response). There is also a column in this section labeled Not,

which is used when there is no teacher response following the

correct response. Followed by Corrective Feedback is also

subdivided by Model-Test-Retest and Other Corrective

Feedback;nexttothisisacolumnforNot Followed by

Corrective Feedback. There is also a column in this section

for Followed by Positive Feedback, which is used when

incorrect responses are followed by positive feedback. These

comprise cells A through P and provide the behavior analyst

with the opportunity to count different types of feedback.

To score this top section, the behavior analyst must wait for

an entire unit of instruction (e.g., teacher’s question, students’

response, teacher’s response to the students’response) to oc-

cur before making a tally. For example, imagine this

interaction:

1. Teacher to the entire class: “Everyone, what is the capital

of Michigan?”(a question posed to the group).

2. Students in unison: “Lansing.”(a group response that is

correct).

3. Teachertoentireclass:“Yes, Lansing, correct!”(specific

praise for a correct academic response).

In this example, the behavior analyst would mark a tally in

cell A. The unit of instruction consisted of a question posed to

the entire group, followed by a correct response in unison,

followed by specific praise. If the teacher had simply

responded, “Correct!”(general praise for a correct academic

response), then a tally would be marked in cell B. If the teach-

er had responded, “Great job answering together!”(specific

praise but for social rather than academic behavior), a tally

would be marked in cell C (and also in cell Q; see the

following section). If the teacher did not provide any positive

feedback, then a tally would be marked in cell D to indicate

that no feedback was provided. Across all of these scenarios, if

the question had been posed to just one student (e.g., “Who

can tell me what the capital of Michigan is? Randy?”)rather

than the group, then the tallies would have been marked in

cells I through L, respectively.

Now, let us consider this teaching interaction:

1. Teacher to the entire class: “Everyone, what is the capital

of Michigan?”(a question posed to the group).

2. Students in unison: “Ann Arbor.”(a group response that is

incorrect).

3. Teacher to the entire class: “The capital of Michigan is

Lansing. Everyone, what is the capital of Michigan?”(an

error correction consisting of a correct model and a

“test”—an opportunity to respond correctly).

In this case, the behavior analyst would make a tally in cell

E. The test response also constitutes another teacher question,

and if the students now answer, “Lansing!”and the teacher

provides specific praise for this response, another tally would

also be made in cell A. now consider this scenario: The teacher

does a few more questions and then comes back to,

“Everyone, what is the capital of Michigan?”and the students

respond, “Lansing!”The teacher then says, “Awes om e ! I t is

Lansing!”In this case, we would record a tally in cell A and

put a tick mark across one of the tallies in cell E to indicate that

the teacher completed the error correction by also

implementing the retest component. If the teacher had used

another method of corrective feedback, a tally would be

marked in cell F.

In this scenario, if the teacher ignored the incorrect re-

sponse and went on to the next question, a tally would be

scored in cell G. On the other hand, if the teacher instead said,

“Right!”when the students made an error (yes, we have seen it

happen), a tally would be scored in cell H. Like for correct

student responses, if any of this had happened when a question

was asked of an individual student, these responses would

have been scored in cells M through P, respectively.

Praise statements provided by the teacher for social behav-

ior (e.g., “Thanks for taking your books out so quietly.”), as

opposed to academic responses, are recorded in cells Q and R,

depending on whether the praise is specific (e.g., “Great job

getting in your seats so fast!”) or general (e.g., “Great job!”or

“Wow ! ”). Reprimands, or corrections for problem behavior,

are recorded in cell S. For example, if the teacher said, “Stop

running!”or “I told you to stop poking Sheila!”a tally would

be made in cell S.

Below Social Behavior is a section where Noninstructional

Demands can be counted as Specific/Do requests (e.g.,

“Please take out your books.”)orNonspecific requests (both

nonspecific “do”requests, e.g., “Get ready for our next

Behav Analysis Practice

lesson,”and “don’t”requests, e.g., “Don’t sharpen your pencil

again!”). Here, the behavior analyst can record any teacher

demands not directly related to instruction. For example, if

the teacher says, “It’s time to get ready for lunch. Please put

your books away and stand by the door,”this would be re-

corded as a specific “do”request in cell T. If the teacher says,

“It’s time to get ready for lunch,”this would be recorded as a

nonspecific “do”request in cell U. If the teacher says, “Don’t

forget to put your books away before lining up for lunch,”a

tally would be recorded as a nonspecific “don’t”request in cell

U. For all of these requests, we suggest that the behavior

analyst mark the tally with a vertical line (i.e., “|”) and then

cross the line with a horizontal line to create a plus (i.e., “+”)if

the request is followed by compliance. In this way, the behav-

ior analyst can collect data on requests that produced compli-

ance and requests that did not result in compliance, which can

then be used to determine the percentage of each.

Finally, a Transitions checklist is provided, where the be-

havior analyst can check whether each transition had a clear

beginning signaled with an auditory or visual cue, whether the

teacher provided reinforcement for compliance with the tran-

sition and avoided reinforcement for noncompliance with the

transition (if any noncompliance occurred), and whether the

transition had a clear end point. There is also space for the

behavior analyst to record the duration of the transition, which

begins when the teacher initiates the transition and ends when

all of the students have completed the transition. These would

be recorded below cells V, W, and X for each transition ob-

served. There is space for observing three transitions.

The shaded boxes on the data-collection form indicate

spaces for the behavior analyst to summarize data from the

observation. The version of the data-collection form in the

Appendix shows the formulas for calculating summary data.

Another version including the recommended targets can be

downloaded for personal use in supplemental materials.The

numbers in the cells represent targets for which the teacher

should be aiming and against which the behavior analyst can

give recommendations to the teacher. If no numbers are

provided in the shaded cells, this indicates that there is not a

recommended target, per se, but the observed data are of

interest and should be considered.

As with any data-collection system, it takes time to learn

and become familiar with this data-collection form. However,

we have found this data-collection form very useful when

making teaching observations. It allows for an objective sam-

ple of several classroom variables that can have a direct influ-

ence on both academic and social performance. Having this

information may allow the behavior analyst to determine

whether changes should be made to the baseline classroom

context and to give the teacher direct, objective information

about the specific behavior he or she is displaying that may be

related to student behavior and learning in the classroom. This

may help the teacher make adjustments at the classroom level

to decrease disruptive behavior, increase learning opportuni-

ties, and avoid the need for an FBA and individualized behav-

ior support plan in some cases.

Discussion

Behavior analysts consulting in schools should be mindful to

not overlook systems-level variables that can contribute to

disruptive behavior in the classroom. In this article, we have

recommended conducting observations of baseline classroom

practices and assessing the following variables: (a) OTRs and

ASR, (b) appropriateness of curriculum, (c) feedback and re-

inforcement, and (d) effective instructions and transitions. For

each, we have provided guidelines on effective parameters

and recommendations for measurement of these antecedent-

and consequence-based practices. To assist with objective ob-

servation, we have provided a data-collection form and guid-

ance for structuring the assessment itself. Behavior analysts

may also seek out additional resources for capturing data dur-

ing specific activities, such as the direct instruction observa-

tion form by Marchand-Martella et al. (1997).

We suggest that behavior analysts consider including an

assessment of the variables described in this article even when

a referral singles out an individual student who engages in

disruptive behavior. Such an assessment can be incorporated

into an FBA, and if these systems-level variables show that

effective baseline classroom conditions are not in place,

classroom-level intervention may be warranted before consid-

ering an individualized intervention for problem behavior.

These interventions can help teachers provide antecedent

and consequence interventions designed to prompt appropri-

ate classroom behavior and effective learning. Toward this

end, we included a summary of interventions behavior ana-

lysts and teachers could use to improve performance and pos-

itively affect student outcomes at a “baseline”level.

The approach of assessing baseline classroom practices

presents several potential advantages. First, in some cases,

more intensive assessments and interventions may be avoided.

Establishing new classroom practices may effectively de-

crease problem behavior and increase appropriate behavior.

School resources are conserved when behavior change can

be achieved with minimal time spent on assessment and inter-

vention. Second, this strategy is consistent with the approach

of serving students in the least restrictive environment.

Interventions at the group level are often considered less re-

strictive than those at the individual level. Third, effective use

of the suggested classroom practices addresses academic per-

formance. In addition to decreasing target behavior, students

may gain access to effective learning practices. Finally, other

students in the class may experience academic and behavioral

benefits when new practices are implemented. Individual in-

terventions for target behavior often do not provide a direct

Behav Analysis Practice

benefit to peers. When implementing effective class-wide

practices, all students in the class can reap the educational

and prosocial benefits.

Although we believe this approach is generally appropri-

ate, some cases may warrant other tactics, particularly for

referrals for individuals who engage in significant problem

behavior. At times, it is clinically appropriate to conduct an

in-depth FBA, or specifically a functional analysis, before

targeting broader classroom variables. One example may be

when a student exhibits high-rate high-risk behavior. If target

behavior poses an immediate risk to self or others (e.g., self-

injury or aggression), it may be necessary to quickly identify

the function and intervene to eliminate the behavior. For each

referral, behavior analysts should exercise clinical judgment

and assess for factors that would require prioritizing other

procedures. Likewise, behavior analysts should consider the

context when assessing the recommended variables and plan-

ning interventions. Although the recommended parameters

for each classroom variable are based on research, adjustments

may be warranted in some situations. For example, the general

recommendation for the ratio of praise to reprimands is 5:1. It

is conceivable, however, that some students may require a

higher ratio (e.g., 10:1) to see the same benefit. We provide

these parameters as general recommendations, but behavior

analysts should consider their appropriateness for particular

contexts and individuals.

Hanley (2012) proposes that there is a humanistic rationale

for conducting individualized behavior assessments. He ar-

gues that it “dignifies”the problem behavior and involves

the client in the treatment development. For many students,

there is a similar humanistic value for including an assessment

of—and potential intervention on—classroom variables. This

practice ensures that teachers are providing a supportive envi-

ronment that reasonably sets the occasion for appropriate be-

havior and high academic performance. If students are still

struggling despite having best-practice first-line interventions

in place, then a functional-based intervention package is a

reasonable next step. Sometimes when best practices are not

in place, it may not make sense to create an individualized

intervention for a “target”student.

We have recommended four variables for assessment, but

there are other variables that contribute to desired classroom

behavior and academic success. It is possible that other vari-

ables should be included for analysis. Some possibilities in-

clude effective prompting strategies and visually displaying

positively stated classroom expectations. Likewise, there

may be student skills that should be targeted as preventive

measures. Some possibilities include functional communica-

tion, play and leisure skills, compliance, tolerating delay, and

tolerating restricted access to activities and items (Hanley,

Heal, Tiger, & Ingvarsson, 2007). It is currently unclear which

variables are the most critical for a baseline classroom assess-

ment. Future research should further refine these

recommendations by empirically evaluating the validity of

the proposed data-collection form and the variables it is meant

to assess.

Assessing and intervening on baseline classroom variables

is not a new idea. There is evidence, however, that best-

practice classroom methods are often not in place (Wehby,

Symons, Canale, & Go, 1998). It is important that behavior

analysts consider these variables when assessing undesired

student behavior. Explicit consideration of student–teacher in-

teractions and classroom ecology can lead the behavior ana-

lyst to prescribe interventions that positively impact both

classroom behavior and academic performance. The recom-

mended method constitutes a comprehensive approach to

school-based behavior consultation. Most importantly, it en-

sures that students have access to effective and therapeutic

learning environments.

Compliance with Ethical Standards

Conflict of Interest All authors declare that they have no conflicts of

interest.

Human Subjects This article does not contain any studies with human

participants or animals performed by any of the authors.

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Full-text available

Jan 2025

These procedures examined the impact of teacher–student ratios on staff and student behavior in a therapeutic day school. Ratios (teacher:student) of 5:4, 4:4, 3:4, and 2:4, involving the teacher, paraprofessionals, and four junior high students with autism were measured. Using momentary time sampling, environment, organization, student, and staff activity data were collected. Higher ratios correlated with increased student engagement and fewer behavioral interventions, while lower ratios led to decreased staff–student interactions and more behavioral interventions. • Authors discussed the impacts of teacher–student ratios in educational settings for children with autism. • State guidelines need revision to be sensitive to classroom composition. • Researchers have focused on academic outcomes and teacher perceptions. • Momentary time sampling is a practical approach for clinicians to use when objectively assessing variables in the classroom setting.

There’s a Time and Place for Timeout

Article

Apr 2023

Timeout has been used extensively within schools to address inappropriate behavior in children. In practice, timeout is not a single strategy; rather, it is a compilation of related practices. Some forms of timeout are research-based practices for modifying student behavior. However, others lack research support and frequently are misused, hindering academic performance and risking harm to children. This article reviews the different types of timeout and shares best practices to help teachers use timeout successfully.

Smart Education Under 5G+

Conference Paper

Nov 2021

Evaluating the Temporal Location of Feedback: Providing Feedback Following Performance vs. Prior to Performance

Article

Full-text available

May 2017

In order to make feedback as effective as possible, it is important to understand its function in the three-term contingency (TTC) and the impact of various factors involved in delivering feedback. Timing of feedback is one factor that can affect the impact of feedback on learner’s behavior. An analysis of timing of feeback may help us getting closer to better understanding how feedback functions in the TTC. The purpose of this study was to evaluate the effects of feedback at different temporal locations. Specifically, feedback was provided either immediately (a) after a teaching session or (b) before the following teaching session, on teaching performance of undergraduate psychology students. The results indicated that feedback provided before the teaching session was more effective in improving teaching skills than feedback that was provided after the session. These findings suggest that feedback may function primarily as an antecedent to future performance and not necessarily as a consequence for past performance. However, the behavioral mechanism that explains these results is not yet clear. Future studies should investigate this further by manipulating the content of feedback prior to the teaching performance.

Systems for basic instruction: Theory and applications

Article

Full-text available

Jan 1978

Increasing Teacher Praise and on Task Behavior for Students With Autism Using Mobile Technology

Article

Full-text available

Dec 2015

This study investigated the effects of using mobile technology as an intervention to increase the amount of praise delivered by teachers to low-performing students with autism. Additionally, the study also investigated the impact that praise would have on the on-task behaviors of these students. Participants included five elementary students with autism, two teachers, and two paraprofessionals. Results indicated that prompts from the mobile devices were effective in increasing the amount of praise provided by each of these educators. However, the extent to which teachers’ contingent praise impacted student on-task behaviors was less clear. Implications for future research and practice are discussed.

The Right to Effective Behavioral Treatment

Article

Oct 1988

We propose that individuals who are recipients or potential recipients of treatment designed to change their behavior have the right to: (1) a therapeutic environment, (2) services whose overriding goal is personal welfare, (3) treatment by a competent behavior analyst, (4) programs that teach functional skills, (5) behavioral assessment and ongoing evaluation, and (6) the most effective treatment procedures available.

Application of an ecobehavioral approach to the evaluation of early intervention programs

Article

Jan 1987

Functional Analysis With Typically Developing Children: Best Practice or Too Early to Tell?: In Response to

Article

Dec 2013

Hanley (2012) provided a guide to practitioners and researchers in the conduct of functional assessment for problem behavior, citing a body of empirical work to support his view. Much of the work Hanley drew upon was conducted with individuals with developmental delay in hospitals or outpatient clinic settings. In this paper, we argue that the guidelines he put forth are not necessarily applicable to work in schools or with typically developing individuals. We examine the evidence supporting use of functional analysis with typically developing children, emphasizing school-based consultation, and conclude by recommending a tiered assessment approach from least to most intensive based on the needs of the individual child.

Evaluating the Impact of Increasing General Education Teachers Ratio of Positive-to-Negative Interactions on Students Classroom Behavior

Article

Mar 2017

The aim of this study was to isolate and evaluate the impact of increasing teachers’ ratios of positive-to-negative interactions with their students. Training teachers on the 5:1 ratio was evaluated using a randomized-block pre–post control design with general education classroom teachers (N = 6) that were characterized by a higher ratio of negative-to-positive interactions between students, as well as low academic engagement and high disruptive behaviors. Teachers in the intervention group were trained on the 5:1 ratio, instructed to wear a device that prompted them on a VI 5-min schedule to deliver specific praise, approval statements, and positive non-verbal gestures to specific students exhibiting expected behaviors or the entire class as a whole, and completing a self-monitoring chart to increase their awareness of their ratios. Results revealed that students in the intervention group displayed significantly fewer disruptive behavior problems and higher academic engaged time when compared with students in control classrooms. Social validity results also revealed that teachers found the strategy feasible, acceptable, and effective. Limitations of this research, including the small-scale nature of this study, and future directions for teacher training, retention, and schoolwide universal prevention are discussed.

Aversive Stimuli in Academic Interactions between Students with Serious Emotional Disturbance and their Teachers

Article

Aug 1993

This manuscript presents a review of the growing body of research concerning the influence of negative reinforcement on student and teacher behaviors. Negative reinforcement strengthens behaviors that result in escape from, or avoidance of, aversive stimuli. It is argued that negative reinforcement is a variable in the academic interactions of students with serious emotional disturbance and their teachers, Although both desired and undesired behaviors may be outcomes of negative reinforcement, because of the academic deficiencies of students with serious emotional disturbance and the instructional behaviors of their teachers, the escape and avoidance behaviors exhibited by these students more often may be undesired. The authors call for research to investigate sources of aversive stimuli within academic interactions between teachers and students with serious emotional disturbance as well as the potentially aversive stimuli present in academic activities. Implications for teaching practices also are presented.

Opportunity to respond and academic achievement

Article