David W. Barnett , Rebecca L. Rahschulte and Julie Q. Morrison · 2 David W. Barnett, Rebecca L. Rahschulte and Julie Q. Morrison Significant advances have been realized in the development

Chapter

BEHAVIORAL CONSULTATION AND DEVELOPING

SUPPORT CONDITIONS FOR CHANGE AGENTS:

INTERVENTION ACQUISITION AND FLUENCY IN

MULTI-TIERED MODELS

David W. Barnett*, Rebecca L. Rahschulte and Julie Q. Morrison University of Cincinnati

ABSTRACT

Behavioral consultation has the goal of establishing or improving real world interventions

through systematic problem solving. Yet, much evidence exists that demonstrates how

problematic this may be for students’ academic and social challenges. Following a brief

explication of behavioral consultation, including its role in multi-tiered services, we

examine the pervasive problem of intervention adherence (also known as intervention

fidelity) through key ideas from the instructional hierarchy (Haring & Eaton, 1978). The

basics of our recommendation are the introduction of acquisition, performance feedback,

and fluency conditions based on a triage model of risk in plan development to support

teachers in acquiring intervention skills. The goal is to better support those carrying out the

interventions through higher quality intervention implementation. Failed or poorly

implemented interventions may be due to the interventionist’s skill, concerns or other

combined performance factors. Intervention skill applies to questions of teachers, or others, moving from unskilled intervention performance to acquiring skilled intervention

performance that is maintained and can be adapted to new situations that undoubtedly arise.

Failed interventions may have lasting negative consequences for all involved, including

students, teachers, and schools. Acquisition of intervention skill requires support and

feedback, an especially important role for behavioral consultants.

* Corresponding author: David W. Barnett, University of Cincinnati, College of Education, Criminal Justice, and

Human Services, School of Human Services, School Psychology Program, P.O. Box 210068, Cincinnati, OH

45221-0068. E-mail: [email protected].

David W. Barnett, Rebecca L. Rahschulte and Julie Q. Morrison 2

Significant advances have been realized in the development of evidence-based

interventions to address school-based academic and behavior concerns in the past two decades.

Evidence-based interventions coupled with the use of data-based decision making to best match

student needs with appropriate interventions within multi-tiered systems of support have had a

meaningful impact on how students are served in schools. The use of empirically-based

interventions is an expected part of a teacher’s professional role. Despite these advances,

progress in understanding and appreciating the conditions needed to support teachers’ use of

such interventions within the natural classroom environment remains the missing piece of the

equation. Indeed, little has changed in the decades since Gresham (1989) bought to light the

inadequacies of the “consult and hope” (p. 48) approach to describe the practice of providing

consultation services with little or no attention for follow-up to ensure teachers (or other

interventionists) implement the intervention with fidelity.

Intervening to produce behavior change at a student level requires attention to critical

contextual, behavioral, and person-specific variables that influence a student’s performance of

academic or behavior/social skills (Bandura, 1986). However, just as it is important to address

the unique perspectives and contributions of the student and the environment that contributes

to a student’s performance (Sheridan et al., 2012), that same level of rigor needs to be dedicated

to understanding adult behavior change in the context of school-based consultation. Research

indicates that teachers’ knowledge and skill in implementing interventions in the classroom

varies considerably from teacher to teacher (Piasta, McDonald Conner, Fishman, & Morrison,

2009). A disconnect is perpetuated when we fail to recognize that school-based consultation

requires a similar systematic and analytic approach to understanding the context and specific

variables that influence adult behavior change relevant to a teacher’s implementation of a new

intervention. The principles of behavior change and skill development widely recognized at a

student level apply equally well to teachers learning a new instructional strategy or behavior

intervention.

BEHAVIOR CONSULTATION

Behavioral consultation is an indirect service-delivery model featuring structured data-

based problem-solving between a consultant and a teacher or parent consultee for the purpose

of implementing evidence-based interventions in the school or home (Sheridan & Kratochwill,

2008; Sheridan, Kratochwill, & Bergan, 1996). Problem situations are identified, operationally

defined, and analyzed for the purposes of developing a hypothesis-driven intervention plan for

the resolution of the specific concern (Kratochwill & Bergan, 1990). Behavioral consultation

is collaborative in nature. Frequent, context-specific collaborative interactions between the

teacher/parent and consultant produce mutual understanding and responsibility for problem

solving (Sheridan et al., 2012). The goals of behavioral consultation are two-fold: (a) to address

the mismatch between the expected level of performance and a student’s current and

problematic performance in order to resolve the problem situation in the natural setting, and (b)

to build the capacity of the teacher or parent to increase the probability of successful prevention

and intervention of future problem situations.

Throughout the collaborative problem-solving process, consultants employ a variety of

strategies to support the teacher/parent consultees as they work together to specify and prioritize

Intervention Fluency 3

the concerns to be targeted during consultation. A data-based problem identification statement

is the focus of problem analysis to determine variables that contribute to and maintain the

problem situation. In empirically-based and multi-tiered school services, data-driven, behavior

analytic principles embedded in behavioral consultation inform the practices of examining

baseline data, the identification of antecedents and consequences, and the design of a research-

based intervention functionally linked to the hypothesis developed during problem analysis. An

intervention plan is designed collaboratively with the consultant and teacher/parent consultee

for use in the natural classroom or home setting with the objective of successful

implementation, usually maximizing intervention adherence or making needed changes in

plans as an intervention is implemented. Data are gathered throughout the problem-solving

process and the consultant and consultee use the data to evaluate the intervention’s

implementation and effectiveness and plan for its continuation or need for modifications.

The attainment of desired student outcomes through behavior consultation rests in no small

part of the successful implementation of the plan by the teacher or consultee. The degree to

which intervention plan implementation moderates the effects of an intervention has often been

unknown (Sheridan et al., 2012), leading to the risk of casting aside an otherwise effective,

beneficial intervention because it was not implemented accurately. Indeed, the research on

effective interventions for students is far more substantial than the research regarding

behavioral consultation approaches needed for establishing and sustaining accurate

intervention implementation in field settings (Noell, Duhon, Gatti, & Connell, 2002; Sheridan

& Gutkin, 2000).

The Problem: Challenges of Real World Services

Problems with intervention implementation in natural settings have been greatly

minimized. The degree to which an intervention is implemented as planned is known as

intervention adherence, intervention fidelity, or treatment integrity (Gresham, Gansle, Noell,

Cohen, & Rosenblum, 1993; Noell et al., 2000). Although it is widely recognized that lower

levels of intervention adherence undermines the intervention’s potential direct and positive

impact on student outcomes (Sanetti, & Kratochwill, 2009a), the degree to which this difference

in intervention adherence in the early stages of initial implementation and over time affect the

effectiveness of the intervention is mostly undocumented in practice (Sheridan et al., 2012).

Measuring intervention adherence is critical to making valid conclusions regarding the

intervention effects (Shadish, Cook, & Campbell, 2002). Yet, research suggests that

implementation or achieving intervention adherence in consultation is frequently more

challenging than expected (Foxx, 1996; Noell, Duhon, Gatti, & Connell, 2002). That is,

implementation of empirically-based and real world interventions may require additional and

skilled consultation supports; it is easier to discuss and develop plans in contrast to doing them.

Ample research suggests that the delivery of skilled intervention plans may not simply

spring forth following consultation (Noell, Witt, Gilbertson, Ranier, & Freeland, 1997; Noell

et al., 2000; Noell, Gresham, & Gansle, 2002; Noell, Witt, Slider, Connell, Gatti, Williams,

Koenig, Resetar, & Duhon, 2005; Witt, Noell, LaFleur, & Mortenson, 1997). For example, in

a study that involving the direct observation of intervention adherence following behavioral

consultation, Wickstrom, Jones, LaFleur, and Witt (1998) reported 33 elementary teachers


implemented behavioral interventions with less than 10% integrity after agreeing to use an

intervention and receiving explicit verbal and written instructions.

EMPIRICALLY-BASED BEHAVIORAL CONSULTATION STRATEGIES

TO SUPPORT INTERVENTION ADHERENCE

Strategies to enhance intervention adherence have generally focused on proactive

approaches to intensifying intervention planning and the use of performance feedback

conditions during intervention delivery. Most prominent among the proactive approaches are

the use of staff development sessions, manuals, intervention scripts (Erhardt, Barnett, Lentz,

Stollar, & Reifin, 1996), and structured intervention planning protocols (Sanetti & Kratochwill,

2009b). However in practice, adherence resulting from traditional methods of preparing

consultees for intervention implementation has been minimal when examined (Noell et al.,

2002). A notable exception has been the focus on consultant’s use of performance feedback to

promote and sustain intervention adherence among teachers has proliferated within the

consultation literature (Solomon, Klein, & Politylo, 2012).

Performance feedback is an evidence-based practice of providing information, knowledge

and data to promote acquisition or maintenance of new behaviors (Mortensen & Witt, 1998).

The research regarding the effectiveness of performance feedback within behavioral

consultation to reinforce teacher behavior change is compelling (e.g., Codding, Feinberg,

Dunn, & Pace, 2005; Codding, Skowron, & Pace, 2005; Colvin, Flannery, Sugai, & Monegan,

2009; DiGennaro, Martens, & McIntyre, 2005; Hemmeter, Snyder, Kinder, & Artman, 2011;

Leblanc, Ricciardi, & Luiselli, 2005; Matheson & Shriver, 2005; Mortensen & Witt, 1998;

Myers, Simonsen, & Sugai, 2011; Noell et al., 2000; Noell et al., 2005; Reinke, Lewis-Palmer,

& Martin, 2007; Scheeler, McAfee, Ruhl, & Lee, 2006; Slider, Noell, & Williams, 2006). In a

comparison of three consultation follow-up conditions, performance feedback was found to be

superior to a brief weekly interview condition and a weekly interview combined with an

emphasis on social influence (e.g., emphasis on the commitment to implement the

intervention), demonstrating an effect on teacher intervention adherence that can be

characterized as a large effect (Noell et al., 2005). Given the evidence in support of performance

feedback to support teacher skill acquisition and fluency, several researchers have identified a

need for further study into the amount and frequency of performance feedback to maximize

intervention efficiency and cost-effectiveness (Myers et al., 2011; Reinke et al., 2007; Slider et

al., 2006), the effectiveness of performance feedback under varying levels of intervention

complexity (Solomon et al., 2012), and the application of a continuum of supports to increase

intervention adherence (Myers et al., 2011; Solomon et al., 2012).

In summary, steps for successful consultation, especially in high risk situations (Barnett,

Hawkins, & Lentz, 2010), can include conditions whereby the intervention skills are acquired,

practiced to fluency (Haring & Eaton, 1978) and tried out including planned trouble shooting

(VanDerHayen & Witt, 2004). Why? Even seemingly straightforward intervention support

consultations may be much harder to implement than commonly expected (Noell et al., 2005).


MULTI-TIERED CONTINUUM OF SUPPORT FOR

INTERVENTION ADHERENCE

A multi-tiered system is the term commonly applied to represent the logic of integrated

response-to-intervention (RTI) and positive behavior supports (PBS) in elementary and

secondary school buildings (Sugai & Horner, 2009). The application of multiple tiers to

customize consultation through the use of a continuum of supports for increasing and sustaining

intervention adherence marks the most recent advance in behavioral consultation.

Rather than targeted variables emerging through the problem-solving process, some key

variables are set in advance through the tiered model instituted by schools. For example,

universal screening may create opportunities for intervention supports and consultation that

have never existed in a school. By using systematically identified literacy, math, and social

variables, students may be selected that would have been missed in traditional consultation.

Schools and teachers may vary considerably in their use of key data and intervention supports

(e.g., VanDerHeyden & Witt, 2005). Recognizing that the type and frequency of performance

feedback needed for intervention adherence varies by teacher, Gilbertson, Witt, LaFleur

Singletary, and VanDerHeyden (2007) examined the effects of response-dependent

performance feedback on intervention adherence and child academic performance within a

peer-tutoring intervention for math. Low intensity consultation supports involving verbal

training, the provision of written instructions, and role playing constituted the first phase.

Decision rules were established for matching teacher intervention adherence to consultation

supports of increasing intensity. When intervention adherence was stable and below 100% for

at least three consecutive sessions, teachers were provided increasingly intensive consultation

support that was systematic and progressive. Performance feedback was not provided when

teachers implemented the intervention with 100% integrity. Findings from the study indicate

individual differences in the level of support needed from the consultant to obtain the criterion

for intervention adherence and that intervention adherence was not maintained consistently

across teachers when performance feedback was faded (Gilbertson et al., 2007). These results

may suggest a need to consider the function of the performance feedback within the

consultation process (Balcazar, Hopkins, & Suarez, 1985). A more recent study utilized a multi-

tiered approach applied to adult behavior within a professional development context to vary

performance feedback across three tiers of increasingly intensive consultation supports, which

in turn impacted teacher responsiveness in the classroom (Myers et al., 2011).

The objective of a multi-tiered system is to create a fluid and flexible system of support

services to maximize students’ progress toward academic and behavioral goals (Kupzyk, Daly,

Ihlo, & Young, 2012). The basic features of this multi-tiered framework include: (a) universal

screening procedure and frequent progress monitoring; (b) data-based decision making and

problem solving to determine if students require more or less intensive interventions; (c) a

continuum of evidence-based supports and interventions provided to students based on risk-

related indications of prevention and needed intensity, along with decision points to determine

if students are performing at or below expectation; (d) structures and procedures to ensure

implementation fidelity throughout the system (Ardoin, 2006; Barnett, Daly, Jones, & Lentz,

2004; Christ & Poncy, 2005; Fuchs & Fuchs, 2006; Gresham, 2004).

In the context of behavioral consultation with teacher skill acquisition and fluency as the

target behavior, teachers required different levels of supports varying by intensity to obtain the


criterion for intervention adherence (Myers et al., 2011). Following the typical schoolwide

positive behavior support training (Tier 1 intervention), teachers who did not attain the

performance criteria received targeted training support (Tier 2 intervention), which consisted

of (a) brief consultation with a rationale and examples of specific, contingent praise; (b) data

on before and after ratios of positive to negative interactions with students; and (c) weekly

praise from the research contingent on improved rates of specific, contingent praise statements.

More individualized consultation assistance (Tier 3 intervention) was provided to the teachers

based on their response to the Tier 2 training supports (Myers et al., 2011). The outcomes of

this study, demonstrating teacher performance gains resulting from a systematic, customized,

data-driven approach to consultation, are consistent with previous research showing that greater

intensity direct training strategies (i.e., classroom rehearsal and feedback) resulted in superior

gains in intervention adherence than the lower intensity indirect training strategies (i.e., verbal

instruction) typically used in consultation (Sterling-Turner, Watson, & Moore, 2002).

THE INSTRUCTIONAL HIERARCHY FOR TEACHER INTERVENTION

IMPLEMENTATION SKILLS

The Instructional Hierarchy model (Haring & Eaton, 1978) outlines the stages that an

individual progresses through when learning a new skill, as well as the changes in instructional

practices that should accompany each stage of learning. This learning hierarchy details the

increasing complexity of academic and behavioral skills displayed as an individual transitions

through a series of four stages: (a) acquisition, (b) fluency, (c) generalization, and (d)

application/adaptation. The instructional hierarchy has served as a useful framework for

identifying students’ skill level and targeting a need for skill development with appropriately

matched academic interventions (Daly, Lentz, & Boyer, 1996; Daly, Martens, Barnett, Witt, &

Olson, 2007; Daly, Witt, Martens, & Dool, 1997).

As this model has integrated itself into research and practice, the focus has been the way

students develop skill proficiency in academic settings, instructional methods to increase skill

fluency, and assessment methods to measure fluency (e.g., curriculum based measurements).

Yet the instructional hierarchy model applies to the learning of new skills in all individuals,

including teachers who are the developing skills needed to implement an intervention for which

they are unfamiliar. Teachers develop intervention skill acquisition as they begin to learn the

steps required for implementation and begin to display those steps accurately.

The instructional hierarchy also has application in conceptualizing how a teacher’s skills

in intervention delivery move from initial acquisition to fluency over time in the

implementation of the intervention. Acquisition is defined as the “period between the first

appearance of the desired behavior and the reasonably accurate performance of that behavior”

(Haring & Eaton, 1978, p. 25). Fluency is the “combination of accuracy plus speed of

responding that enables competent individuals to function efficiently and effectively in their

natural environments” (Binder, 1996, p. 163). Although intervention adherence pertains to the

accuracy in which a teacher implements an intervention as intended, intervention skill fluency

is also required of teachers to successfully implement new and increasingly complex

intervention plans in the busy classroom setting.


Skilled teachers demonstrate the ability to both generalize their instructional practices as

well as adapt those practices as needed. Generalization would occur when the teacher is capable

of broadening implementation to include other settings, students, or other circumstances. This

might include the ability for the teacher to integrate intervention procedures “on the fly” when

noticing that a student might be struggling with an academic skill during instruction, or when

displaying a challenging behavior that would require a teacher to intervene quickly with

evidence-based intervention strategies that have worked for students in the past. Adaptation of

an intervention is reflected when the teacher makes modifications to the intervention to meet

the demands of a new situation (e.g., different school environment, student characteristics,

availability of resources, etc.).

BUILDING INTERVENTION IMPLEMENTATION FLUENCY

THROUGH BEHAVIORAL CONSULTATION

Intervention implementation fluency is achieved when the intervention is implemented

both accurately and with appropriate instructional pace. Intervention delivery transitions from

initial skill acquisition to fluency over time, and yet this transition is often neglected in research

and practice. Consultation methods that capitalize on acquisition and fluency-building

strategies highlighted within the instructional hierarchy model can be used to increase teacher

fluency with intervention implementation (i.e., accuracy and speed of responding).

Using the instructional hierarchy model as a framework, the intervention acquisition stage

begins with direct training strategies on intervention procedures for teachers. During the initial

acquisition stage, instructional procedures should utilize demonstration, modeling, and cueing

in combination with performance feedback (Haring & Eaton, 1978). Intervention adherence is

a measure of the accuracy of intervention skill acquisition.

Intervention skill fluency is attained when both accuracy and speed of responding have

been achieved and the implementation is performed at a level that ensures maintenance of the

intervention procedures. Although instructional pace has long been recognized as an important

instructional variable (Munk & Repp, 1994), few studies have assessed instructional pace as it

applies to the implementation of complex intervention procedures. Studies that have examined

instructional pace have documented the importance of this variable in decreasing problem

behaviors and improving accuracy in student academic responding (Carnine, 1976; West &

Sloane, 1986). Intervention skill fluency necessarily includes measures of skill accuracy

(intervention adherence) and speed of responding (i.e., duration of intervention session

implementation).

Fluency researchers have suggested that at least 70% of instructional time be devoted to

skill practice (Binder, 1996). Yet, teachers are generally not afforded the opportunity to practice

new intervention skills to mastery given the competing demands on teacher’s time generally

permits only a brief overview of an academic or behavior intervention, perhaps accompanied

by verbal and/or written directions. The continuum of consultation services for teachers

learning new skills do not always include the supports found be research to best support skill

fluency, which includes: (a) brief, repeated practice opportunities with modeling, feedback, and

reinforcement; (b) monitoring and charting of skill performance; and (d) performance goals

(Chard, Vaughn, & Tyler, 2002; Martens et al., 2007).


AN EXAMPLE: APPLYING THE INSTRUCTIONAL

HIERARCHY TO BUILDING TEACHER INTERVENTION SKILL

IN BEHAVIORAL CONSULTATION

The following hypothetical case study, adapted from research (Raschulte, Morrison, &

Barnett, 2012), illustrates how the instructional hierarchy model can provide a flexible, fluid,

and data-driven continuum of consultation supports to build teacher intervention skill fluency

in implementing a classroom-based math intervention package.

The Intervention: Detect, Practice, and Repair

Significant numbers of students with math problems may place a school as well as the

students at risk (USDOE, 2008; VanDerHeyden & Witt, 2005). Detect, Practice, and Repair

(DPR) is a multi-component math fact fluency intervention program that has been shown to

increase automaticity with math facts across various mathematical functions. Its inclusion of

basic intervention components often linked to acquisition of mathematics and other educational

objectives makes it a good example of interventions used to reduce early mathematics risk

(Axtell, McCallum, Bell, & Poncy, 2009; Poncy, Skinner, & Axtell, 2010; Poncy, Skinner, &

O’Mara, 2006). The DPR intervention’s initial complexity illustrates needed support conditions

provided by consultants to build intervention skills, as well as the ease at which teacher fluency

can develop overtime with these intervention procedures given supports. In addition, research

has established an approximate duration of implementation for this intervention, which has not

been clearly established with other intervention procedure. Poncy and colleagues (2010)

suggest that DPR implementation takes approximately 12 minutes; therefore, a criterion

duration can be established that is based on research findings.

A single Detect, Practice, and Repair (DPR) (Poncy & Skinner, 2006) intervention session

involves the progression through three intervention phases of detect, practice, and repair. The

first phase of the DPR intervention involves the detection of math facts that are non-automatic.

The next phase requires practice of non-automatic facts through a Cover, Copy, and Compare

(CCC) procedure. The final phase incorporates a timed fluency drill followed by feedback

provided to the student through self-graphing procedures.

Intervention training for the teacher. Prior to implementation of the DPR intervention,

the teacher is provided instruction on the intervention by the consultant using direct training

strategies, which include modeling of intervention procedures, opportunities for practice

through role-play, and performance feedback. The acquisition of intervention procedures by

the teacher is assessed through use of the intervention adherence checklists utilized during role-

play. Once the teacher achieves a criterion level of accuracy on the intervention adherence

checklist during role-play procedures (i.e., 80%), the student training stage begins.

Intervention training for the students. Following teacher training, student participants

are instructed on the DPR intervention procedures by both the consultant and trained teacher.


Student acquisition of intervention procedures are assessed through use of a student

intervention adherence checklist. Student intervention adherence is then compared to the

established criterion for student intervention adherence (i.e., 80%) prior to intervention

implementation.

Acquisition stage. An acquisition stage begins when the teacher and student participants

achieve a criterion level of accuracy with intervention procedures, assessed through

intervention fidelity checklists utilized during the training stage. On-going data-based decision

making during this initial implementation stage is based on the following measures: (a)

intervention adherence data (both teachers and students), (b) duration of the implementation

session, (c) curriculum-based measurement of student math fact fluency, and (d) a measure of

opportunities to respond per intervention session (active student practices calculated by adding

the number of opportunities to respond during the detect phase, the practice phase, and the

repair phase).

Performance feedback regarding implementation skill is provided to the teacher based on

the measure of intervention adherence. Specific intervention steps lacking adherence are

modeled for the teacher, and opportunities for role-playing, and performance feedback are

provided.

Fluency stage. This phase begins when the teacher demonstrates fluent implementation of

the DPR intervention. This is evident by intervention implementation accuracy (i.e., >90%

accuracy on intervention fidelity assessments) and appropriate speed of intervention delivery

(i.e., DPR intervention implemented within 11-13 minutes). Collection of progress monitoring

data would continue as documented in the acquisition stage above. If the speed of delivery falls

below the established fluency criterion, the teacher would receive further consultation supports.

Illustrative findings. To demonstrate what a data set might look like for the above

scenario, Figure 1 depicts the teacher transition from acquisition of the intervention steps to

intervention implementation fluency. This transition is documented through the collection of

intervention fidelity data, as well as duration data highlighting the length of each intervention

session in minutes and seconds. As represented on the graph, “fluency” is obtained during the

fifteenth instructional day and maintained throughout the ensuing intervention sessions.

Figure 2 depicts the curriculum based measurement (CBM) single-digit multiplication

math fact fluency rates of a small-group of students participating in a Detect, Practice, and

Repair (DPR) intervention. Median opportunities to respond per intervention session are co-

plotted on this graph to examine a possible co-relationship between these variables (i.e., teacher

fluency, opportunities to respond, and math fact fluency).

Figure 1. Hypothetical graph documenting a teacher’s progression from intervention acquisition to intervention implementation fluency as represented by

intervention fidelity data and implementation duration data.

Figure 2. Hypothetical graph documenting academic outcomes linked to teacher’s acquisition and fluency of implementation. Academic outcomes include

median student opportunities to respond and math fact fluency rates as measured through curriculum based measurement procedures.


In an effort to demonstrate the impact of acquisition versus fluency on student outcomes,

this research could also examine the student rate of improvement obtained during the teacher

acquisition (pre-fluency) stage versus the student rate of improvement once teacher fluency has

been achieved. In this hypothetical example, the rate of improvement during the fluency stage

was 1.38 correct digits per minutes per instructional day whereby the rate of improvement

during the acquisition stage was .72 correct digits per minute per instructional day. This

demonstrates that teacher fluency in this hypothetical example resulted in more rapid daily

gains in student academic outcomes.

Other Benefits of Fluency in Application

Increased teacher fluency will result in students having access to more opportunities to

respond in a given intervention or instructional session. Research has shown that increased

opportunities to respond is of critical importance to both increases in academic achievement

and on-task behavior, as well as decreases in disruptive behavior (Sutherland, Alder, & Gunter,

2003).

It is relatively easy to envision the potential student benefits that could arise from teacher

fluency with intervention implementation (i.e., more opportunities to respond, better student

academic outcomes, higher student engagement); however, teacher benefits are also

conceivable. While in a state of learning, acquisition is effortful resulting in the potential for

stress (i.e., new requirements for the implementation of a complex intervention that exceed the

teacher’s perceived resources to cope). This can be compounded by the fact that some

interventions are incredibly complex to facilitate and can be challenging for teachers to

implement in an environment that is competing for their time and resources (Noell et al., 2005).

As acquisition evolves into fluency of implementation, the procedures become more automatic

and less cumbersome for the teacher to deliver, which in turn reduces the stress associated with

implementation (Binder, 1996).

According to the instructional hierarchy model, learning progresses from fluency to

generalization, and finally to the ability to engage in adaptation. Therefore, in theory, teacher

fluency would progress into a state of generalization whereby the teacher is capable of

broadening implementation to include other settings, students, or other circumstances. Finally,

adaptation of an intervention would become feasible for the teacher, who would then be capable

of adjusting the intervention to meet the demands of a new situation (e.g., different school

environment, student demographics, availability of resources, etc.).

CONCLUSION

Consultation and Conditions of Intervention Supports

Educators and educational leaders want to ensure that children reach a level of academic

mastery that allows them to fluently use, generalize, and adapt their newfound knowledge and


skills to situations that exceed the walls of the classroom. It only makes sense that the

instructional hierarchy model, commonly used to describe the progression of learning within

student populations, would also apply to adult populations (e.g., teachers) learning to acquire

new knowledge and skills (e.g., intervention procedures, instructional curricula). Future

research should attempt to explore this phenomenon with teachers who are learning the

procedures and implementation guidelines for new intervention programs, with the

examination of their progression of learning from acquisition to fluency. It is hypothesized that

teacher fluency with intervention procedures will have a direct impact on the rate of student

opportunities to respond, which in turn will impact students’ academic achievement. Future

research may also consider the examination of factors beyond fluency, such as the

generalization of intervention procedures to other settings or students. Once acquisition,

fluency, and generalization of intervention implementation occurs, research might consider the

appropriateness of teacher adaptation of intervention procedures to align with the needs of a

given student population or instructional environment.

Situational Risk and Consultation Supports

An appraisal is needed of costs of appropriate consultative support weighed against

positive outcomes of teacher and student success. Unfortunately, research documents

adherence problems even with relatively simple empirically-based interventions (Noell et al.,

2000). School teams may benefit from decision rules for adherence checks and teacher supports

based on a triage model (Barnett et al., 2010). Basically, the more student (i.e., years of failure)

or situational risk (i.e., potential harm to self and others), the greater an investment may be

needed in implementing intervention supports toward acquisition and fluency of basic skills

needed to carry out intervention plans. With heightened behavioral risk and comprehensive

interventions, or high risk, high cost academic interventions, the first few sessions or days may

benefit from comprehensive supports and troubleshooting (Witt, VanDerHeyden & Gilbertson,

2004) that are faded with improved student outcomes. In multi-tiered systems, progressions

from prevention through the tiers are founded not on child data alone but on parallel data

showing valid interventions were well carried out. Ethical and legal decisions in multi-tiered

systems of supports put the burden of success on change agents and well planned interventions.

It is vital that we ask ourselves this question, “Is intervention acquisition alone sufficient?”

The research and theoretical perspective highlighted here suggests that school settings need to

ensure that both teachers and students have secured implementation fluency, which requires

high quality intervention training for all parties involved. In addition, these findings may

support changes in the collection of intervention fidelity data, with an emphasis beyond

acquisition of intervention steps to the inclusion of assessments of fluency. This requires

researchers to begin to define “fluency” for different intervention programs, focusing on both

the required level of intervention fidelity, as well as the recommended session duration for

various research-based intervention protocols. This review also suggests new significant roles

for consultants.


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