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The Behavior Analyst 1996, 19, 163-197 No. 2 (Fall)

163

Behavioral Fluency: Evolution of a New Paradigm

Carl BinderPrecision Teaching and Management Systems, Inc.

Behavioral fluency is that combination of accuracy plus speed of responding that enables compe-tent individuals to function efficiently and effectively in their natural environments. Evolving fromthe methodology of free-operant conditioning, the practice of precision teaching set the stage for dis-coveries about relations between behavior frequency and specific outcomes, notably retention andmaintenance of performance, endurance or resistance to distraction. and application or transfer oftraining. The use of frequency aims in instructional programming by Haughton and his associatesled to formulation of empirically determined performance frequency ranges that define fluency. Useof fluency-based instructional methods has led to unprecedented gains in educational cost effective-ness, and has the potential for significantly improving education and training in general. This articletraces the development of concepts. procedures, and findings associated with fluency and discussestheir implications for instructional design and practice. It invites further controlled research and ex-

perimental analyses of phenomena that may be significant in the future evolution of educational

technology and in the analysis of complex behavior.Key words: fluency, behavior frequency, precision teaching, automaticity, instructional design,

free operant

Fluency-based education and train-ing programs have produced some ofthe most dramatic results in thehistory of behaviorally oriented in-struction. During the 1970s, the Preci-sion Teaching Project in Great Falls,Montana (Beck, 1979; Beck &Clement, 1991) produced improve-ments in elementary students

standard achievement test scores ofbetween 20 and 40 percentile pointsover a 3-year period. The interventionwas the addition of only 30 min per

This paper is dedicated to Eric C. Haugh-

ton. whose 20 years of commitment to behav-ior frequency and childrens learning laid afoundation for much of what we now knowabout behavioral fluency. Erics prematuredeath in 1985 left his colleagues and studentswith a great legacy of ideas and a challenge tocontinue the work he began. I gratefully ac-knowledge the contributions to this manuscript

provided in discussions with Beatrice Barrett,

Jay Bimbrauer Elizabeth Haughton, KentJohnson, Harold Kunzelmann, Ogden Lind-sley, Richard McManus, Jim Pollard, ClayStarlin, and Cathy Watkins.

Correspondence concerning this article shouldbe addressed to Carl Binder, Binder-Riha Associ-ates, 4966 Wilshire Drive, Santa Rosa, CA 95404.Email: [email protected]; Website: www.Binder-Riha.com.

This Acrobat/PDF document was createdwith permission as a replica of the original pa-

per publication, preserving pagination andformatting as much as possible.

day of timed practice and charting toan otherwise typical elementaryschool curriculum. Binder and Bloom(1989) described fluency-based corpo-rate training programs that producednew sales trainees considered by theirmanagement to be more knowledge-able than senior sales representativeswith up to 6 years of experience.

Johnson and Layng (1992) reportedresults of a fluency-based adult liter-acy training program that were greaterin magnitude than those produced byany other program funded by the JobTraining Partnership Act. In the samepublication they cited comparablysuperior results with children at theMorningside Academy in Seattle andwith precollege students at Malcolm XCollege in Chicago. The size of theseeffects suggests that fluency-basedinstruction may offer a cost-effectiveweapon against the increasingly ac-

knowledged failure of the Americaneducation system. If confirmed byfurther systematic research, these re-sults may lead to a fundamental shiftin our understanding and design ofoptimally effective instructional pro-gramming taking fluency intoaccount.

The work on fluency has combinedformal research with extensive field


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164 CARL BINDER

investigation and development con-ducted in demonstration programs,

plus application in hundreds of preci-sion teaching classrooms since themid1960s. Most of this work has notbeen documented in the scientific lit-erature, but many of the empiricalgeneralizations derived by fluency re-searchers and practitioners over thelast 30 years suggest opportunities forimportant systematic research.

This article is intended to fill impor-tant gaps in the conceptual and his-torical record so that future research-ers and practitioners can work from afull appreciation of what has come be-

fore, and make contact with currentand past contributors. It brings to-gether an extensive list of referenceson the topic, and provides context andbackground commentary to supportfurther investigation and discussionamong interested readers.

DEFINITIONS OFFLUENCY

An advantage of the term fluency isthat many people already understand itintuitively or metaphorically. This fa-miliarity may arise from common useof the term with reference to language(as in he speaks French fluently). Ihave often begun corporate seminars,graduate classes, and teacher work-shops by asking the audience What isbehavioral fluency? prior to any ex-planation of the concept. Responsesfrom participants virtually always re-flect prior understanding of the termand its implications. For example,when asked to list associations withthe phrase behavioral fluency, onegroup produced responses that in-

cluded easy to do, mastery, reallyknows it, flexible, smooth, remem-bered, can apply, no mistakes, quick,without thinking, automatic, can useit, not tiring, expert, not just accurate,and confident. Each of these reflectsone or more attributes of what wemean when we use this term to de-scribe the goal of instructional pro-gramming.

As currently defined, fluency is thefluid combination of accuracy plus

speed that characterizes competentperformance (Binder, 1988b, 1990a).Fluency has also been described as acombination of quality plus pace(Haughton, 1980). Other termsequated with fluency are automatic(Haughton, 1972a) and second natureperformance (Binder, 1990a). Aplain-English description of fluency isthat it is doing the right thing withouthesitation (Binder, 1988b).

The features ascribed to fluent per-formance closely resemble those tradi-tionally associated with mastery. In

defining the desired outcome of in-struction, Barrett (1977a) explainedthat Stability or predictability of per-formance is, then, vital in definingskill mastery (p. 183). Gagnes de-scriptions of mastery as immediatelyaccessible and performed with per-fect confidence (Gagne, 1970,1974;Gagpe & Briggs, 1974) have had sig-nificant influence on fluency research-ers since the 1970s. In the final analy-sis, the term fluency is a metaphor re-flecting all of these qualities, referringto a collection of observations about

relations between response frequencyand critical learning outcomes.

The empirical definition of fluencyis related to its measured effects.When learners achieve certain fre-quencies of accurate performance theyseem to, retain and maintain

1 what

they have learned (Berquam, 1981;Kelly, 1995; Orgel, 1984); remain ontask or endure for sufficient periods

1 The term retention refers to the relation

between behavior frequencies at two points intime, between which the individual has had no

opportunity to emit the behavior.Maintenance,on the other hand, refers to the relation be-tween a behaviors frequency at two points intime, between which the individual has an op-

portunity to emit the behavior to produce rein-forcement in the natural environment. It is anempirical question as to whether the frequencyrequired to make a behavior usefulcapableof being emitted, reinforced, and thereby main-tained in its natural environmentis the sameas the frequency that will ensure retention ofthe behavior after a period of time in which ithas not occurred.


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BEHAVIORAL FLUENCY 165

of time to meet real-world require-ments, even in the face of distraction

(Binder, 1984; Binder, Haughton, &Van Eyk, 1990; Cohen, Gentry,Hulten, & Martin, 1972); and apply,adapt, or combine what they learnedin new situations, in some cases with-out explicit instruction (Binder, 1976,1979d, 1993a; Binder & Bloom, 1989;Haughton, 1972a; Johnson & Layng,1992, 1994). When a combination ofaccuracy plus speed of performanceoptimizes these outcomes with respectto a specific behavior class, that is thelevel of performance that has been de-fined as true mastery of the behav-

ior (Binder, 1987). Haughton (1980)captured this definition in an acronymby specifying what he called reten-tion-endurance-application perform-ance standards, or REAPS.

A NEW PARADIGM?

I have previously suggested thatfluency represents a new paradigm inthe analysis of complex behavior andthe design of instruction (Binder,1993a; Pennypacker & Binder, 1992).Although the term may be overused, itseems appropriate in this case. In his

historic work, The Structure of Scien-tific Revolutions, Kuhn (1970, pp. 10-11) used the termparadigmto refer todevelopments in scientific method andpractice that attract an enduringgroup of adherents and that are suf-ficiently open-ended to leave all sortsof problems for the redefined group ofpractitioners to resolve. Because de-velopments associated with fluencyhave produced discontinuous changesin practice among a community of re-searchers and practitioners with re-spect to the definition of instructional

outcomes and the measurement of in-structional effectiveness, in the designand implementation of instruction, andin efforts to account for and reverseeducational failure, they arguably rep-resent a ground-shifting developmentworthy of this term. Despite the factthat the measures and methods of flu-ency initially evolved from past workin operant conditioning, their implica-

tions have subsequently led in direc-tions that are truly revolutionary and

unlike what preceded them. The re-mainder of this article is devoted todescription of related historical devel-opments and explication of their prac-tical and scientific ramifications.

EARLY HISTORICALDEVELOPMENTS

Origins in Free-Operant Conditioning

The work in behavioral fluencytraces its origins to free-operant con-ditioning insofar as fluency research-ers and practitioners have explicitlystudied and tried to produce streamsof continuous responding rather thanpaced or controlled opportunities torespond (Barrett, 1977b; Binder,1978b, 1993a; Lindsley, 1964, 1972,1996a).

Skinners (1938) continuous meas-urement of behavior frequency in op-erant conditioning experiments revo-lutionized the study of behavior(Bjork, 1993, p. 93ff.). He observedlater in his career that response fre-quency measures and the cumulativeresponse recorder may have been hismost important contributions (Skin-ner, 1976). Indeed, virtually all of thebasic discoveries made in the researchlaboratories of Skinner, his students,and colleagues involved single-subjectdesigns with continuous recording offree-operant response frequencies oncumulative recorders. In contrast totraditional estimates of response prob-ability based on percentage correctcalculations, Skinner (1938) pursued aprogram of research in which rate ofresponding is the principal measure-ment of the strength of an operant

and where probability of action hasbeen attacked experimentally bystudying the repeated appearance ofan act during an appreciable period oftime (Skinner, 1953, p. 70). Theglossary in Schedules of Reinforce-ment (Ferster & Skinner, 1957) de-fines probability of response as theprobability that a response will beemitted within a specified interval, in-


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166 CARL BINDER

ferred from its observed frequencyunder comparable conditions (p. 731)

and strength of response as some-times used to designate probability orrate of responding (p. 733).

Despite these seemingly funda-mental views concerning the impor-tance of behavior frequency, whenSkinner and his colleagues began re-search in programmed instruction, aneffort to extend basic laboratory dis-coveries into education and training,they generally dropped response fre-quency measures in favor of moreconventional percentage correct or ac-curacy-only assessments (Skinner,

1954, 1968). In retrospect, this may bewhy fluency is only now emerging asa key element in the design of behav-ioral instruction: Most behavioraleducators abandoned the frequencymeasure, except occasionally whenmonitoring problem behavior, morethan 30 years ago.

Precision Teaching and the StandardCeleration Chart

Ogden Lindsley took exception tothe trend away from frequency meas-

ures in educational applications. Dur-ing the 1950s and early 1960s, Lind-sley worked with Skinner directing thefirst operant conditioning laboratoryfor humans in which he confirmed andextended principles and procedures,originally developed in the animallaboratory, to human behavior andcoined the term behavior therapyas away of distinguishing applied operantconditioning from psychotherapy(Lindsley & Skinner, 1954; Skinner,Solomon, & Lindsley, 1954). As inthe animal laboratory, Lindsley relied

on cumulative response records of be-havior frequencies as the basic meas-urement and analysis technology often simultaneously monitoring mul-tiple operants with separate cumula-tive recorders.

During the early 1960s, Lindsleyand his associates (prominently B. H.Barrett) applied functional behavioranalysis in the laboratory to the diag-

nosis and remediation of retarded be-havior (Barrett, 1965, 1969, 1971;

Barrett & Lindsley, 1962; Lindsley,1964). This work led to thedevelopment of precision teaching(Binder, 1988b; Binder & Watkins,1990; Kunzelmann, Cohen, Hulten,Martin, & Mingo, 1970; Lindsley,1972, 1990; White & Haring, 1976),in which teachers and their studentsused behavior frequency measures andthe standard behavior chart (Penny-packer, Koenig, & Lindsley, 1972) tomonitor individual classroom pro-grams and make educational deci-sions.

Vargas, participating in both thebroader tradition of behavioral educa-tion and in the subcommunity of pre-cision teachers, wrote that

Teaching is not only producing new behavior,it is also changing the likelihood that a studentwill respond in a certain way. Since we cannotsee a likelihood, we look instead at how fre-quently a student does something. We see howfast he can add. The student who does prob-lems correctly at a higher rate is said to knowaddition facts better than one who does them ata lower rate. (1977, p. 62)

This statement, rare among main-

stream behavioral educators, eloquent-ly repositions behavior frequency atthe heart of behavioral instruction.

The standard behavior chart (morerecently known as the standard celera-tion chart; see Figure 1) provided ameasurement advance comparable tothe cumulative recorder. Initially,Lindsley created the standard chart sothat teachers sharing graphs of behav-ior frequencies would be able to sharedata more efficiently, based on a stan-dard 44 graphic language. By allow-ing students, teachers, and researchers

to monitor behavior frequencies in astandardized graphic format, this toolreduced the time required to sharedata sets in a group from 20 to 30 minto about 2 to 3 min per chart (Lind-sley, 1971).

An important feature of the stan-dard chart is its combination of a lin-ear abscissa for calendar time with alogarithmic ordinate for behavior fre-


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168 CARL BINDER

Precision teachers learned to useprojected celerations (later called

celeration aims) to set minimum ac-ceptable learning rates (Koenig, 1972;White & Haring, 1976) for daily orweekly instructional decision making.As long as the actual data did not fallbelow the projected celeration line formore than 2 days in a row, the pro-gram continued. Data failing to accel-erate or decelerate as rapidly as theceleration aim for several days in arow prompted a change in the pro-gram. Analogous in use to thewithin-session cumulative responserecord in the laboratory, the standard

chart became an ongoing deci-sion-making tool for practitioners andbehavior scientists studying changesin frequencies across sessions. It al-lowed easy inspection, quantification,and decision making based on the nextderivative of behavior frequency,change in daily frequency per week(Kazdin, 1976).

Lindsleys goal (1972) was to putscientific methods in the hands ofteachers and studentsto transformclassrooms into places for data-baseddiscovery, fully integrated with edu-

cational practice. Adapting the labo-ratory model of direct continuous re-cording, Lindsley and his associatestimed and counted various types ofclassroom behavior for extended peri-ods of time during the early years oftheir work in education. They beganprecision teaching by transferringlaboratory strategies and tactics intothe classroom. using the standard be-havior chart to monitor and analyzeperformance and learning. In fact,early students of Lindsley studiedmany of the response classes and phe-

nomena addressed by other appliedbehavior analysts.

For example, Kunzelmann (1965)completed a masters thesis with Lind-sley by designing a transducer formonitoring frequencies of out-of-seatbehavior in the classroom. Haughtons(1967) doctoral dissertation likewisedealt with the relatively traditional be-havioral topic of reinforcer sampling,

presenting data on a precursor of thestandard behavior chart.

Initially, precision teachers meas-ured how much time students requiredto complete practice sheets and cal-culated count per minute with a fixednumerator and variable denominator(Lindsley, personal communication,1995). After a while, the practice ofcollecting brief (e.g., 1-min) fixedsamples of behavior frequenciesemerged as a critical component ofprecision teaching (Haughton, 1972a;Kunzelmann et al., 1970; Starlin,1972), in part for calculation conven-ience. Although Lindsley (personal

communication, 1995) at first resistedshort measurement intervals, prefer-ring to record behavior over extendedperiods of time as in the operant con-ditioning laboratory, proponents ofbrief timings persevered. They quicklyrecognized the sensitivity of brieftimings to differences in skilled per-formance, and began to use brief tim-ings as a rapid and inexpensivemethod for gathering descriptive in-formation about various types of hu-man behavior. This methodologicalshift toward using brief fixed timings

to calculate behavior frequencies ledto initial discoveries about fluencyamong precision teachers (Haughton,1972b; Kunzelmann et al., 1970).

Professional Communication Basedon Charts Rather Than Publications

Those involved in precision teach-ing did not seek to publish in the waythat is generally maintained by aca-demic contingencies of reinforcement.There seem to have been three pri-mary reasons for this turn of events.First, most were practitioners who did

not pursue publication for career ad-vancement. Second, discoveries inprecision teaching were progressingmore rapidly than journal or bookpublication cycles could match, andthis discouraged even the academicsamong precision teachers from for-mally reporting findings or practicesthat would be obsolete by the time ofpublication. Third, from his own ex-


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straint on the ability of reinforcementto increase the frequency of composite

behavior. When Haughton (1972a)and his associates first began to rec-ognize the importance of behaviorfrequencies as indicators of skill pro-ficiency, they attempted to reinforceperformance of basic academic skills.But low frequencies of tool skills(e.g., writing digits) imposed ceilingson the acceleration of composite be-havior frequencies (e.g., writing an-swers to math problems), and previ-ously identified reinforcers aloneproved incapable of increasing fre-quencies of the composites to the de-

sired levels. Only prompting and rein-forcing performance of componentsled to higher composite frequencies.Thus, new observations about re-sponse-response frequency relationsrevealed a previously unrecognizedconstraint on the potential of rein-forcement procedures to increase fre-quencies of complex behavior. Evenordinarily strong reinforcement con-tingencies, identified separately withother response classes in the same in-dividual, might prove to be ineffectiveif applied to composite behavior when

component behavior frequencies arelow. This finding also led to researchdesigns in which experimenters mustbe certain before hand that componentbehavior frequencies do not artifactu-ally constrain the growth. of compos-ite responses being subjected to ex-perimental procedures designed to in-crease their frequencies (Binder,1984).

Programming Based onComponent-Composite Relations

Initial use of performance aims fo-cused on tool skills related to reading,writing, and computational math. Anunderstanding of the relations amongtool skills and basic academic skillsled Haughton to use a chemical anal-ogy, referring to a general relationamong response classes as elementsand compounds (Haughton, 1981a).His analogy suggested that, like atoms

requiring a certain valence or energyto combine, behavioral elements re-

quire a certain frequency to formcompound response classes. Others(Barrett, 1977a; Binder, 1978a), bor-rowing from the literature of percep-tual-motor learning (e.g., Gagne,Baker, & Foster, 1950), first used theterms component and composite to re-fer to this general part-whole relationas applied in precision teaching.

Curriculum analyses and designsduring the 1970s and early 1980s fo-cused on identifying relations betweenbehavior components and compositerepertoires. Haughton (1972a) studied

correlations in log-log scatter plots be-tween frequencies of components andcomposites in the repertoires of indi-viduals and groups. Initial functionalanalyses studied component-com-posite relations by attempting to buildfrequencies in components and thenobserving the effects on composites(Haughton, 1972a). Van Houten(1980, pp. 24-25) described a proce-dure that used the frequency of writ-ing answers to long multiplication anddivision problems (composite) as adependent variable to assess the ef-

fects of increasing frequencies ofwriting answers to basic multiplica-tion facts (components).

Extending the approach beyondacademic behavior, Haughton and hisassociates worked with teachers ofmultiply disabled students who exhib-ited severe deficits in fine and grossmotor control. Collaborating withMary Kovacs, who was trained as aphysical therapist and nurse (Haugh-ton & Kovacs, 1977; Kovacs &Haughton, 1978), and with AnneDesjardins and Bev Palmer (Binder,

1979a), Haughton identified a set offundamental component skills, origi-nally called The Big 6 (reach, point,touch, grasp, place, release) and laterenlarged to The Big 6 Plus 6 (includ-ing twist, pull, push, tap, squeeze,shake). They also developed a taxon-omy of behavior components involv-ing gross motor control of hunk, arms,legs, and head (Kovacs, 1978). Esti-


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172 CARL BINDER

mating competent performance rangesusing brief timed samples of adult per-

formance to establish aims and pro-viding isolated practice with these fineand gross motor skill elements,Haughton and his associates enabledseverely disabled people to achievepreviously unattainable functionalskills (Binder, 1991b). Binder and hisassociates extended this work tomultidisciplinary programming withphysical, occupational, and languagetherapists (Binder, 1981a, 1981b;Binder & Pollard, 1982; Burgoyne,1982; Imbriglio, 1992; Pollard &Binder, 1983).

Perhaps the most dramatic successstory during these years was the caseof Terry Harris, a boy born with se-vere cerebral palsy and diagnosed aslikely to be institutionalized, nonver-bal, and nonambulatory. Eric andElizabeth Haughton worked withTerry and his parents from earlychildhood (Binder, 1991b). Today, inhis 20s, Terry attends graduate school,drives, skis, and is a motivationalspeaker, despite the persistence of hisneuromuscular handicap. His successwas built on many thousands of hours

of practice to achieve fluency on themost basic fine and gross motor ele-ments and an entire repertoire con-structed of those elements, using pre-cision teaching methods in a progres-sive curriculum of component- com-posite relations. (Records of this caseinclude a videotaped presentationfrom the 1990 International PrecisionTeaching Conference featuring Terry,his mother, and Elizabeth Haughton,his teacher, in addition to charteddata.)

Much work at Barretts Behavior

Prosthesis Laboratory and associatedagencies (see below) during the late1970s focused on application of theseprinciples to a broad range of self-careand vocational skills among the se-verely disabled, especially develop-ment of materials and procedures forassessing and practicing componentsin isolation prior to combining theminto chains (Barrett, 1977b, 1979;

Binder, 1976; Bourie & Binder, 1980;Pollard, 1979; Solsten & McManus,

1979). These procedures provided al-ternatives to accuracy-based backwardchaining methods that had proven tobe unreliable in producing lasting,functional repertoires for many dis-abled learners (Barrett, 1977a).

FROM AIMS TO REAPS

Seeking Performance Standards

As Haughton and his associatesworked to identify performance aims,they frequently found it necessary toraise what they had thought to be ap-

propriate criteria to higher levels, be-cause students were able to achievethem and because achieving morerapid performance of componentsusually led to easier learning and bet-ter performance of composites. Forexample, Haughton (1972a) reportedthat reading orally at 100 words perminute and writing answers to basicarithmetic problems at 40 to 50 prob-lems per minute were sufficient to en-sure subsequent progress through cur-riculum. By the end of the 1970s,commonly used aims for those skills

were 250+ words per minute (Starlin,1979) and 80 to 110 problems perminute (Haughton, 1980), respec-tively. Acknowledging this evolvingdevelopment of fluency standards,every list of performance aims distrib-uted by Haughton included a revisiondate set I year after the date of crea-tion, indicating that the aims recom-mended in any given document shouldbe reviewed at least once per year, tosee if they reflect current evidence.

During that period, some precisionteachers had begun to set aims with

their students using levels of perform-ance significantly below normal adultfrequencies (Howell & Kaplan, 1979;White & Haring, 1976). In fact, somepractitioners even suggested loweringaims to account for age and level ofdisability. An educational practiceknown as curriculum-based measure-ment (Binder, 1990b; Deno, 1985)was influenced by precision teaching


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work conducted in Minnesota by Clayand Ann Starlin (1973a). This ap-

proach reduced the notion of compe-tency-based aims to norm-based crite-ria, however, using class averages asperformance standards instead of cri-teria intended to reflect empiricallydetermined competence levels and toensure successful learning and appli-cation. The use of handicapped aimsand of class averages to set aims con-tains an inherent flaw, if the objectiveis to produce competent performers.When applied in schools in whichclassroom medians fall far below lev-els shown to represent competence in

the community (e.g., Wood, Burke,Kunzelmann, & Koenig, 1978), theseapproaches virtually institutionalizeincompetence in the form of subopti-mal performance criteria. The generalpractice of setting educational goalsbased on norms rather than on empiri-cally validated measures of compe-tence may be responsible for the in-creasing prevalence of illiteracy andother skill deficits within the schoolgraduate population. Haughton and hiscolleagues pushed in the opposite di-rection, establishing aims by collect-

ing measures of competent adult per-formance, and encouraging students toachieve their personal best levels forevery skill.

Setting Aims Using FrequencySampling

Wood et al. (1978) collected brieffrequency samples of math skills per-formed at peak levels byhigh-performing and low-performingeighth graders as well as by profes-sionals who used arithmetic in theirjobs. The data revealed that adult pro-fessionals were generally higher inperformance frequency than eighthgraders at the top of their classes, ex-cept in skills seldom used by adultprofessionals (e.g., fractions anddecimal arithmetic). Barrett (1979)made similar comparisons of perform-ance on 16 prevocational and preaca-demic skills among competent adults,normal children, and institutionalized

disabled students in her laboratoryclassroom. Although all performed at

100% accuracy and were therefore in-distinguishable from one another onan accuracy scale, the ranges of be-havior frequencies for each populationclearly separated competent adultsfrom normal children and distin-guished both groups from the disabledstudents.

The approach of sampling perform-ance of various populations intro-duced an important element of natu-ralistic observation that would havebeen impossible with accuracy-onlymetrics. As a rule of thumb, on any

well-practiced skill in a homogeneousadult population, the range of fre-quencies represented by as few as ahalf dozen individuals generally pro-vides a reasonable estimate of per-formance levels in a larger population.(To convince yourself, ask a halfdozen competent adults to write an-swers to simple addition problems ona sheet containing 120 or more suchproblems for 1 min as rapidly as pos-sible. You will likely find that most ofthe individuals will write between 80and 110 answers per minute.) Such an

empirically determined range of be-havior frequencies is quite differentfrom an arbitrarily chosen percentagecorrect criterion. Unlike percentagecorrect, a dimensionless quantity(Johnston & Pennypacker, 1980), be-havior frequency is a standard unit ofmeasurement and places fre-quency-based instructional design andassessment squarely in the domain ofnatural science (Barrett, 1977a, 1979;Binder, 1995). For well-practiced be-havior in a normal adult repertoire,samples of competent adult perform-

ance generally provide a good firstapproximation for setting instructionalaims. Prior to completion of con-trolled studies designed to identifyoptimal performance aims for specificskills, behavior frequency samplingmethods (sometimes known as snap-shots among precision teachers) pro-vide important tools for instructionaldesigners and practitioners.


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174 CARL BINDER

REAPS: Aims Based on CriticalLearning Outcomes

During the late 1970s, Haughtoninitiated use of the term R/APS (reten-tion/application performance stan-dards), suggesting that we set aimsempirically by determining what lev-els of performance ensure retentionand application of skills (Haughton,1981b). Shortly thereafter, the termexpanded to REAPS (retention-endur-ance-application performance stan-dards), reflecting observations thatachieving high performance frequen-cies seemed to increase the likelihood

that students would maintain attentionto task over extended durations of per-formance and in the face of distraction what he and others called the en-durance of performance (Binder,1984; Binder et al., 1990; Cohen et al.,1972; Haughton, 1980). Endurancebecame a new subject for instructionalresearch. The REAPS acronym set along-term research agenda aimed atdetermining, for every response classof interest, performance standards thatensure these critical learning out-comes.

Evidence to support REAPS

The determination of performancestandards based on the criterion thatthey optimally support retention, en-durance, and application suggests avirtually endless program of investi-gation that could keep researchersbusy for decades. To meet the chal-lenge posed by Haughtons acronym,we would need to determine, for eachbehavior class, the frequency rangesrequired for optimally supporting eachof these outcomes. Moreover, the fre-

quencies are likely to vary for anygiven class of behavior. For example,an individual might permanently re-tain or remember basic math factspracticed to 60 or 70 per minute, withnegligible improvements in retentionbeyond that range, yet continue to im-prove in the ability to apply the skillin mental math as it accelerates be-yond 100 per minute. That is, the op-

timal frequency for retention might bedifferent from that for endurance or

application. Multiplied by the totalnumber of response classes in a hu-man repertoire, this challenge may bepractically impossible to address forevery important one. Nonetheless,practitioners and researchers will con-tinue to investigate and experimentwith levels of performance and theireffects in several important domains,most notably the basic academic andintellectual skills.

Simply demonstrating in a system-atic fashion that higher performancefrequencies improve outcomes in one

or more of the three categories for anybehavior class is itself a notable ac-complishment, one that can surely in-spire many theses and dissertations inthe future. What follows is a briefsummary of some key findings relatedto each of these outcomes, most ofwhich beg for replication and system-atic experimental analysis.

Retention. A variety of classroominstructional design projects havedemonstrated effects of frequencybuilding on retention. Disabled stu-dents who had previously failed to ac-

quire or maintain behavior chains(e.g., assembly or dressing skills) withstandard accuracy-based backwardchaining procedures were able tocombine and apply behavior compo-nents in chains after repeated dailypractice of each component in isola-tion had increased performance fre-quencies (e.g., Pollard, 1979; Solsten& McManus, 1979). Although theseprojects were clinical in nature anddid not involve formal control condi-tions, they were essentially multiplebaseline replications across individu-

als. They are generally referred to assupport for the application aspect ofREAPS. However, many teachers ofthe disabled have worked with stu-dents who do not retain componentsof even the simplest chains for morethan a few hours or days after accu-racy-only chaining procedures. Theresults of these programs suggestedthat increasing behavior frequencies


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improves retention, in the sense thatretention of components is a minimal

prerequisite for subsequently inte-grating them into chains.In addition, college students who

practiced calculus formulas and rulesusing timed flash cards to achieveaims of saying 50+ facts per minutewere able to perform nearly twice asaccurately on tests 6 weeks later asthose who did not achieve highfrequencies (Orgel, 1984). Berquarm(1981) demonstrated similar relationsbetween retention and performancefrequency. Kelly (1995) used awithin-subject yoked design to sepa-

rate the effect of mere repetition fromthat of achieving more rapidresponding, and supported theconclusion that achieving more rapidperformance yields greater retention.

Endurance. Binder (1982, 1984;Binder et al., 1990) has reported re-search on the ability of students toperform for extended periods of timeas a function of initial performancefrequency. Early observations withdisabled students demonstrated thatfor those with low levels of perform-ance, practice durations as short as 3

to 5 min were too long to sustainsteady performance, even with addedreinforcement procedures. Studentsslowed their performance within thefirst minute or two, and often exhib-ited off-task or disruptive responses.When required performance durationswere shortened to 1 min or less, per-formance frequency jumped or turnedup and exhibited less variability, andstudents stopped emitting off-task be-havior. Changing performance dura-tions affected frequencies of correctand error performance as well as

celerations. Working for shorter inter-vals often enabled students to achievehigh levels of performance faster.These effects are easy to observe, inany population in which individualshave not yet achieved competent lev-els of performance. Application ofthese findings to instructional pro-gramming involves working with veryshort intervals (e.g., 10 s) called

sprints (Haughton, 1980) until stu-dents are able to achieve aims, then

gradually lengthening practice inter-vals to build endurance (Bourie, 1980;Desjardins, 1981). Haughton, Ma-loney, and Desjardins (1980) adaptedthe count per minute standard celera-tion chart for such procedures bychanging the day-lines into successiveminute-lines for charting repeatedsprints. Johnson and Layng (1994)have reported using a version of thismethodology in the Morningsidemodel.

Johnson (personal communication,1996) reports a cautionary note that

students who achieve high frequenciesfor brief durations within sessions,without continuing on successive daysto practice until they achieve aims forlonger durations, may not exhibit thesame degree of retention or applica-tion during later sessions as if theyhad been required to achieve aims forlonger durations on successive days.This finding emphasizes the impor-tance of distributing practice overmultiple sessions, and of checkingperformance frequencies on more thanone day to be certain they are retained.

Two unpublished sets of pilot dataobtained by the author provide tem-plates for future endurance research.In the first (Binder, 1994), teacherscollected samples from 75 studentsrepeatedly writing digits 0 to 9 forvarying durations, once per day, in as-cending sequence: 15 s, 30 s, I min, 2min, 4 min, 8 min, and 16 min. Thedistribution of performances acrossthe population for the 15-s intervalranged from less than 20 per minute toover 150 per minute. Each subjectsmedian count per minute across all

durations placed him or her in a fre-quency bin, each bin spanning a rangeof 20 per minute. Figure 2 summarizesthe results, each data point rep-resenting a median frequency at agiven duration for the individuals in agiven frequency bin. These data showgreater performance decrements at thelong intervals for subjects with lowerperformance frequencies. Around 70


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176 CARL BINDER

per minute appears to be a cut-off

point beyond which higher initial fre-quencies do not predict greater abilityto sustain prolonged performance.Using this approach (being sure tostudy at least an order of magnituderange in both behavior frequenciesand performance durations), future in-vestigators may be able to identifysuch cut-off points for other types ofbehavior.

The second pilot design (Binder,

1979c) is a free-operant analogue ofautomaticity experiments conductedby cognitive psychologists (LaBerge& Samuels, 1994) who used latencymeasures in trials procedures. Twoadult subjects performed five differenttasks in successive 3-min intervals:reading numbers, saying answers tosimple addition problems (sums to18), reading printed anglicized names

Figure 2: Points represent group median count per minute at each performance duration of each of

eight groups of subjects. Each group contained subjects whose median performances across all

durations were within the indicated frequency rqnge. N = 75.


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of Hebrew characters, saying numbersin response to the names of Hebrewcharacters (previously learned in apaired associate procedure), andadding Hebrew characters by usingthe previously learned paired associateto assign numbers to the characters

(an example of stimulus equivalence).Subjects performed all tasks byreading aloud from Practice sheetsinto a microphone attached to avoice-operated relay with electrome-chanical equipment for counting andrecording responses on a cumulative

Figure 4: Each pair of cumulative records reprsents the same pair of subjects as i Figure 3 performing

the listed behaviors, recorded by means of a voice-operated relay. Tick marks indicate onset and

termination of a distracting auditory stimulus associated with the suppression ratios.

Figure 3: Each pair of cumulative records represents the pair of subjects performing the listedbehaviors, recorded by means of a voice-operated relay.


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178 CARL BINDER

recorder. Figure 3 shows pairs ofcumulative records for each task,.each

pair representing the performance ofthe 2 subjects during a single session.Note that the 2 subjects perform thefirst three tasks at about the samefrequencies, as would be expectedbecause these three are well-established arithmetic and readingskills found in competent adults. Onthe fourth task, a newly teamed pairedassociate, the 1st subject, who hadcompleted more practice sessions,performed at a higher frequency thanthe 2nd subject. And on the fifth task,which required the newly learned

paired associate as a component, the1st subject performed considerablymore rapidly, as would be expected.After a brief rest period, both subjectsrepeated the same tasks, this timewearing headphones through whichthey heard random numbers (a dis-tracting stimulus) for 30-s periodshalfway through each session. Figure4 shows cumulative records of theseperformances, with suppression ratioscalculated as frequencies between thetick marks divided by frequenciesaveraged for the periods before and

after the marks (Estes & Skinner,1941). These suppression ratios andcorresponding visual dips in the re-cords between tick marks reflectproportional decrements in respondingassociated with the distractingstimulus.

Although others will surely need toreplicate this experiment to test thefindings further and apply the designwith other response classes, these pilotdata indicate that lower performancefrequencies may be associated withgreater distractibility, measured as

relative suppression of respondingduring presentation of an externalstimulus. This model applies free- op-erant laboratory methods to measuredistractibility as an alternative to themore cumbersome and less sensitivelatency-based trials procedures gener-ally used by cognitive researchers andby some behavior analysts.

In combination, these two pilot

studies reflect expected characteristicsconnoted by the term endurance: the

ability to continue performing overincreasing durations and in the face ofenvironmental distraction (much likethe strong long-distance runner whocan persist without stumbling, evenwhen encountering obstacles in thepath). Most important, they offerdesigns for further analysis.

Application. By far the greatestamount of evidence exists to supportthe conclusion that increasedperformance frequencies improveapplication. By application we meanintegration of component response

classes into composite responseclasses. Haughtons (1972a) originalreport indicated that increasing thefrequencies of component skills sup-ports more rapid learning andperformance of composites. This basicfinding has been replicated countlesstimes in precision teaching classroomsfor regular or mildly disabled students(Beck, 1979; Evans & Evans, 1985;Johnson & Layng, 1992; Lindsley,1992; Maloney, Desjardins, & Broad,1990; Mercer, Mercer, & Evans,1982; Starlin, 1972; Van Houten,

1980). Classroom and vocationalprojects with severely disabledlearners (Binder, 1976, 1979d;Pollard, 1979; Solsten & McManus,1979) demonstrated that frequencybuilding of components not onlyallows more rapid acquisition ofcomposites but sometimes seems toproduce composites with virtually noformal instruction an effect thatJohnson and Layng (1992) have calledresponse adduction (Andronis, 1983;Epstein, 1985).

An earlier study by Binder (1979d)

demonstrated the effects of buildingcomponent frequencies in afree-operant analogue of Sidmans(1971) mediated transfer procedurewith 4 institutionalized disabled stu-dents. Subjects learned to read sightwords corresponding to words in theirexisting speaking vocabularies (i.e.,they could vocally name the actionsand objects corresponding to the


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180 CARL BINDER

knowledge.Johnson and Layng (1992, 1994)

have reported that when the basics arefluent later learning becomes easierrather than more difficult (a topic richwith opportunities for controlled re-search). Thus, greater focused practiceto achieve fluency in a foundationrepertoire is more likely to be cost ef-fective and time efficient than a broadaccuracy-based approach to curricu-lum. This is in contrast to the typicalinstructional program in which mosttime is spent on acquisition or estab-lishing the skills, with insufficientpractice to ensure fluency.

Mainstream educators often charac-terize traditional drill and practice asoutmoded, boring, and ineffective be-cause it does not support the higherorder problem-solving repertoiresneeded in todays world. Johnson andLayngs (1992) report that fluent pre-requisites support easy acquisition ofproblem-solving repertoires contra-dicts this philosophical assertion.

Fluency-based instructional meth-ods alter important features of tradi-tional practice methodologies thatmake them ineffective and unpleasant

(Binder, 1994). First, much of tradi-tional academic practice has no clearlydefined objective other that to getbetter. Whereas practice in suchskills as martial arts or musical per-formance sets implicit time-based flu-ency aims as practice goals (smooth-ness, quickness), traditional accu-racy-based educational assessmentcannot measure improvement beyond100% correct. In the absence of a flu-ency goal and feedback against thatgoal, practice receives little or no rein-forcing consequences. With the addi-

tion of fluency aims and daily meas-urement, practice has a clearly definedgoal, and advancement toward thatgoal might be reinforcing. Second,many practice procedures strain theendurance of individuals who have notyet achieved sufficiently high per-formance frequencies (Binder et al.,1990). Prolonged practice when be-havior frequencies are low may be

subjectively unpleasant and may oc-casion undesirable off-task behavior.

Learners may not be able to maintaina given level of performance for morethan a very brief interval (e.g., 15 or30 s) when the performance level isfar below its fluency aim. Brief prac-tice intervals can provide acost-effective antidote for these unde-sirable effects, often accelerating cor-rect responding while reducing vari-ability and problem behavior. Third,much traditional practice occurs underaversive control. Effective precisionteaching methods positively reinforceimprovement with feedback from

charted data and encouragement fromteachers or practice coaches.

Developments in Instructional Design

Steps and slices. Precision teachersuse the language of stepsand slicestodescribe curriculum sequences (Star-lin, 1972; White & Haring, 1976). Astep is a phase change to a new classof behavior or a new subobjective(e.g., from writing digits 0 to 9 overand over again to writing digits as an-swers to math problems). To step backis to practice a prerequisite class of

behavior as a form of remediation in acurriculum sequence. A slice is a sub-set of all possible instances of a par-ticular behavior (e.g., writing answersto addition problems with sums to 10as. a subset of all possible additionproblems with sums to 18). To sliceback is to select a smaller set of be-havior instances for practice to accel-erate learning. The term curriculumweight (Haughton, 1979, personalcommunication) reflects an under-standing that if learners are asked topractice too large a slice of behavior,

the acceleration of that behavior maybe weighted down by too heavy aburdentoo many instances at once.These concepts, plus a variety of logi-cal component-composite chunkingand sequencing approaches to cur-riculum design, were the focus of pre-cision teaching for many years (e.g.,Howell & Kaplan, 1979; Starlin &Starlin, 1973b, 1973c, 1973d).


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Learning channels. The conceptand classification of learning channels

represented an important advance influency-based curriculum analysis anddesign. Early precision teachers usedverb channels (Kunzelmann et al.,1970) to indicate the type of move-ment represented by a given responseclass or pinpoint (Lindsley, 1972).The nomenclature of verb channelsincluded such active verbs as say,write, touch , and mark. They providedunambiguous language for categoriz-ing the form of behavior. Later, preci-sion teachers combined inputs (ante-cedent stimuli) with verb channel out-

puts to describe behavior (e.g., oralreading is see/say words). Finally,Haughton (1980) introduced thelearning channel matrix (Figure 5), agrid listing possible inputs down theleft (see, hear, sniff, taste, touch, andfree) with verbs across the bottom in-dicating actions (e.g., say, mark, type,write, tap, do, etc.). Many types ofskills might fall into a given in-put/output combination or channel(e.g., see/say numbers, words, colors,and pictures of famous people).

Haughton (1980) used the term

channel setto refer to the collection ofall skills in a given channel (corre-sponding to a cell in the learningchannel matrix) and the performancestandards associated with each. Thechannel matrix enabled curriculumdesigners to plan for a variety of dif-ferent skills in the same curriculumarea by viewing all possible in-put/output combinations on a singlesummary form. For example, see/sayanswers to math facts, hear/write an-swers, and hear/say answers might allbe parts of a math curriculum. Each

form of behavior could be assessedand practiced on its own or in combi-nation with others. Lindsley (1992)cites evidence indicating that learningand performance in one channel aregenerally independent of (or cannot bepredicted from) others, recommendingexplicit. assessment and instruction inevery channel of interest for a givencurriculum area. Haughton (1977;

Hastings County Board of Education,1977a, 1977b) and his associates used

the channel matrix to map out broadsets of curriculum in a range of differ-ent domains. Binder (1989) usedchannel language as a core componentof the FluencyBuilding approach toinstructional design, and ElizabethHaughton (1993a, 1993b, 1994) hascontinued to apply this analyticalframework in designing instructionalactivities and materials.

One implication of the learningchannel matrix for setting aims is thatthe aim for a given form of behaviorin one learning channel may be pre-

dictable from others in that same .channel. For example, reading wordsat 150 to 250 per minute in the see/saychannel helps to predict the pace atwhich an individual might be able tosilently read the fronts of flashcards(McDade & Olander, 1990) and saythe words written on the backs. Like-wise, the frequencies of all see/writeskills have predictable quantitativerelations that can be estimated withfrequency sampling procedures in agiven population. Categorizing typesof behavior in this way may help to

estimate appropriate performanceaims for one behavior based on whatis known of another in the same chan-nel, without having to empirically es-tablish REAPS for an infinite numberof different specific types of behavior.For example, count per minute aimsfor a set of see/say biology flash cardswill be approximately the same as fora set of history flash cards. On theother hand, some operants that onemight naively believe would occur atthe same frequencies do not (e.g.,see/say numbers, see/say the colors of

dots). So its always best to confirmfrequency ranges by sampling the per-formance of competent adults or ex-perts.

Combining precision teaching withdirect instruction. Maloney and Hum-phrey (1982) and Maloney et al.(1990) first combined the methods ofprecision teaching with Engelmannsdirect instruction approach (Binder &


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182 CARL BINDER

Watkins, 1990; Engelmann &Carnine, 1982; Watkins, 1988). Theintention was to optimize direct in-structions small-group teachingmethods and empirically validatedstrategy-based instructional designswith the addition of the assessmentand frequency-building methods ofprecision teaching. Johnson andLayng (1992. 1994) continued in thisdirection by adding significant re-finements in instructional design anddelivery methodology, influenced by

the work of Tiemann and Markle(1990). They analyzed and sequencedcurriculum to encourage generativity,the emergence of new behavior basedon the principle of contingency ad-duction (Andronis, 1983). Binder hasapplied similar analyses and designprinciples to develop curriculum andlearning procedures for corporatesales professionals (Binder & Bloom,

1989).

Assessment Methodology

Placement in a curriculum se-quence. Starlin and Starlin (1973a,1973b, 1973c, 1973d) developed aprecursor to what Johnson and Layng(1994) have recently called precisionplacement. By breaking curriculumsequences into fine steps and slices,and by sampling performance atpoints up and down those sequences,precision teachers place students in a

curriculum based on performance fre-quencies.

Using ratios to predict perform-ance. Precision teachers began to useratios of component to composite be-havior frequencies sampled in com-petent performers (e.g., writing digitsand writing answers to problems) topredict composite frequencies frommeasured component frequencies in

Figure 5: An early version of the learning channel matrix.


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students. For example, they estimatedthat writing digits normally occurs at

around 1.5 to 2.0 times the frequencyof writing answers to basic mathproblems (Gaasholt, 1970; Haughton,1972). Thus, if a student writes digitsat only 60 per minute; 30 to 40 perminute is probably the range in whichthat student will be able to write an-swers to problems. Ratios provide asimple means of quantifying and pre-dicting relations between behaviorcomponents and composites.

Predicting special educationalneeds. Kunzelmann and his associatesused frequency (performance

measures) and celeration (learningrates) to predict future learning andperformance. In the Seattle-Spokane-Tacoma Project (Child Service Dem-onstration Program, 1974), they col-lected approximately 150,000 samplesof academic behavior frequencies onnearly 3,000 skills from a total of17,996 students in three schooldistricts. Teachers collected 7 to 10days of repeated measures per studentper skill to determine median frequen-cies and celerations. By flaggingstudents with less than half the median

class frequency or median classceleration on a given skill, it waspossible to identify more than 70% ofthe students, later diagnosed withmore costly procedures, as havinglearning problems. Koenig and Kun-zelmann (1980, pp. 49-55) laterdemonstrated that celeration (learningrate on repeated measures) is aculturally unbiased predictor ofacademic success. Several textbookson assessment (Howell & Kaplan,1979; White & Haring, 1976) docu-mented basic skills assessment proce-

dures that used both frequency andceleration measures to place studentsin a curriculum.

Kunzelmann and his associates(Kunzelmann & Koenig, 1980;Magliocca, Rinaldi, Crew, & Kunzel-mann, 1977) used single frequencysamples of four skills (writing loops,touching circles, touching body parts,counting from I to 10) to assess

preschool childrens readiness for firstgrade. With 92% predictive validity,

children who performed in the bottom25% in any three of the four skillswere diagnosed 1 year later as re-quiring special education programs.

Identifying individuals best learn-ing channels. Koenig and Kunzel-mann (1980) used celeration to assesslearning potential in different learningchannels in what they termed learningscreening, operationalizing whatmany in the mainstream educationalliterature have called learning styles.For example, teachers collected re-peated 7 to 10 daily measures of

writing answers to written arithmeticproblems (see/write), saying answersto written problems (hear/write), andsaying answers to spoken problems(hear/say). They then used celerationsobtained from these repeated meas-ures to predict the learning channels inwhich individuals would accelerateperformance most rapidly.

Component composite diagnosis.Bourie and Binder (1980; Binder,1980) applied frequency and celera-tion assessment methods to the se-verely disabled, collecting 10 daily

frequency samples of 47 componentand composite academic and voca-tional skills in a population of institu-tionalized students. They used fre-quency and celeration measures toidentify learning and performancedeficits. Influenced by Gilberts(1978) notion of potential for im-proving performance as the ratio be-tween the best measured performanceand the average or typical perform-ance in a population, they coined theterm deficit ratio to refer to the ratiodividing competent adult performance

levels or REAPS by each individualstudents frequency on a given skill.They identified skills with the greatestdeficit ratios as being most in need ofremediation and most likely to imposeceilings on other skills, prioritizingthem for classroom interventions.

Materials development. In additionto developments in assessment meth-odology, some of these efforts, nota-


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184 CARL BINDER

bly the Seattle-Spokane-Tacoma pro-ject (Child Service Demonstration.

Program, 1974), resulted in thousandsof standard practice/assessment sheetsfor fine slices of academic skills overmultiple curriculum areas. These ma-terials subsequently served as an in-valuable resource for other precisionteaching work around North America,including massive curriculum devel-opment efforts in Hastings County,Ontario during the 1970s and theSacajawea Precision Teaching Projectin Great Falls, Montana (Beck, 1979).These early precision teaching materi-als, including revised versions, are

currently available for purchasethrough Sopris West, a publishing andtraining company in Longmont, Colo-rado.

CUMULATIVE DEFICITAND GENERATIVITY

The Problem of CumulativeDysfluency

In the process of collecting data andusing frequency aims, precision teach-ers began to highlight the phenome-non of cumulative dysfluency. A new

understanding of educational failurederived from the recognition that be-havior components with frequencydeficits, despite their accuracy, accu-mulate when they are layered on topof one another in a curriculum se-quence. This accumulation of dysflu-ent skills limits and may even preventacquisition of composites that dependon them.

In mathematics, for example, manyreaders of this article probably experi-enced increasing difficulty somewherein the curriculum. Whether this oc-curred in long division, algebra, orcalculus, the point is the same: Cu-mulative dysfluencies in prerequisiteand component skills mounted tomake progress. through the curriculumincreasingly difficult. If performingsimple mental arithmetic calculationsrequires more than a fraction of a sec-ond, for example, then one is likely toexperience difficulty when attempting

to follow a teachers rapid demonstra-tion of solving an algebra equation.

Fluency-oriented educators are com-ing to view cumulative dysfluency asperhaps the single most importantfactor in long-term student failure(Binder, 1988b; Johnson & Layng,1992; Pennypacker & Binder, 1992).The analysis of cumulative dysfluencyis rich with opportunities for con-trolled research and for communica-tion with the larger educational com-munity

Unfortunately, in an educationalenvironment . in which accuracy is theonly metric for mastery, it is impossi-

ble to detect dysfluency prior to its ul-timate cumulative effect: the inabilityto learn or perform complex skills dueto multiple dysfluent (but possibly ac-curate) prerequisites or components.Although measures of performancefrequency clearly separate individualsor groups with obviously differentlevels of competence, accuracy as-sessments may not (Barrett, 1979).

Generativity: A Result ofCumulative Fluency

After the original discovery that

component skills must be fluent tosupport easy application (Haughton,1972a), precision teachers began tounderstand that the ability to combineresponse classes and improvise orproblem solve depends on the devel-opment of fluent components. Ac-counts of child development based onan analysis of component behaviorfrequencies in the womb (Edwards &Edwards, 1970) and in early child-hood (Mira, 1977) reflected a viewthat composite milestones in the tradi-tional account of child developmentemerge when behavior componentsincrease in frequency and spontane-ously combine and are then reinforcedby natural consequences. Early inves-tigation of the effects of frequencybuilding on application and transfer ofskills (Binder, 1976) emphasized theimplications for creativity of build-ing high frequencies of behavior com-ponents. Classroom programs (Binder,


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1979c; Pollard, 1979; Solsten &McManus, 1979) in which students

were sometimes able to emit behaviorcomposites without direct training af-ter building frequencies of compo-nents, demonstrated this creativepotential in component-composite re-lations. Haughtons (1979, personalcommunication) attention to the workof deBono (1970) reflected an interestin identifying components that mustbe made fluent to support flexible,problem-solving, and creative reper-toires. Johnson and Layng (1992;Layng, Jackson, & Robbins, 1992)have recently linked the selectionist

language of basic research on contin-gency adduction and generativity(Andronis, 1983; Epstein, 1985) tofluency-based instructional design,and have made this generative effectof building component frequencies thehallmark of their instructional designmodel (Johnson & Layng, 1994).

Haughton (1982, personal commu-nication) hypothesized that the moretypes of behavior an individual canperform at high frequencies, the morelikely he or she will be able to learnnew classes of behavior and adapt to

new situations. he proposed a con-ceptual formula for the og, calculatedas the number of response classes in arepertoire multiplied by the frequen-cies at which they can be performed.(The term was a play on Ogden Lind-sleys name and by analogy to the erg,a measure of work in the metric sys-tem.) He suggested that ogs mightpredict learning ability for an individ-ual, and that a key to acceleratinglearning ability is to maximize thefrequency of as many critical behaviorcomponents as possible in the reper-

toire of an individual. Whether or notsuch a calculation is practically feasi-ble, the concept corresponds to theprinciple of generativity based on fre-quencies of behavior components.

AFFECTIVE CORRELATESOF FLUENCY

The affective correlates of fluent

behavior have been a topic of in-creasing interest among fluency re-

searchers. Binder et al. (1990) ob-served that students may emit inap-propriate or aggressive behavior whenasked to perform dysfluent skills formore than brief periods, and that suchbehavior disappears when teachersshorten practice durations. Binder(1990a), discussing the affective cor-relates Of fluency, observed thatsalespeople report feeling more con-fident after achieving fluent verbal be-havior required for their jobs. Lind-sley (1992) points out that fluency isfun. These observations are consistent

with Haughtons (1980) guideline thatabove a certain performance level, es-timated as about half of REAPS, indi-viduals will continue to practice withlittle or no explicit feedback, or ar-ranged reinforcing consequences otherthan the usual chart-based feedbackprocedures. The affective correlates offluency deserve further research, andmay provide a basis for analyzingsome of the concepts related to innermotivation espoused by traditionaleducators.

REMOVING CEILINGS: TOWARD AFLUENCY-BUILDING TECHNOLOGY

Work at B. H. Barretts BehaviorProsthesis Laboratory in Waltham,Massachusetts, during the 1970s andearly 1980s focused on developmentof frequency-based instructional tech-nology. Early laboratory studies (Bar-rett, 1965, 1969, 1971; Barrett &Lindsley, 1962) and subsequent class-room application (Barrett, 1977b) setthe stage for this work by emphasizingthe free aspect of free-operant condi-tioning and the necessity of producing

minimal behavior frequencies in orderfor natural reinforcement contingen-cies to be effective. Our research overthe course of nearly a decade followeda progressive investigation of fourkinds of ceilings that can prevent orinhibit development of skill (Binder,1978b) and methods for removingthem. Although this section coverssome of the same ground as previous


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186 CARL BINDER

sections, it is useful because framingthat work in the context of the four

ceilings explains the conceptual andtechnological evolution in a way thatcan be applied progressively to im-prove any type of learning program

Removing Measurement-DefinedCeilings

Most instructional procedures at thetime used controlled operant trials,with an emphasis on stimulus controland an accuracy-only approach tomeasurement. Following programmedinstruction, these procedures did notmeasure behavior frequencies, be-

cause they were mostly controlled bythe experimenter or teacher. In con-trast, we always used frequencymeasures, even in conjunction withteacher-controlled trials procedures.We compared the actual frequenciesof performance allowed by procedures(trials per minute) with the fre-quencies of similar skills that mightoccur as free operants in natural set-tings (responses per minute). For ex-ample, typical trials procedures forteaching reading to severely disabledstudents occurred at 12 or fewer op-

portunities to respond per minute(Binder, 1977, 1978b), measured withan uninterrupted timer. By compari-son, competent oral readers perform at250 or more words per. minute. It be-came obvious that accuracy-basedmeasurement procedures are not sen-sitive to these important differences inbehavior and instructional procedures(Barrett, 1979). In that sense, they im-pose a ceiling on educators ability todetect the difference between compe-tent and incompetent performance,and may be among the most basic ob-structions to effective educationalpractice in schools and training pro-grams. Frequency measures allowedus to break through ceilings imposedby the 100% correct maximum in tra-ditional educational assessment meth-ods and to achieve a new level ofmeasurement sensitivity.

Eliminating Procedure-ImposedCeilings

The identification and removal ofmeasurement-defined ceilings madeprocedure-imposed ceilings obvious,and led to an effort over the course ofseveral years to design teaching andpractice procedures that freed studentsto behave at their own pace. Much ofthe instructional time in trials proce-dures involves a teacher or experi-mental apparatus presenting stimulione trial at a time, presenting conse-quences, and recording responses.Students are repeatedly required to

stop and start behaving in a way thatdoes not mimic the normal stream ofbehavior Working with prevocationaland preacademic skills, we createdmaterials and procedures designed toallow free-operant responding. Forexample, by shifting from a trials pro-cedure for teaching word naming oneword at a time to a procedure in whichwords were laid out in an array forstudents to name as rapidly as possi-ble, we were able to instantly triplebehavior frequencies in some studentswithout any additional intervention

(Binder, 1979d; George, 1975; Pease& George, 1975). Other examples in-cluded procedures that allowed stu-dents to continuously count objectsinto arrays of cups marked with num-bers, write numbers and letters onpractice sheets, and practice compo-nents of dressing skills by insertingthe arm into multiple cutaway shirtsleeves, one on top of another, asrapidly as possible. With these proce-dures it was possible to fade out ex-trinsic antecedent prompts, correctivefeedback, and other trial-by-trial inter-

ruptions as rapidly as possible, anddevelop procedures to accelerate un-interrupted correct responding. Weused response opportunities per min-ute as an indicator of instructional ef-ficiency.

Our goals were threefold: (a) to freethe behaver to respond at his or herown highest frequency, (b) to providemany opportunities to respond, and (c)


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to eliminate interruptions of attentioninvolved with trials procedures. These

transitions from trials to self-pacedprocedures often occurred after stu-dents had achieved between 66% and100% accuracy, although we wereable to change some acquisition pro-cedures even before achieving accu-racy.

Beyond the immediate multiplica-tion of frequency allowed by changesin procedure, it became possible to useboth antecedent stimuli and conse-quences to build frequencies of correctresponding without slowing down be-havior. High-paced prompting with

finger-pointing, touching, and verbalcues often accelerated behavior fre-quencies. Accentuating amount ofwork completed (e.g., markers everynth item on an array of stimuli) andamount of time passed (e.g., largesweep-second hands on darkroomtimers) enhanced the effects on be-havior frequencies of fixedratio rein-forcement schedules. Procedurescalled coaching and cheerleadingcombined energetic hustling antece-dents with enthusiastic social conse-quences, often making the classroom

appear more like an athletic gymthan.. a school (Binder, 1976, 1977;Binder & Haughton, 1982). The termfluency coaching may have originatedwith these procedures.

During the same period we ob-served that performance durations asbrief as 5 or 10 min were too long forstudents who had not achieved mini-mal behavior frequencies (Binder,1982, 1984; Binder et al., 1990).Shortening practice durations acceler-ated and stabilized performance inmany cases, often reducing error fre-

quencies without any other in-tervention.

An unexpected side-effect of theseprocedures was a significant reductionin problem behavior. Students whohad exhibited such inappropriate be-havior as jumping up from their seats,biting their hands, or throwing objectsoften stopped exhibiting these types ofbehavior when allowed and encour-

aged to behave continuously withoutinterruption for brief intervals. Posi-

tive affect in the form of smiles andlaughter often replaced negative be-havior.

These developments would proba-bly not have occurred had we beenworking with nondisabled students.Most precision teaching procedures inregular public school classrooms al-ready involved relatively self-pacedbehavior using such materials as prac-tice sheets or pages of reading mate-rial. Only with the severely disabled,for whom trials procedures were thenorm, did it become obvious how

these procedures imposed severe re-strictions on the development of com-petence (Barrett, 1979). Having rec-ognized these restrictions, however,we became more sensitive to analo-gous limitations imposed by materialsand procedures in regular classrooms.For example, competent adults canwrite as many as 120 answers to sim-ple math problems in a minute, butmost public school classrooms neitherallow nor encourage students to com-plete that much work in a single prac-tice episode. Even most classroom

procedures for professional adultsprevent individuals from respondingat optimal pace. It is an essential fea-ture of fluency-based instruction toremove such procedure-imposed ceil-ings as rapidly as possible.

Remediating Deficit-Imposed Ceilings

Only with procedures that allowedbehavior frequencies to seek their ownlevels did it became clear that studentswere unable to perform certain be-havior components at competent fre-quencies. Resnick, Wang, and Kaplan

(1973) influenced our work by pro-viding an example of thorough com-ponent-composite task analysis in ba-sic mathematics, which we adaptedfor use with severely disabled students(Pease, 1975). The focus of researchshifted to assessment and remediation.of component behavior deficits andthe effects on composite behavior ofincreasing component frequencies.


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188 CARL BINDER

We worked closely with Haughtonand his associates and with colleagues

in the Boston area (Binder, 1978a,1979a; Binder & Haughton, 1982;Bourie & Binder, 1980; Pollard, 1979;Solsten & McManus, 1979). By intro-ducing precision teaching methods tooccupational therapists (Binder,1981b), language therapists (Binder,1981a; Burgoyne, 1982), physicaltherapists (Imbriglio, 1992), and entiremultidisciplinary teams (Binder &Pollard, 1982; Pollard & Binder,1983), established a common meas-urement language and assessmentstrategy for identifying and remediat-

ing deficit ratios between studentsbehavior frequencies and aims basedon competent adult performance.

We created free-operant analoguesof mediated transfer stimulus equiva-lence procedures (Binder, 1979d) andengaged in research using free-operantbursts on computer keyboards to in-vestigate the effects on chains of in-creasing the frequencies of smallerkeystroke sequences (Blakeslee, Bar-rett, & Buchman, 1985). This workexpanded component-compositeanalysis beyond academic skills to

broader repertoires of complex be-havior and focused on remediation ofcomponent behavior deficits.

Handicap-Defined Ceilings

Finally, having removed the firstthree types of ceilings, we acknowl-edged the existence of nonremediablecomponent dysfluencies in disabledrepertoires. Although accuracy meas-ures were often incapable of distin-guishing between obviously disabledbehavior and competent performance,

Barrett (1979) demonstrated that fre-quency measures of freely emitted be-havior could help to define success orfailure in application of thethen-popular normalization princi-ple in special education. In the faceof persistent dysfluencies, the alterna-tive was to identify alternative reper-toires or to create behavior prosthe-ses to compensate for the deficits

(Barrett, 1977a).

Fluency Blockers and Builders inInstructional Design

Some time after active fluency re-search had ceased at the BehaviorProsthesis Laboratory, Binder (1989,1990a) classified fluency blockers andfluency builders according to catego-ries originating in the ceilings definedin the laboratory (Figure 6). Thesewere factors in the instructional orperformance environment related tomeasurement, procedures, materials,skills (component responses), and

knowledge (component discrimina-tions and verbal behavior). As aframework for diagnosing problemslimiting any type of instructional pro-gram, these categories serve as a use-ful checklist for improving instruc-tional effectiveness.

In retrospect, an important contribu-tion of our research at the BehaviorProsthesis Laboratory was the intro-duction of fluency concepts and meth-ods to Kent Johnson during nearly 2years of informal discussion towardthe end of the 1970s. Johnson and his

associates (Johnson & Layng, 1992,1994) have subsequently made im-portant contributions by integratingand expanding many of these conceptsand methods in the Morningsidemodel of generative instruction.

CORROBORATING EVIDENCEOUTSIDE BEHAVIOR ANALYSIS

Evidence in other traditions of re-search and educational practice sup-ports many of the general conclusionssuggested in this article. Robbins(1994) recently conducted a review of

the cognitive literature on automatic-ity (Bloom, 1986), and previous refer-ence to LaBerge and Samuels (1974)in this article acknowledges the im-pact of that work on our study of en-durance and distractibility. Binder(1979b, 1979c) conducted an exten-sive review, available on request, thataimed to uncover all precedents forusing time-based measures in research


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Comparatively few early research-ers measured latencies in paired

associate or other forms of verballearning, probably because theinstrumentation required was expen-sive and inaccurate until recent years,and because subjects require extensivepretraining to respond with reliablespeed in trials procedures (Runquist,1966). However, those who didmeasure latencies consistently foundthat they continued to decline withlearning trials beyond 100% accuracyand that shorter latencies predict betterretention and transfer of training (Hall& Wenderoth, 1972; Judd & Glaser,

1967, 1969; Keller, Thomson, &Tweedy, 1967; Milward, 1964;Osgood, 1946; Peterson, 1965;Suppes, Groen, & Schlag-Ray, 1966;Theios, 1973). Osgood (1946), forexample, concluded that latencymeasures provide a more sensitiveindicator of habit strength (p. 46)than does accuracy-only.

Measurement Problems inTrials Procedures

Some traditional researchers identi-fied methodological problems with

trials procedures. Hall (197 1, p. 429)recognized that practice beyond 100%accuracy represents a more stringentcriterion, yet acknowledged that inthe absence of measures beyond the100% accuracy ceiling, no directspecification of that criterion is possi-ble.

Kruger (1929) tried to scale thevalue of an overlearning trial by set-ting the number of trials required foran individual to reach 100% accuracyas a unit, and then providing all indi-viduals with 1.5 times that number oftrials. His hypothesis was that if oneoverlearning trial has the same effectas another for a given individual, allsubjects given 1.5 times the number oftrials required to achieve accuracywould show approximately the sameproportional increases in retention.Results did not support this hypothe-sis.

Peterson (1965) argued further that

in trials procedures, because latenciesdecrease with overlearning trials, the

accuracy assigned to a series of trialsdepends artifactually on the time al-lowed to respond after presentation ofthe stimulus (the anticipation interval).He wrote It is clear that the ability ofthe S to respond correctly is a functionof the length of time allowed for himto respond. In a learning experimentwith a fixed anticipation interval,relative frequencies of correct re-sponse will not be independent of la-tency (p. 167). If given a longer an-ticipation interval, a subject has ahigher probability of responding cor-

rectly than if given a very short inter-val in which to respond. Thus, accu-racy criteria from one experiment toanother may not be comparable, de-pending on the anticipation intervals.In effect, accuracy only assessmentdoes not support a measurement stan-dard, unless other aspects of the pro-cedure are carefully controlled. Thegeneral conclusion from these andother studies is that time-based meas-ures are more sensitive and more reli-able indicators than is accuracy-only.

Component-Composite RelationsOutside Behavior Analysis

A variety of studies in the percep-tual-motor literature corroborate thefinding that increasing performancespeed of component behavior pro-duces improved performance of com-posites. For example, Bilodeau andBilodeau (1954) found that improvingspeed of performance of a less profi-cient component of a psychomotortask boosts overall composite per-formance. Gagne et al. (1950) foundpositive transfer to a perceptual motortask from practice on a discriminationcomponent. Gagne and Foster (1949)observed positive transfer from prac-tice on components of a motor task.

INTEGRATION ANDCOMMERCIALIZATION OFFLUENCY-BASED INSTRUCTION

Much work during the last two dec-ades has been devoted to integration


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and commercialization of fluency-based instructional technology. Early

precision teaching demonstrationprojects persuaded the federal gov-ernment to fund dissemination of flu-ency-based instruction in publicschools for nearly 10 years (Beck,1979; Beck & Clement, 1991). Subse-quent work in Ontario, Florida, andUtah provided more evidence of suc-cess.

Private Sector Businesses

During the 1980s and 1990s, anumber of fluency-based instructionaltechnologists began private sector

businesses aimed at promulgating andsupporting further development ofthese methods, with the rationale thatestablishing a successful commercialenterprise offered the best chance ofsupporting continued research and de-velopment independent of grants andpublic sector fads (Binder, 1993b;Binder & Watkins, 1989). These in-cluded Michael Maloney QuinteLearning Centers, Ontario), CarlBinder (Precision Teaching and Man-agement Systems, Inc., and ProductKnowledge Systems, Inc., Boston),

Kent Johnson (Morningside Academy,Seattle), Elizabeth Haughton (Haugh-ton Learning Center, Napa, Cali-fornia), Ian and Aileen Spence (BenBronz Academy, Hartford, Connecti-cut), and Anne Desjardins (CacheValley School, Utah), among others.

Efforts to Computerize Fluency

A number of efforts to computerizefluency-based instruction have metwith mixed results. Maloney andSummers (1982) produced MightyMath , one of the first commerciallyavailable software packages for de-veloping fluency in basic academicskills. Ben Bronz Academy has alsodeveloped computer programs forpracticing basic math skills (Spence,1996). Perhaps the most sophisticatedenterprise in this area, BehaviorTech,Inc. (Orgel, 1984), produced a com-puter-based learning system calledExemplar that ultimately failed in the

corporate marketplace due to lack ofinterest rather than lack of results.

Claudia McDade and her colleagues(including former colleague CharlesOlander) at the Center for Individual-ized Instruction at Jacksonville StateUniversity have produced a number ofcomputer-based testing and instruc-tional software packages. Joseph Par-sons, of the University of Victoria,developed ThinkFast ; James Cow-ardin, John Eshleman, and their asso-ciates produced a computer-basedtraining system (now owned by Preci-sion Learning Systems, Inc., Atlanta)aimed at producing fluency. The con-

sistent challenge in these and other ef-forts to build fluency with computershas been to escape the limitations ofcontrolled operant procedures and toraise ceilings on the speed at whichlearners can interact with computers ina continuous stream of behavior. Inaddition, most current computer-basedfluency pro grams suffer from ceilingsimposed by component typing-skillsdysfluencies among most learnerpopulations. High speed voice- recog-nition technology may offer hope forovercoming this problem in the future.

CONCLUSION

Little known to most of our behav-ioral colleagues, there is a rich historyof conceptual and technical evolutionfocused on development of behavioralfluency. This article has attempted tosummarize the key stages and aspectsof that evolution for the benefit ofthose only recently becoming inter-ested in this field.

Fluency offers a new organizingframework (or paradigm) for re-searchers and practitioners accus-

tomed to accuracy-only measures ofeducational mastery and deficit andaccuracy-only criteria for advancingthrough curriculum sequences. Al-though a great deal more systematicresearch and development must takeplace in order to pin down key vari-ables and parameters, there is noquestion that the addition of frequencyaims and frequency-building proce-


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192 CARL BINDER

dures can improve instructional effi-ciency and effectiveness.

Paradoxically, fluency-based in-struction represents a return to whatnearly all cultures and individualswith traditions of skilled performancealready know: Fluent, well-practicedbehavior is the characteristic of truemastery in any field of skilled en-deavor. Musicians, athletes, martialartists, skilled craftspeople, and manyothers already understand the impor-tance of fluency and practice. For be-havior analysts, the challenge is to in-corporate these principles into our re-search agendas and technologies,

augmenting the methodology of free-operant conditioning with a fresh un-derstanding of component-compositebehavior relations.

It may not be too optimistic to pre-dict that with continued and acceler-ated development of fluency-based in-structional technology, many of ourmost pressing educational problemswill become far less daunting. Such adevelopment, however, depends onbroad cultural appreciation of this newunderstanding, a goal now. being ad-dressed by some of those involved in

fluency research and practice (Binder,1993b; Binder & Watkins, 1989; Pen-nypacker & Binder, 1992).

Fluency is a new paradigm for re-search to the extent that it integratesand redirects our scientific and tech-nological endeavors with a new defi-nition of masteryone that requiresinclusion of the time dimension. It is anew paradigm in education to the ex-tent that it changes teaching practicesand enables us to multiply the cost ef-fectiveness of education and trainingprograms.

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Andronis, P. T. (1983). Symbolic aggres-sion by pigeons: Contingency coadduction.Unpublished doctoral dissertation, The Univer-sity of Chicago.

Barrett, B. H. (1965). Acquisition of oper-ant discrimination and differentiation in insti-tutionalized retarded children. American Jour-nal of Orthopsychiatry, 35, 862-885.

Barrett. B. H. (1969). Behavioral individu-ality in four cultural-familially retarded broth-

ers.Behavior Research and Therapy, 7. 79-91.Barrett, B. H. (1971). Behavioral differ-

ences among an institutions back ward resi-

dents.Mental Retardation, 9(1), 4-9.Barrett, B. H. (1977a). Behavior analysis. In

J. Wortis (Ed.), Mental retardation and devel-opmental disabilities (Vol. 9. pp. 141-202).

New York: Brunner/Mazel.Barrett, B. H. (1977b, October). Some pa-

rameters of pupil freedom . Presented at themeeting of the American Association for theEducation of the Severely and ProfoundlyHandicapped, San Francisco.

Barrett, B. H. (1979). Communitization andthe measured message of normal behavior. InR. York & E. Edgar (Eds.). Teaching the se-verely handicapped(Vol. 4. pp. 301-318). Co-lumbus, OH: Special Press.

Barrett, B. H., & Lindsley, 0. R. (1962).Deficits in acquisition of operant discrimina-

tion and differentiation shown by institutional-ized retarded children. American Journal of

Mental Deficiency, 6(3), 424-436.Beck, R. (1979). Report for the Office of

Education joint dissemination review panel.Great Falls, MT: Precision Teaching Project.(Available from Ray Beck. Sopris West, 1140Boston Ave., Longmont, CO 80501)

Beck, R... & Clement, IL (1991). The GreatFalls Precision Teaching Project: An historicalexamination. Journal of Precision Teaching,8(2), 8-12.

Berquam, S. (1981). The relation betweenfrequency of response and retention on apaired-associate task. Unpublished doctoraldissertation, University of Florida.

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