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TRANSCRIPT
A
MODEL
FOR
CONDUCTING
A
FUNCTIONAL
ANALYSI
S
OF
ACADEMIC
PERFORMANCE
PROBLEMS Abstract: The
purpose of this article is to describe a simple conceptual
framework for academic intervention that extends functional
analysis procedures to basic academic skills. We organized
the empirical research on academic intervention into five
hypotheses that can guide the selection of interventions. T
reatment protocols for six academic interventions and proced
ures for quickly testing their effectiveness are presented.
The description of procedures for simple tests includes di
scussion of design issues, measuring outcomes, and guideline
s for selecting treatments.
There is an expectation in our
society that children will attend school and that school,
at the very least, will impart important academic skills. C
hildren are expected to be able to perform basic skills suc
h as reading, mathematics, and writing. Some fail to accomp
lish what is expected of them. When what a child can actua
lly do is at variance with what is expected of that child,
there is often an interest in understanding or explaining
the deficits in academic performance. The goal of this arti
cle is (a) to suggest some explanations for the failure of
some children to perform, (b) to offer a series of direct
"tests" for each of the common reasons children fail, and
(c) to provide some simple interventions which are linked
directly to the outcome of the tests.
The reasons why chil
dren fail are complex. Reading, for example, involves trans
lating symbols from a page into meaningful language, and in
numerable parts or combinations of this visual and neurolog
ical process can malfunction. When school psychologists are
asked by a parent or teacher why a child does not learn,
often the correct answer is, "I don't know." This response,
however, is not very satisfying to either the speaker or
the listener. Therefore, we often attempt to explain studen
t failure using diagnostic terms such as dyslexia or learni
ng disability or mental retardation. Relating academic perfo
rmance to other observed or inferred student characteristics
represents a structural approach to understanding failure
(Nelson & Hayes, 1986). Structural explanations rely upon p
revious correlational research to help school psychologists
organize what is often a complex picture of student strengt
hs and weaknesses, and structural labels typically occasion
major changes in a child's life such as special education
placement. From an intervention perspective, however, struc
tural explanations for student failure are limited in two r
espects. First, student performance as well as the traits i
nferred from such performance cannot be manipulated directly
(Nelson & Hayes, 1986). Rather, student behavior can only
be altered indirectly by manipulating one or more factors e
xternal to the child (e.g., what is taught or the amount o
f teacher assistance provided). Second, because structural e
xplanations emphasize student traits as causal agents, they
do not identify those factors external to the child that
may be contributing to academic failure.
Relating academic
performance to aspects of classroom instruction that prece
de and follow student performance represents a functional a
pproach to understanding failure. Functional explanations ap
peal to factors external to the child that have been shown
experimentally to affect academic performance such as time
for learning, feedback from the teacher, and reinforcement
for correct responding. Because these factors are external
to the child and subject to direct manipulation, functiona
l explanations have the added advantage of identifying simp
le, practical targets for instructional programming.
Functi
onal explanations of poor academic performance will perforce
be related to what teachers do to teach students: arrangin
g the instructional environment, sequencing how instruction
is delivered as students progress through the curriculum, p
roviding sufficient opportunities to respond, and/or structu
ring contingencies. Each of these teaching actions occurs a
s a result of instructional decision making. When students
are not learning, the task is to analyze how these factors
affect student performance to make explicit decisions abou
t how best to teach the child. In this article, five commo
n reasons why students fail will be described. These reason
s are related to what teachers should be doing in the clas
sroom to teach students effectively. Next, some simple meth
ods for testing these hypotheses quickly and efficiently wi
ll be presented. This section will include protocols for ef
fective academic interventions, how to structure these simpl
e tests (i.e., design issues), and important outcome measur
es that can be obtained reliably. The goal of this article
is to demonstrate that it is possible to apply the logic
and procedures of functional analysis to academic skills, p
rovide a framework for doing so, and offer protocols for ef
fective academic interventions.
Five Common Reasons Why Stu
dents Fail and What You Can Do About Them The field of beh
avior analysis has been particularly successful at linking
assessment to effective interventions by identifying the fun
ctions of behavior. When the contingencies maintaining behav
ior are known, it is possible to rearrange the contingencie
s so that appropriate and adaptive choices are more likely
and inappropriate responses are less likely (Martens, Witt,
Daly, & Vollmer, in press). For example, when it is known
that adult attention maintains tantrum behavior in a child
(i.e., tantrum behavior functions to gain attention), the
intervention might consist of not providing attention while
a child is having a tantrum, but providing attention when
it is sought in an appropriate manner. This line of resea
rch has been applied largely to aberrant social behaviors a
nd has not yet been applied to academic skills in a way th
at systematically compares variables functionally related to
student learning. A functional analysis of academic behavi
ors would provide information about the relative effects of
different teaching strategies (e.g., modeling, error correc
tion, or practice) on student performance. When the effects
of different instructional strategies are known (e.g., mod
eling versus practice), it is possible to alter instruction
to maximize the likelihood that the intervention will be s
uccessful.
Five common factors known to affect student aca
demic performance can be used as a starting point for gener
ating hypotheses that lead to interventions. Five of the mo
st common reasons students perform academic work poorly are
that (a) they do not want to do it, (b) they have not sp
ent enough time doing it, (c) they have not had enough hel
p to do it, (d) they have not had to do it that way befor
e, or (e) it is too hard (see Figure 1).
In Figure 1, eac
h is referred to as a "Reasonable Hypothesis" because they
are factors over which educators have control and are funct
ional reasons for why students fail to succeed in school. M
ore detail is provided about no. 3 because of the different
forms of assistance that students may need. These hypothes
es are not intended to be independent of each other. Academ
ic skills require a sequence of instructional activities th
at build upon one another to increase response rates in the
presence of curricular materials. Therefore, the strategies
that teachers use are often interrelated (e.g., modeling a
nd error correction are always confounded with opportunities
to respond). Therefore, the hypotheses are intended to ref
lect increasing levels of intrusiveness of academic interven
tion rather than independent hypotheses.
Time is a preciou
s commodity. Educators need to be efficient when problem so
lving. Under many circumstances, the most efficient thing t
o do is to test the easiest hypothesis first, implement an
intervention, and monitor and evaluate outcomes. If that a
pproach fails to improve student performance, then something
progressively more time intensive can be attempted until t
he probable cause of failure is identified. Also, easier so
lutions are more likely to be implemented consistently whil
e solutions which are more time consuming or technically di
fficult for teachers and support personnel are less likely
to be implemented correctly (Gresham, 1989). Therefore, the
"reasonable hypotheses" presented in Figure 1 are ordered
from those requiring the least intervention to those requir
ing the most intensive instructional intervention based on
logical considerations of classroom environments in general
.
This sequence is intended to be heuristic and not an in
variant sequence to be implemented by practitioners. When c
onducting assessment and intervention for academic problems,
practitioners might want to consider factors like teacher
skill, classroom routines, time required for implementing an
intervention, and student skill level in the hypothesis ge
neration phase. Therefore, practitioners might consider alte
rnate sequences of the proposed hypotheses depending upon t
he unique characteristics of the settings in which they wor
k (e.g., changing instructional materials might be very acc
eptable in some settings and not in others). In some cases,
practitioners might already have assessment data that indi
cate that one hypothesis is more likely than another (e.g.,
variable student performance might suggest a performance d
eficit; alternately, many errors might suggest an accuracy
problem). A direct test might then be conducted to confirm
or disconfirm the hypothesis.
We will examine the empirica
l support for the role of each hypothesis in student academ
ic performance and related interventions before we describe
strategies to test for each hypothesis. Figure 2 contains
examples for interventions that have been effective at impr
oving student performance, and each is associated with pres
umed functions of the target behavior. Appendix A contains
references that can be useful in developing interventions f
or each of these areas.
They Do Not Want To Do It Is the
student not able to perform the skill (a skill deficit) o
r is the student able to perform the skill, but "just does
n't want to?" The distinction between skill and performance
deficits was clarified by Lentz (1988, p. 354) who stated,
"Skill problems will require interventions that produce ne
w behavior; performance problems may require interventions i
nvolving manipulation of 'motivation' through contingency ma
nagement." It is relatively easy to test the hypothesis of
a performance deficit. Incentives for reading (Ayllon & Rob
erts, 1974; Staats & Butterfield, 1964; Staats, Minke, Finl
ey, Wolf, & Brooks, 1964) and math (Broughton & Lahey, 1978
) have been effective in improving students' motivation and
performance (i.e., increasing active participation and decr
easing disruptive behaviors). If a student fails to respond
to incentives for increased academic performance, then eit
her the wrong incentives were used or the student does not
have the skills to perform the task.
The literature conta
ins numerous examples of interventions that can be used to
test this hypothesis. For example, Lovitt, Eaton, Kirkwood,
and Pelander (1971) improved students' oral reading fluenc
y by offering incentives for reading faster. Another strate
gy for improving students' motivation that is relatively ea
sy to implement for most teachers is offering students a ch
oice of work to be performed (e.g., a story about baseball
versus a fairy tale) or the order in which work is perfor
med (e.g., allowing the child the choice of a vocabulary dr
ill or a silent reading first). Students' on-task behavior
has been improved by giving students a choice of instructio
nal activities (Dunlap et al., 1994; Seybert, Dunlap, & Fer
ro, 1996), a strategy that can be easily adapted to most i
nstructional formats. It is noteworthy that in some of this
research students displayed high rates of on-task behavior
on those very assignments that they refused to do previous
ly. The only difference between assignments completed and a
ssignments refused was that students were allowed to choose
among several instructional assignments during seatwork. Wh
en students were allowed to choose the assignment, their co
mpliance and on-task behavior improved.
They Have Not Spen
t Enough Time Doing It A student may not be progressing ac
ademically because he or she simply has not spent enough ti
me actively practicing the skill. There are large differenc
es in the amount of time students spend actively engaged in
academic responding (Rosenshine, 1980). Large differences a
lso have been observed across socioeconomic levels (Greenwoo
d, 1991). For instance, longitudinal studies conducted by r
esearchers at the Juniper Gardens Children's Project have i
dentified large cumulative differences across socioeconomic
levels in the amount of time students are actively respondi
ng. These differences amount to greater than 1.5 years more
schooling by the end of middle school for students of hig
her socioeconomic levels than for students of lower socioec
onomic levels (Greenwood, Hart, Walker, & Risley, 1994).
In a review of the literature on academic engaged time, Yss
eldyke and Christenson (1993) concluded that variability acr
oss classrooms and schools leads to large differences in th
e amount of time that students are academically engaged. Th
ese differences increase the salience of engaged time as an
important variable in the investigation of a student's aca
demic problems and underscore the importance of examining t
his factor on an individual basis. The implications for int
ervention are obvious. As a first step, a student's current
rate of active responding in the problematic subject area
or time of day should be estimated. This task can be accom
plished through recent advances in observation techniques su
ch as the Ecobehavioral Assessment Systems Software (Greenwo
od, Carta, Kamps, & Delquadri, 1995) and the Behavior Obser
vation of Students in Schools (Shapiro, 1996), two observat
ion tools that provide estimates of student active engageme
nt. The second step involves increasing the student's activ
e responding. Various strategies such as providing highly s
tructured tasks, allocating sufficient time for instruction,
providing continuous and active instruction, maintaining hi
gh success rates, and providing immediate feedback have bee
n shown to improve student engagement rates (Denham & Liebe
rman, 1980; Stallings, 1975; Ysseldyke & Christenson, 1993).
Even simpler solutions may be equally as effective; for in
stance, allocating more time for student responding and dec
reasing intrusions (e.g., transition time) into instructiona
l time (Gettinger, 1995; Rosenshine, 1980).
They Have Not
Had Enough Help To Do It Feedback. Ysseldyke and Christenso
n (1993) warn that engaged time is only moderately (though
significantly) related to student achievement. Increasing ti
me for engagement may not be sufficient ira student needs m
ore help to perform instructional tasks successfully. Feedba
ck for student responses may be necessary to assist a stude
nt to respond accurately and quickly (Heward, 1994). Feedba
ck is an integral part of the learning trial and consists
of an instructional antecedent (e.g., "Who was the first pr
esident of the United States?"), an active student response
(e.g., "George Washington."), and a consequence (e.g., "Co
rrect!"). When teachers actively provide feedback to student
s for responding, they increase the likelihood of student a
chievement (Rosenshine & Berliner, 1978).
Belfiore, Skinner
, and Ferkis (1995) showed that complete learning trials we
re more effective in helping students to master sight words
than merely having students repeatedly say the correct res
ponse. A learning trial consists of an antecedent (e.g., a
flashcard with "3 x 3") prior to a response and a conseque
nce (e.g., "Correct!" or "No, the correct answer is 9.") fo
llowing a response. Another strategy for increasing feedback
via complete learning trials is choral responding. Choral
responding involves having all students respond verbally dur
ing group lessons. Choral responding has been shown to impr
ove learning rates for diverse groups of students, includin
g preschool children with developmental disabilities, childr
en identified as Severe Behavior Handicap, first grade Chap
ter 1 students, general education students, and students id
entified as Developmentally Handicapped in special education
classrooms (Heward, 1994). Choral responding has been show
n to be more effective at improving learning rates when com
pared to on-task instruction in which the teacher praised s
tudents for paying attention while asking the same number a
nd type of questions (Sterling, Barbetta, Heron, & Heward,
1997).
Another strategy for increasing feedback to students
for nonverbal responses is response cards. To use response
cards, teachers can instruct students to write the correct
response on laminated cards during group instruction for m
ath, spelling, or content lessons. When the teacher asks a
question, the students are expected to write their answers
on the cards and to hold up the correct response. The teac
her scans students' responses and provides feedback to stud
ents. Heward (1994) provided guidelines for implementing the
use of response cards. Response cards have been shown to
improve (a) rates of responding and quiz scores relative to
hand-raising during fourth-grade recitation social studies
lessons (Narayan, Heward, Gardner, Courson, & Omness, 1990),
(b) ontask behavior of students with disruptive and off-ta
sk behavior during social studies lessons (Gardner, Heward,
& Grossi, 1994), and (c) quiz scores in earth science cla
sses for high school students (Cavanaugh, Heward, & Donelso
n, 1996). The common strand of these strategies is that the
y increase the amount of feedback given to students immedia
tely following responding. It is an opportunity to provide
positive feedback for correct responses and to correct erro
rs immediately rather than allowing a student to practice t
he wrong answer.
The Instructional Hierarchy. In the event
that increasing feedback during time allocated for instruc
tion is not sufficient for improving student performance, i
t may be necessary to look more carefully at a student's s
kill level as a basis for developing instructional interven
tions. How much assistance a student requires is dependent
upon his or her level of skill mastery. Mastery, in turn,
develops in a sequence of stages which lead to proficiency
and use of the skill across time and contexts (Daly, Lentz,
& Boyer, 1996; Haring, Lovitt, Eaton, & Hansen, 1978; Howe
ll, Fox, & Morehead, 1993). Initially, effective instruction
promotes accurate performance of the skill. At this stage,
modeling the skill and observing the learner are critical
components of good instruction. Also, explicit feedback abou
t performance is necessary. After the learner becomes accur
ate, the next step is to become fluent in the use of the s
kill. For a skill (e.g., "4 x 2 = 8") to be useful in the
future (e.g., with long division), the learner must be abl
e to respond rapidly when presented with the problem. Pract
ice improves skill fluency. Accurate and fluent skill use i
ncreases the chances that the learner will generalize the s
kill across time, settings, and other skills (Daly, Martens
, Kilmer, & Massie, 1996; LaBerge & Samuels, 1974; Wolery,
Bailey, & Sugai, 1988).
Daly, Lentz, and Boyer (1996) used
the heuristic notion of an "instructional hierarchy" devel
oped by Haring et al. (1978) to show that in many studies
of academic interventions the effectiveness of instructional
strategies for improving student accuracy and fluency coul
d be predicted based on the strategies used (e.g., modeling
versus practice). Although other instructional hierarchies
have been developed during the past century, Haring et al.'
s (1978) model is particularly useful because it explains p
atterns of results and allows us to predict which intervent
ions are most likely to be effective based on the component
s of instruction and where students are in the learning seq
uence. The instructional hierarchy suggests that strategies
that incorporate modeling, prompting, and error correction c
an be expected to improve accuracy and that strategies that
incorporate practice and reinforcement for rapid responding
can be expected to improve fluency. In a demonstration of
the predictive power of this particular instructional hier
archy, Daly and Martens (1994) accurately predicted the pat
tern of results of three reading interventions based on the
instructional strategies incorporated by each.
They Have
Not Had To Do It That Way Before Thus far, we have conside
red contingencies for performance, the amount of time stude
nts are actively engaged in schoolwork, how much feedback t
eachers give, and some important teaching strategies. If in
terventions based on these factors do not lead to improveme
nts in academic performance, then the next factor to examin
e is the role of the instructional materials of improving s
tudent outcomes. When analyzing the instructional tasks and
assignments as possible factors related to poor student pe
rformance, there are at least two reasons why they may be
hindering student learning: either they are not helping the
student to practice actual skill use (Vargas, 1984) (Reaso
n # 4) or they are too difficult (Reason #5) (Gickling & A
rmstrong, 1978; Gickling & Rosenfield, 1995).
The goal of
instruction is to bring student responding under the contro
l of the instructional materials (i.e., they do not need he
lp to get the correct answer) so that students can apply t
heir skills to real life demands (Heward, Barbetta, Cavanau
gh, & Grossi, 1996). For instance, when teaching a student
to read the word "Name," the goal is for the printed word
"Name" to elicit the student response (i.e., reads "Name").
In this way, the student will be able to respond to many
demands for use of the skill such as completing a job appl
ication. Practicing the skill means that instructional mater
ials provide enough examples and nonexamples of the target
skill so students know when to use the skill (Englemann &
Carnine, 1991). For instance, if you are teaching colors to
students, you want to be sure to present enough examples
of the colors being taught using several different shapes s
o that they do not confuse the color red with the shape of
the objects you are using. Therefore, the instructional ma
terials are critical for providing students enough opportuni
ties to practice important academic skills while helping th
em recognize when to use the skill and when not to use the
skill.
Practicing the skill also means that students need
to know how to obtain the correct answer. Unfortunately, m
any instructional materials allow students to achieve the c
orrect answer for the wrong reason (Vargas, 1984). Take Bil
l, for instance, who knows how to complete his vocabulary w
orksheets by looking at the number of blanks where he is s
upposed to write the response and counting the number of le
tters in each of the vocabulary words. He is not learning
his vocabulary words. Instead, he has devised a strategy fo
r obtaining the right answer for the wrong reasons. Finally
, the instructional materials might not have the student re
spond in the way that he or she would be expected to respo
nd according to the curriculum. Therefore, teaching spelling
by having students circle spelling words in word puzzles o
r having them circle the correct answer among four options
is inadequate preparation for spelling words for which dict
ation is required.
Interventions aimed at improving the in
structional materials should be directed toward using materi
als that elicit the kinds of student responses that would b
e expected of students who have mastered the curriculum. As
a first step, the student's objectives should be defined (
e.g., will spell words with common consonant combinations a
ccurately). The, there must be assurance that the instructi
onal materials provide enough practice in actual use of the
skill (e.g., have students practice spelling words with co
mmon consonant combinations rather than having them circle
correct spellings). Worksheets that only require the student
to respond to a few items (e.g., each vocabulary word app
ears once) in a limited format (e.g., circling correct word
s versus spelling words) are not likely to improve student
performance. Therefore, whereas Hypothesis #2 suggested that
a student might not be spending enough time practicing the
skill to perform it better, Hypothesis #4 suggests that th
e student might not be spending time on the right kinds of
instructional activities to perform the skill better, indi
cating a need for a different type of intervention.
It Is
Too Hard Finally, the student might not be successful beca
use the instructional materials are too difficult. Gickling
and Armstrong (1978) improved students' accuracy of assign
ed work and on-task behavior by changing instructional mate
rials to assure that they were not too difficult or too ea
sy. Students are more likely to generalize what they have j
ust learned to other similar instructional materials when t
hey are instructed at their instructional level. Daly, Mart
ens, Kilmer, and Massie (1996) found that providing reading
instruction at a level that was more appropriately matched
to students' skill levels resulted in greater generalizati
on than when instruction was provided in more difficult mat
erials. The importance of the effects of task difficulty on
student learning is obvious and cannot be overstated. Unfo
rtunately, often teachers have in their classrooms learners
with multiple skill levels, and changing instructional mat
erials to match each student's instructional level is a dif
ficult task. Therefore, it is one of the last things that
they may be willing to change. For this reason, we present
it last. If, however, instructional changes based on the o
ther factors are not effective, teachers will be hard press
ed to insist that they do not want to change the difficult
y level of the materials to meet the student's needs.
Simp
le Methods for Testing Hypotheses Within recent years, proc
edures for conducting a functional analysis of behavior hav
e virtually revolutionized the treatment of behavior problem
s in children and individuals with developmental disabilitie
s (Homer, 1994; Iwata, Pace et al., 1994; Mace, 1994). The
key steps in conducting a functional analysis of behavior
involve (a) generating hypotheses about the possible ways i
n which problem behavior is being reinforced, and (b) expos
ing the individual to brief test conditions designed to mim
ic the hypothesized contingencies (Martens et al., in press
). For example, based on teacher interview and classroom ob
servation, one might suspect that a child is engaging in di
sruptive behavior to obtain attention from the teacher or t
o avoid schoolwork. To determine which of these hypotheses
is correct, we might instruct the teacher to attend to the
child for every misbehavior during some class periods and
to send the child to the office for every misbehavior durin
g other class periods. If we observe increases in disruptiv
e behavior only under the condition during which attention
is provided, then we can conclude with confidence that the
child's behavior is being positively reinforced by attention
from the teacher.
The use of brief test conditions to id
entify the functions of problem behavior was pioneered by I
wata and his colleagues in their work with self-injurious b
ehavior (Iwata, Dorsey, Slifer, Bauman, & Richman, 1994). T
he impact of their work on the treatment of self-injurious
behavior can be attributed, in part, to the logic underlyin
g functional analysis procedures. First, although the possib
le functions of problem behavior are relatively few in prin
ciple (e.g., positive or negative reinforcement), it is ass
umed that these principles operate in ways that are idiosyn
cratic to the life of each individual. Second, because test
conditions are designed to mimic and intensify the conting
encies supporting problem behavior, individual responses to
these conditions are based in large part on previous learni
ng. Capitalizing on previous learning allows test conditions
to be brief in duration (e.g., 10 min) yet still produce
relatively large changes in behavior. Third, functional anal
ysis can be characterized as an experimentally based assess
ment procedure in that it allows one to identify the functi
ons of problem behavior by manipulating contingencies. Once
identified, these functions can be selectively eliminated,
reversed, or weakened by implementing the appropriate trea
tment procedure (Martens et al., in press). As an assessmen
t technique then, functional analysis has the potential to
make all existing interventions more effective by allowing
them to be matched with confidence to the causes of behavio
r problems. Interventions based on a functional analysis of
behavior have been dramatically effective in decreasing se
lf-injurious behavior, and test conditions also have been u
sed to identify the causes of classroom behavior problems w
ith excellent results (e.g., Broussard & Northup, 1995; Ker
n, Childs, Dunlap, Clarke, & Falk, 1994).
Unlike many beha
vior problems which are supported by operant contingencies
(i.e., performance deficits), students' academic problems of
ten result from an absence of skills or inadequate skill de
velopment (i.e., skill deficits). How then is it possible t
o conduct a functional analysis of something like poor read
ing? If the goal of a typical functional analysis is to id
entify the operant contingencies affecting problem behavior,
then the goal of a functional analysis of reading is to i
dentify the instructional contingencies affecting academic b
ehavior. Because skills cannot be unlearned once they are a
cquired, test conditions for academic problems involve brief
presentations of two or more instructional strategies beli
eved to improve performance applied to different sets of ma
terials (Cooper et al., 1992; McComas et al., 1996). The fo
llowing is a sequence of test conditions for identifying th
e instructional contingencies affecting academic performance
problems. These conditions were designed based upon a revi
ew of research suggesting the availability of(a) instruction
al strategies that are brief, easy to implement, and produc
e immediate and substantial improvements in performance (e.g
., repeated readings or listening passage preview); (b) mea
sures that are sensitive to short-term gains in academic pe
rformance (e.g., curriculum-based measurement probes); and (
c) a theoretical model for sequencing the presentation of t
est conditions (e.g., the instructional hierarchy).
Design
Considerations The most common approach to determining the
sequence of test conditions in a functional analysis is ref
erred to as a multielement design (e.g., Iwata, Pace et al.
, 1994). A multielement design typically begins with a base
line period to establish levels of the behavior in question
. Then, one or two sessions are conducted under the first
test condition followed by sessions conducted under the sec
ond, third, etc., condition until all conditions being comp
ared have been alternated repeatedly in a semi-random seque
nce (e.g., Iwata, Dorsey et al., 1994). The data points for
each condition (usually four or more) are then connected p
roducing separate data series which are examined for degree
of overlap.
An alternative version of the multielement de
sign requires that only one session be conducted per test c
ondition. In these studies, test conditions were presented
in a predetermined sequence that proceeded from easiest to
implement (requiring the least amount of adult assistance)
to most difficult to implement (requiring the most amount o
f adult assistance) (Harding, Wacker, Cooper, Millard, & Je
nsen-Kovalan, 1994; McComas et al., 1996). For example, Har
ding et al. (1994) presented test conditions in sequence un
til a significant increase in on-task behavior was observed
. Once this occurred, the condition producing the increase
was repeated with the condition just prior to it that did
not produce an increase, thereby resulting in a mini-revers
al design. By demonstrating improved performance on two occ
asions using the mini-reversal design, the researchers were
able to conclude with greater confidence that a given cond
ition would be an effective intervention in the natural set
ting. The brief multielement design has been used extensive
ly for conducting functional analyses of social behaviors i
n clinic settings in which time limitations make it difficu
lt to conduct extended analyses (Derby et al., 1992). Vollm
er, Marcus, Ringdahl, and Roane (1995) recommend its use if
differentiated response patterns can be obtained briefly b
efore trying more extended experimental analyses. By virtue
of its simplicity (relative to other kinds of evaluation d
esigns), the brief multielement design is uniquely suited t
o the kinds of time limitations under which school psycholo
gists often operate.
Because the test conditions reported
in this article target separate aspects of skill developmen
t and can be ordered according to ease of teacher implement
ation, we suggest that they be presented using a combinatio
n of strategies reported by Harding et al. (1994) and McCom
as et al. (1996). By way of example, Figure 3 presents dat
a for Jill, a sixth-grade student being instructed at a fif
th-grade reading level. At baseline, Jill correctly read 87
words per minute from the fifth-grade book of a basal rea
ding series. In this condition no special assistance was pr
ovided (i.e., she read the passage independently while the
examiner recorded her performance). This session was then f
ollowed by one session each of a series of test conditions
in sequence until her reading fluency reached a predetermi
ned criterion of 100 correctly read words per minute (Shapi
ro, 1996). Conditions were presented in the following order
: Contingent Reinforcement and Repeated Readings. In the Co
ntingent Reinforcement condition, Jill was offered incentive
s for reading at a rate of 100 correctly read words per mi
nute with three or fewer errors. Jill read 72 words correct
ly in one minute in this condition, indicating that her low
fluency rate is not a result of a performance deficit. In
the Repeated Readings condition, Jill read the passage fou
r times. After each reading she received feedback on how qu
ickly she read the passage. On the fourth reading Jill read
at a rate of 129 words correct per minute, a rate that ex
ceeds the criterion. Next, a baseline condition was repeate
d (in which Jill read 94 words correctly per minute) follow
ed by Repeated Readings (in which she read 117 words correc
t per minute) to produce a minireversal. It appears that Ji
ll benefits from increased opportunities to respond and rep
eated practice. Notice that the number of test conditions w
hich are implemented prior to the reversal will likely diff
er across children and the criterion levels of improvement
differ across grade levels.
Once the mini-reversal is comp
leted, one may conclude that an instructional program conta
ining an effective strategy in the test condition is more l
ikely to improve student performance than an ineffective st
rategy. Appendix B contains intervention protocols (includin
g protocols for oral reading and classroom assignments perf
ormance deficits) for testing hypotheses.
Outcome Measures
The objective of the test conditions is to identify the ins
tructional intervention that produces the largest outcomes i
n the most efficient manner as a basis for recommending an
intervention. At the very least, it may be possible to el
iminate interventions that are not likely to be effective b
ecause they do not even produce immediate effects. Special
considerations need to be made for the multielement design
such as that previously described. Assessment materials shou
ld be of equal difficulty level across conditions to assure
that differences in outcomes are not due to differences in
difficulty level. The assessment materials should, however,
be sufficiently different from each other so that treatmen
ts do not interfere with outcomes across different conditio
ns. Finally, it is important to maximize obtained treatment
effects by ensuring that the assessment materials have con
siderable overlap with the instructional materials for a gi
ven condition (Daly, Martens et al., 1996). In other words,
the assessment materials used for each condition should re
flect what was taught in that condition and minimize the ef
fects of what was taught in the other conditions while stil
l being of equal difficulty level.
In general, responses w
ill be of two types: oral responses or written responses. F
or academic interventions in basic skills, we recommend usi
ng curriculum-based measures of oral reading, mathematics co
mputation, written expression, and spelling. The materials c
an be designed to maximize overlap with respective test con
ditions and the measures have good technical adequacy for i
nstructional decision making (Shinn, 1989). For example, whe
n reading interventions are being investigated, outcomes can
be measured in the reading passages in which instruction w
as provided (Daly & Martens, 1994). When interventions are
being investigated to improve mathematics computational skil
ls, the examiner may assign different problem types to diff
erent conditions (e.g., 6s to one condition, 7s to another
condition, and 8s to another condition). The assessment pro
bes for each condition would contain problem types for the
associated condition.
Under some circumstances, it may be
desirable to target work completion in classroom tasks. In
this case, the examiner may choose workbook materials that
meet the requirements described above (i.e., of equal diffi
culty level but independently reflect the associated test c
ondition). Test condition results are more likely to sugges
t meaningful differences if (a) difficulty level of the mat
erials is held constant across conditions, (b) differences
between assessment materials are maximized across conditions
, (c) high overlap between instructional conditions and ass
essment conditions is sought, and (d) global but sensitive
indicators of important skills are used.
ConclusionsAfter r
eviewing more than 8,000 studies of academic performance, W
alberg (1992) concluded that two factors produced the large
st overall effects: providing formative and summative feedba
ck, and giving incentives for slow or inaccurate performanc
e. The procedures described herein, which derive directly f
rom the empirical literature on student achievement, provide
simple, efficient tests for these and other common causes
of academic problems. Each "test" represents a brief interv
ention. The notion that intervention can be a form of asses
sment might appear to be contradictory, unless an intervent
ion is defined as virtually any change in environmental con
ditions. When a positive result is obtained from a test for
which the child is provided additional practice or an ince
ntive, then we know that variable is functionally related t
o the student's academic performance. The process is differ
ent from an approach in which structural variables such as
intelligence, learning style, or handicapping condition are
inferred, presumed, or hypothesized to cause problem behavio
r based on correlational data. The interventions described
have been shown empirically to improve student academic per
formance. We have not attempted, however, to provide an exh
austive list of academic interventions (see, for example, S
hapiro & Bradley, 1995, for a discussion of cognitive-based
interventions).
The most important advantage of this func
tional assessment method is that not only has the source of
the problem been identified but the chances of choosing an
effective intervention have been increased. The assessment
process provides a test of the intervention with the child
, using the same classroom materials that would be used, in
the environment in which the child would normally perform
school work. Individuals responsible for making decisions ab
out the child are in the position of being able to say, "W
e tried this and it worked." The decision then is not what
to do but to determine what resources are needed to imple
ment the intervention in a way that is sustainable. If a c
hild requires additional practice or error correction, then
who will provide the services, when will they be provided,
and what additional resources are needed to insure impleme
ntation? The model presented, however, is not intended to r
eplace or eschew the need for formatively evaluating interv
ention outcomes (Fuchs & Fuchs, 1986); it is merely intende
d to increase the likelihood of choosing the least intrusiv
e intervention that has the greatest chances of positively
affecting student academic performance.
Although the princi
pal goal of this work was to summarize the literature relev
ant to simple methods for assessment and intervention of ac
ademic problems, a related aim was to stimulate research. R
ecent advances in the area of functional analysis have show
n promising results for adapting this methodology to classr
oom settings (Broussard & Northup, 1995; Kern et al., 1994;
Northup et al., 1995). Although all of the procedures pres
ented are individually well-supported by research, their use
in combination or as part of a deliberate problem analysis
process has not been investigated. Hence, our hope is that
this article may serve to stimulate research in the develo
pment of procedures that are adaptable to educational setti
ngs (i.e., labor and time efficient) and that can be shown
to improve student responding through improved instructiona
l decision making.
There are two limitations of the functi
onal assessment model presented. First, until more applied
research that provides functional assessment protocols is co
nducted, practitioners will need to rely upon their own ski
lls to apply the model. Given that the process is time con
suming and that little training is available, it is not lik
ely that practitioners will readily apply the model. Future
research should be directed toward validating various trea
tment protocols and describing the conditions under which t
hey are most likely to be indicated. Second, the long-term
validation of experimentally derived academic interventions
(using the brief multielement design) has not been conducte
d. Future research also should be directed toward examining
applications and limitations of the brief multielement des
ign reported. When incorporated as a part of a broader mode
l of functional analysis, the brief multielement design may
be efficient for evaluating treatments when differentiated
response patterns can be produced across conditions (Vollm
er, Marcus, Ringdahl, & Roane, 1995).
Figure 1. Reason
able hypotheses for academic deficits. REASON #1:
They do not want to do it.
The student is not motivated
to respond to the instructional demands
REASON #2: They ha
ve not spent enough time doing it.
Insufficient active stu
dent responding in curricular materials
REASON #3: They ha
ve not had enough help to do it.
Insufficient prompting an
d feedback for active responding
Student displays poor acc
uracy in target skill(s)
Student displays poor fluency in
target skill(s)
Student does not generalize use of the ski
ll to the natural setting and/or to other materials/setting
s
REASON #4: They have not had to do it that way before.
The instructional demands do not promote mastery of the cur
ricular objective
REASON #5: It is too hard.
Students ski
ll level is poorly matched to the difficulty of the instruc
tional materials
Figure 2. Academic interventions identifie
d by the presumed function of the behavior.
Legend for Ch
art:
A - Reasonable HypothesesB - Possible Interventions
A B
The student is not motivated to respond to the instru
ctional demands
Increase interest in curricular activiti
es: 1. Provide incentives for using the skill 2. Teach th
e skill in the context of using the skill 3. Provide choi
ces of activities
Insufficient active student responding
in curricular materials
Increase active student respond
ing: 1. Estimate current rate of active responding & incr
ease rate during allocated time
Insufficient prompting an
d feedback for active responding
Increase rate of comple
te learning trials: 1. Response cards 2. Choral respondi
ng 3. Flash card intervention with praise/error correcti
on 4. Peer tutoring
Student displays poor accuracy in tar
get skill(s)
Increase modeling & error correction: 1. Re
ading passages to student 2. Use cover-copy-compare 3. Ha
ve student repeatedly practice correct response in contex
t for errors
Student displays poor fluency in target skil
l(s)
Increase practice/drill &/or incentives: 1. Have th
e student repeatedly read passages 2. Offer incentives fo
r beating the last score
Student does not generalize use
of the skill to the natural setting and/or to other mater
ials/ settings
Instruct the student to generalize use of
the skill: 1. Teach multiple examples of use of the ski
ll 2. Teach use of the skill in the natural setting 3. "C
apture" natural incentives 4. Teach self-monitoring
The i
nstructional demands do not promote mastery of the curric
ular objective
Change instructional materials to match t
he curricular objective: 1. Specify the curricular object
ive and identify activities that promote use of the skill
in the context in which it is generally used
Student's s
kill level is poorly matched to the difficulty of the ins
tructional materials
Increase student responding using b
etter matched instructional levels: 1. Identify student's
accuracy & fluency across instructional materials and us
e instructionalmaterials that promote a high rate of resp
ondingGRAPH: Figure 3. Number of correctly read words per m
inute in test conditions for Jill.
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Appen
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Appendix B Test Condition Protoc
olsREASONABLE HYPOTHESIS:
The student is not motivated to
respond to the instructional demands.
Performance Defi
cit Protocol for Classroom Assignments 1. Obtain
three previously completed assignments from the teacher. E
ach assignment should be one which the child has failed or
has performed markedly below expectations.
2. Present the
first assignment (with previous answers removed) to the ch
ild and say, "If you are able to get X number of items (80
%) correct, then we can (get a coke, play a game, go outsi
de for 10 minutes, etc.). If the child increases his or he
r score by 25% or obtains a score of 80% or above, then mo
ve to the next step.
3. Present the child with a list of t
eacher approved reinforcers and have the child indicate whi
ch one he or she would like to work for in the future.
4.
Test reinforcer efficacy. Present the previously failed as
signment to the child and say, "If you are able to get X n
umber (80%) correct, then you can select one of these (show
list of rewards). If you get X or fewer correct, then you
will not get to select anything."
5. Evaluate outcomes. If
the child markedly increases performance when offered ince
ntives, suspect a performance deficit. If performance remain
s constant under reinforced and non-reinforced trials, then
suspect a skill deficit.
Performance Deficit Protoc
ol for Oral Reading Fluency 1. Obtain those reinforc
ers that the student identifies as being his or her first,
second, and third choice of things to work for.
2. Ident
ify the reading passage to be instructed for the day.
3. C
ompute the passage's criterion rate [i.e., the rate, in sec
onds, at which the student must read the passage in order
to receive his or her top choice of reinforcers using instr
uctional placement guidelines for mastery from Shapiro (1996
). The rate is simply the number of words in the passage (
which is equivalent to a rate of 60 correctly read words p
er minute) for first and second grade readers and the numbe
r of words in the passage divided by 1.67 (which is equiva
lent to a rate of 100 correctly read words per minute) for
third through sixth grade readers].
4. Explain to the stud
ent that he or she will (a) receive his or her first choic
e if he or she reads faster than the criterion and misses
less than 3 words, (b) receive his or her second choice if
he or she reads at the criterion and misses less than 3 w
ords, and (c) receive his or her third choice if he or she
reads almost at the criterion and misses less than 3 word
s.
5. Have the student read the passage aloud, timing how
long it takes him or her to read the passage and marking e
rrors on your copy.
6. Offer the student that reinforcer co
rresponding to the conditions described in step 4.
REASONAB
LE HYPOTHESIS:
The student displays poor fluency in target
skill(s).
Repeated Readings 1. Identify the reading
passage to be instructed for the day.
2. Tell the student
that you will time him or her while reading.
3. Have the
student begin reading the passage and start the stopwatch.
4. If the student misses a word or hesitates for 3 seconds
, say the word and have the student repeat the word.
5. At
the end of the passage tell the student the time that it
took to read the passage.
6. Repeat steps 3 through 5 thre
e more times.
REASONABLE HYPOTHESIS:
The student displays
poor accuracy in target skills.
Listening Passage Pr
eview and Phrase Drill Error Correction 1. Ident
ify the reading passage to be instructed for the day.
2.
Read the passage aloud while having the student follow alon
g on his or her copy.
3. Have the student read the passage
aloud while underlining errors on the examiner copy and su
pplying the words for mispronunciations, omissions, and hesi
tations of more than 3 s.
4. Show the student each underli
ned error word in the passage.
5. Say the word and have th
e student read each phrase containing the error word three
times.
REASONABLE HYPOTHESIS:
The student does not general
ize use of the skill to the natural setting and/or to othe
r materials/settings.
Generalization Training for P
honics Objectives 1. Obtain passages containing high p
ercentages of target phonics skills.
2. Create word lists
using the phonetically regular words.
3. Explain the phonic
rule to the student and read the words on the word list t
o the student.
4. Have the student read the word list and
correct errors.
5. Read the passage to the student.
6. Have
the student read the passage and correct errors.
REASONABL
E HYPOTHESIS:
The student's skill level is poorly matched
to the difficulty of the instructional materials.
Instru
ctional Match 1. Identify the curriculum for reading in
struction.
2. Obtain passages from books below, at, and ab
ove the student's current reading level based on teacher es
timates.
3. Obtain local norms (e.g., building level CBM no
rms) (Shinn, 1989) or refer to conventional placement stand
ards (e.g., Shapiro, 1996).
4. Have the student read each p
assage for 1 minute while recording the number of correctly
read words per minute and errors.
5. Determine the student
's instructional level by either comparing to average fluen
cy rates for students at different grade levels when using
local norms or by comparing to instructional placement stan
dards.
~~~~~~~~By Edward J. Daly III, University of Cincinn
ati; Joseph C. Witt, Louisiana State University; Brian K. M
artens, Syracuse University and Eric J. Dool, University of
Cincinnati
Address all correspondence concerning this arti
cle to Edward J. Daly III, School Psychology Program, Colle
ge of Education, P.O. Box 210002, University of Cincinnati,
Cincinnati, OH 45221-0002
Edward J. Daly III, PhD, is Ass
istant Professor of School Psychology at the University of
Cincinnati, Cincinnati, OH. His research interests include i
ntervention design for academic performance problems
Joseph
C. Witt, PhD, has research interests in the area of helpi
ng teachers and other professionals to be more effective in
assessing and intervening with learning and behavior probl
ems. He is currently editor of School Psychology Quarterly
Brian K. Martens, PhD, is an Associate Professor of Psychol
ogy at Syracuse University. His research interests include
applied behavior analysis, instructional intervention, and s
chool consultation
Eric J. Dool, MEd, is a doctoral studen
t in the School Psychology program at the University of Cin
cinnati, Cincinnati, OH. He is currently completing his int
ernship in the Cincinnati Public Schools.
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