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Effective Intervention 2

Effective Intervention for Students with Specific Learning Disability: The Nature of Special Education

Kenneth A. Kavale Regent University


Abstract

The nature of effective instruction for students with specific learning disability is explored.

Process training has long been a prominent intervention but is shown to possess limited efficacy.

Better outcomes are attained when effective general education instructional techniques are

adapted for the purposes of special education. Related services are also shown to be useful

adjuncts to the instructional program. Special education for students with specific learning

disability appears to be more efficacious when “education” is emphasized over “special”

interventions not routinely found in general education.


Introduction

Because of the failure to profit from general education students with specific learning

disability (SLD) present significant challenges for special education. What are the best means for

enhancing academic performance? Answers have been difficult because, according to Sarason

and Doris (1979), special education often fails to learn from its past meaning that there is change

but not necessarily progress. Consequently, special education has demonstrated a cyclical nature

that swings between optimism and pessimism in about ten-year periods (Zigler and Hodapp,

1986). Under such circumstances, it has been difficult to provide an unequivocal answer to the

question: Is special education for student with SLD special?

The Nature of Special Education

The definition of special education as “specifically designed instruction…to meet the

unique needs of a child with a disability (U.S. Department of Education, 1999, p. 12425)

emphasizes individualized instruction but does not stipulate the nature of the instruction to be

provided. For students with SLD who “failed” in general education, special education has

historically opted for developing unique and exclusive methods that would differentiate it from

general education. Such “special” methods provided a distinct identity for special education but

also a separateness from general education that produced a skepticism about their benefits on the

part of general education. Because of its increased costs, special education became increasingly

accountable. Could special education for students with SLD substantiate its benefits?

As part of its distinct identity, special education viewed its primary goal to be one of

correcting or reversing the altered learning functions of students with SLD. Beginning with the

word of Jean-Marc-Gaspard Itard with Victor, the “wild boy of Areyron” (Itard, 1806/1962),

special education has focused in enhancing cognitive processes. Thus, process training has a long


history as a primary form of special education (see Mann, 1979). For students with SLD, process

training seeks to improve information-processing abilities so they may then be able to acquire

and assimilate information in the same manner as students in general education. Although

intuitively appealing, there has always been questions about whether or not training processes

can improve learning ability. Such questions date back to Itard whose innovative education

program produced limited improvement in Victor’s performance, and the enduring perception

that Itard had “failed” (e.g., Kirk & Johnson, 1951). In reality, the modest gains were substantial

and became more meaningful with a shift in emphasis from results to methods: “Few current

education interests fail to be illuminated by [Itard’s] single slender volume (Gaynor, 1973, p.

445).

Process training continued to be a prominent form of special education and reached its

greatest prominence with the emergence of SLD as a category of special education.

Nevertheless, questions about the efficacy of process training also continued to be raised. These

questions were typically answered by a rendering of “what the research says”. The vagaries of

interpreting research findings are illustrated in the case of psycholinguistic training a prominent

form of process training during the 1960s and 1970s. Psycholinguistic training was developed by

Samuel A. Kirk and embodied the Illinois Test of Psycholinguistic Abilities (ITPA). The model

was based on the assumption that psycholinguistic ability is comprised of discrete components

and that these components can be improved with training. By the mid 1970s research summaries

were available but they revealed very different interpretations.

A review of 39 studies offered by Hammill and Larsen (1974) concluded that, “the idea

that psycholinguistic constructs, as measured by the ITPA, can be trained by existing techniques

remains nonvalidated” (p. 11). In response, Minskoff (1975) offered a more positive evaluation


and concluded that psycholinguistic deficits can be remediated. The Minskoff review was

immediately challenged by Newcomer, Larsen, and Hammill (1975) who again concluded that,

“the reported literature raises doubts regarding the efficacy of presently available Kirk-Osgood

psycholinguistic training programs” (p. 147). The divergent interpretations along with

increasingly harsh rhetoric made it increasingly difficult to determine what the research says

about the efficacy of psycholinguistic training.

Several years later, Lund, Foster, and McCall-Perez (1978) re-evaluated the original 39

studies, and concluded that, “It is, therefore, not logical to conclude either that all studies in

psycholinguistic training are effective or that all studies in psycholinguistic training are not

effective” (p. 319). Hammill and Larsen (1978) contested the Lund et al. analysis and concluded

that, “the cumulative results…failed to demonstrate that psycholinguistic training has value” (p.

413). Although polemics abounded, a primary question remained unanswered: What is really

known about the efficacy of psycholinguistic training?

META-ANALYSIS AND THE EFFICACY OF SPECIAL EDUCATION

Quantitative Research Synthesis

The case of psycholinguistic training demonstrates the difficulties in providing

unequivocal answers to questions about efficacy. The research investigating psycholinguistic

training typically used the “scientific method” to provide empirical evidence about efficacy

(Kauffman, 1987). As the case of psycholinguistic training demonstrates, difficulties arise when

individual study findings do not agree. Since none of the individual empirical investigations are

“perfect” (i.e. provides unequivocal evidence), individual studies need to be combined to

produce “usable knowledge” (Lindlom & Cohen, 1979) that provides the basis for decisions

about efficacy.


The method used to combine findings about the efficacy of psycholinguistic training

encountered difficulties because they failed to accumulate knowledge in an objective and

systematic manner. To eliminate the subjectivity associated with traditional review methods (see

Cooper & Rosenthal, 1980), quantitative methods, usually termed “meta-analysis” (Glass, 1976),

have become a preferred means of synthesizing empirical findings. The “effect size” (ES) metric

used in meta-analysis imparts a clarity and explicitness to empirical evidence that makes

combined findings more objective and verifiable (Glass, McGaw, & Smith, 1981). Cohen

(1988), based on notions of statistical power, provided “rules of thumb” where ES may be

interpreted as small (.20), medium (.50), or large (.80).

Psycholinguistic Training

In an effort to bring closure to the psycholinguistic training debate, Kavale (1981)

conducted a meta-analysis on 34 studies that yielded an average ES of .39. In a statistical sense,

an ES shows outcomes in standard deviation (SD) units that can be interpreted in terms of

overlapping distributions (treatment vs control). The ES of .29 indicates that the average subject

receiving psycholinguistic training wold gain 15-percentile ranks on the ITPA, and would be

better off than 65% of comparison (no treatment) subjects. At a “medium” level, an ES of.39

does not represent an unequivocal endorsement of psycholinguistic training.

To gain additional insight, ES data were aggregated by ITPA subtest and 5 of 9 ITPA

subtests revealed “small,” albeit positive, effects. Such a modest level of response suggests that

training is not warranted in these 5 cases. For 4 subtests (Auditory and Visual Association,

Verbal and Manual Expression), training improves performance from 15 to 24 percentile ranks

and makes the average trained subject better off than approximately 63% to 74% of entrained

subjects.


The findings regarding the Associative and Expressive constructs appear to belie the

conclusion of Hammill and Larsen (1974) that, “neither the ITPA subtests nor their theoretical

constructs are particularly ameliorative” (p. 12). Although encouraging, the meta-analytic

findings should not be interpreted as an unequivocal endorsement of psycholinguistic training.

For example, in the case of Auditory Association, there are difficulties in defining the skill: What

is Auditory Association? Additionally, it is important to determine whether improvement in

Auditory Association provides enhanced functioning in other than that discrete ability. In

contrast, the case for Expressive constructs, particularly Verbal Expression, presents a different

scenario, however, because it represents the tangible process of productive language behavior

whose improvement is critical for school success. In fact, the Verbal Expression ES (p. 63)

exceeds what would be expected from 6 months of classroom language instruction (ES=.50).

Thus, the Kavale (1981) meta-analysis showed where psycholinguistic training might be

effective and might be initiated when deemed an appropriate part of an intervention program.

Process Training

Mann (1979) suggested that, “process training is, in fact, one of the oldest forms of

education and that, despite periodic discontinuities in its practice, it has continued unabated into

our own day” (p. 537). Table 1 shows that all forms of process training reveal limited efficacy

(see Appendix A for sources). For example, perceptual-motor training, the embodiment of 1960s

special education had practically no effect on improving education performance; famous

programs such as those developed by Kephart (ES=.06) and Frostig (ES=.10) revealed modest

effectiveness.

Although attacks on process training have been vigorous (e.g., Mann, 1971), its

historical, clinical, and philosophical foundation provides a resistance to accept negative


evidence (e.g., Hallahan & Cruickskank, 1973) because, “the tension between belief and reality

provides a continuing sense of justification for process training” (Kavale & Forness, 1999, p.

35). The failure to alter beliefs with evidence was found for modality-matched instruction

(ES=.14) which received a number of negative evaluations (e.g., Arter & Jenkins, 1979;

Larrivee, 1981; Tarver & Dawson, 1978) but also finds teachers maintaining a belief that

students learn best when instruction is modified to match individual modality patterns (Kavale &

Reese, 1991). The empirical evidence demonstrating that interventions developed to define the

uniqueness of special education are not effective needs to be accepted by teachers because,

“schools must view the time, money, and other resources devoted to [process training] as

wasteful [and] as an obstruction to provision of appropriate services (Council for Learning

Disabilities, 1986, p. 247).

Creating Effective Special Education

The long dominant tradition of process training reflected a pathology model of special

education; academic problems were regarded as a disease and interventions are aimed at

“curing” the disease (i.e., reviewing the pathology). By about 1975, the realization that process

training was not producing desired outcomes shifted attention to an “instructional imbalance”

model where school failure was viewed as the result of a mismatch between instructional

methods and student developmental level. The “effective schools” research (see Bickel & Bickel,

1986) was a major influence that stressed, for example, the importance of teachers believing that

all students can achieve, that basic skill instruction should be emphasized, and that clear

instructional objectives should be used to monitor student performance.

At the same time, a “learning process” model emerged that viewed teaching within a

“process-product” paradigm where variables that depict what occurs during teaching are


correlated with products (i.e., student outcomes). Research revealed the importance of a number

of principles like, for example, encouraging student’s active engagement in learning, exploring

innovative approaches to grouping and organizing classroom instruction, and making learning

meaningful by keeping it enjoyable, interesting, student-centered, and goal-oriented (see Brophy

& Good, 1986). These principles became “best practice” and were interpreted for special

education (e.g., Christenson, Ysseldyke, & Thurlow, 1989; Reith & Evertson, 1988; Reynolds,

Wang, & Walberg, 1992).

Effective Special Educational Practice

Research investigating the teaching-learning process has identified a number of effective

instructional practices which have been summarized in a number of quantitative research

syntheses (see Appendix A). Table 2 shows a sample of effective instructional practices and

reveals that substantial positive influence on learning are possible by modifying the way

instruction is delivered. To illustrate the power of the methods listed in Table 2, two additional

ES interpretations are provided. The “common language effect size” (CLES) (McGraw & Wong,

1992) converts ES into a probability that a score sampled from one distribution will be greater

than a score sampled from another. For example, in a sample of studies investigating the use of

self-monitoring (CLES=.83), 83 out of 100 would show that a subject using self-monitoring

would improve when compared to subjects in the control condition. The “binomial effect size

display” (BESD) (Rosenthal & Rubin, 1982) addresses the question: What is the percentage

increase in the number of successful responses to the use of a new instructional practice? Based

on converting ES to r, the BESD for the use of self-questioning shows an increase in success rate

from 25% to 75%. The 50-percentage-point spread between treatment (75%) and control (25%)


success rate shows that the use of self-questioning possesses, not only statistical significance, but

also practical significance.

The use of effective instructional practices moves special education toward the general

education teaching-learning model and away from “special” interventions. For example,

mnemonic instruction (MI) is a strategy that transforms difficult-to-remember facts into a more

memorable form through recoding, relating, and retrieving information (Mastropieri & Scruggs,

1991). A student with SLD receiving MI would be better off than 95% of students not receiving

MI and show a 45 percentile rank gain on an outcome measure. In a sample of studies

investigating MI, 87 out of 100 would show that students with SLD receiving MI would

demonstrate improvement when compared to students in the control condition (CLES=.87). The

BESD shows a 64% increase in success rate which indicates substantial practical significance.

Compare the success rate of MI to, for example, perceptual-motor training (ES=.08) where the

success rate increase is 4% indicating a “small” statistical effect and no practical significance.

Effective Special Education Instruction

The ultimate purpose of implementing effective instruction is to enhance academic

performance. Achievement outcomes are shown in Table 3 and indicate the potential for

substantial gains across subject areas. All achievement domains show “large” ES with gains

ranging from 29 to 41 percentile ranks on achievement measures. On average, almost 8 out of 10

students with SLD will show improvement (CLES=.77). The success rate increases from 27% to

73% indicating an average 46% improvement in the number of students showing a positive

response to instruction.

The example of reading comprehension demonstrates how meta-analysis can be useful

for judging the magnitude of “real” effects. Two quantitative research syntheses contributed


almost all ES measurements and produced ESs of 1.13 and .98 with a modest 3 percentile rank

difference in outcomes (87 vs. 84). When specific methods for improving reading

comprehension are compared, the two meta-analyses revealed similar patterns. The largest

effects (ESs=.60 and 1.33) were found for metacognitive techniques (e.g., self-questioning, self-

monitoring). Text enhancement procedures (e.g., advanced organizers, mnemonics) produced ES

of 1.09 and .92. The least effective (but nevertheless effective) techniques involved skill training

procedures (e.g., vocabulary, repeated reading) with ESs of .79 and .62). The consistency of

findings across these two meta-analyses provides confidence in concluding that it is possible to

enhance reading comprehension.

The meta-analytic evidence suggests that, on average, the real effect of reading

comprehension instruction is 1.04, a level comparable to one year’s worth of reading

comprehension instruction in general education (ES=1.00). Thus, methods adapted for the

purposes of special education produced the same effect as 1 year of general education instruction

but did so in approximately 20 hours. Clearly, students with SLD can significantly improve their

ability to better understand what they read.

Effective Special Education Related Services

A hallmark of special education is the provision for related services to be provided when

deemed appropriate to augment the instruction program. Talbe 4 shows a sample of possible

activites and most demonstrate, at least, “medium” ES. On average (ES=.65), related services

produce a 24 percentile rank gain on an outcome assessment. In 68 out of 100 cases a positive

response to the related service is achieved (CLES=.68). Thus, related services appear to be useful

adjuncts to the instructional program.


Placement has often been viewed as having a positive influence on student performance

(see Kavale & Forness, 2000). The ES magnitude (.12) negates such a view and indicates that the

success rate associated with placement increases only 6% from 47% to 53% (BESD). The

“small” ES suggests that “what” (i.e., nature of the instruction) is far more important influence

on student outcomes than “where” (i.e., placement). In contrast, prereferral reveals significant

positive effects. The CLES (.78) indicates that in 78 of 100 cases prereferral activities will

produce positive outcomes. Prereferral “works” because it is predicated in modification of

instructional activities and its 48% success rate means that almost half of students with SLD

given preferential activities do not enter special education.

Evaluating Special Education

Special education has demonstrated increasing efficacy that may be attributable to a

change in instructional emphasis. Until about 25 years ago, emphasized its special nature

(SPECIAL education) by developing singular and different methods not found in general

education. The goal was to enhance unobservable constructs (“processes”) that were presumable

the cause of learning deficits. Basic skill instruction was a secondary consideration until

processes were remediated and learning became more efficient. Evaluation of SPECIAL

education (see Table I) reveals limited efficacy leading to the conclusion that process deficits are

difficult to “cure.”

In an effort to elevate academic outcomes, emphasis shifted to education (special

EDUCATION) with the goal of improving achievement. Instructional techniques originating in

general education were adapted to assist students with SLD in acquiring and assimilating new

knowledge. The efforts demonstrated significant success (see Table 2) and better academic

outcomes (see Table 3).


The difference between the two forms of special education are seen in the mega ES

(mean of means) for special (.15) versus education (.89) techniques. The comparison reveals

special EDUCATION to be 6 times more affective than SPECIAL education which produces

academic outcomes (mega ES=1.04) that exceed 1 years worth of general education instruction

(ES=1.00). On average, SPECIAL education offers about a 6% advantage meaning that the upper

50% of the group receiving special interventions exceeds only about 56% of the group not

receiving such interventions; this modest level of improvement is only slightly above chance

(50%). Additionally, across meta-analysis investigating SPECIAL education, about 25% of the

calculated ES were negative indicating that in one out of four cases student’s not receiving the

special intervention performed better. Clearly, there is little reason to include SPECIAL

education in intervention programs.

The methods associated with special EDUCATION, in contrast, should be the basis for

designing an intervention program. The use of education practices is likely to move the average

student with SLD from the 50th to the 81st percentile. The 31-percentile-rank gain is better than 5

times the gain found for special interventions and indicates students are better off than 81% of

those not receiving special EDUCATION. For example, Direct Instruction (DI), a behaviorally

oriented teaching procedure based on an explicit step-by-step strategy (ES=.93) is 6 ½ times

more effective than the intuitively appealing modality-matched instruction (ES=.14) that

attempts to capitalize on learning style differences. Students with SLD who are instructed with

DI would be better off than 87% of students not receiving DI and would gain over 11 months

credit on an achievement measure compared to about 1 month for modality-matched instruction.

When grounded in effective instructional methodology, special EDUCATION can sometimes be

up to 20 times more effective than SPECIAL education.


Effective Special Education

The meta-analyses summarized provides insight into the indications and

contraindications of special education interventions (Lipsey & Wilson, 2001). The interventions

associated with special EDUCATION may be considered a form of “evidenced-based practice”

(EBP) (Odom, Brantlinger, Gersten, Horner, Thompson, & Harris, 2005) where interventions are

grounded in empirical findings demonstrating that the actions produce efficacious and beneficial

outcomes. The use of EBP promotes instructional validity where changes can be attributed to the

specific activities and can be used to produce similar results with other students (generalization).

The implementation of EBP may be limited by extraneous factors. For example, tradition

(“We have always used it”) and history (“It has worked before”) are powerful barriers.

Additionally, the bandwagon effect, where an intervention suddenly becomes popular and gains

momentum rapidly, may have a significant influence. As pointed out by Mostert (1999-2000),

“Bandwagons are used to champion a cause, engage in sweeping yet attractive rhetoric, and

generally to promise far more than they ever have hope of delivering” (p. 124). Finally, belief, a

strong connection about the truth, although a legitimate consideration in intervention of

decisions, is only appropriate when the beliefs are grounded in empirical evidence.

The negative influence of these extraneous factors is the reasons why research findings in

special education “are embraced by some, ignored by others, and modified to suit the routines

and preferences of still others” (Gersten, Vaughn, Deshler, & Schiller, 1997, p. 466). Regardless

of how exciting teachers may find new EBP, they tend to resist implementing EBP in favor of

more comfortable existing practices (Swanson, 1984). Heward (2003) identified 10 faulty

notions that hinder the effective delivery of special education. The many obstacles in making

instructional decisions primarily in evidence is the reason for the continuing research to practice


gap (Greenwood & Abbott, 2001) and the resulting problem of sustainability, the maintained use

of an instructional practice supported by evidence of improved outcomes for students with SLD

(Gersten, Vaughn, & Kim, 2004).

Because students with SLD, by definition, possess learning needs, instructional decisions

are critical in the design of individualized programs. The complexities surrounding the

instructional decision process introduces a degree of “uncertainty” (i.e., the program may not

work) (Glass, 1979). Besides uncertainty, there is also the possibility of “risk” (i.e., negative

outcomes) that can be described by the SD, a measure of dispersion around the mean ES that

represents an index of variability. Taken together, the two statistics provide a theoretical

expectation about intervention efficacy (ES ± SD). For example, psycholinguistic training (.39 ±

.54) spans a wide range (-.15 to .93) from negative ES to “large” ES; the difficulty is found in the

inability to predict the outcome for a particular student. The mega mean ES for SPECIAL

education (.15) is associated with a larger mega mean SD (.48) making “special” interventions

more variable than effective (.15 ± .48). In contrast, special EDUCATION (.89 ± .87) is more

effective than variable and, although it may not “work” (ES = .02), there is also the theoretical

possibility of being almost twice as effective (ES = 1.76).

Although the use of special EDUCATION reduces risk (i.e., no negative ES), the special

education teaching-learning process remains variable, unpredictable, and indeterminate. To

minimize the influence of these confounding factors, special education instructional decisions

cannot be prescriptive (i.e., do A in circumstance X or Y, and do B in circumstance Z) but rather

must blend elements of science (theoretical and empirical knowledge) and art (interpretation

necessary to initiate action) to create “best” practice (see Gage, 1978). The actions of


“special education practitioners” will need to go beyond the scientific basis of their work

… [that] must be mediated through the teacher’s own creative rendering of best practice

… because quality education for special education students will always be biased on the

artful application of science” (Kavale & Forness, 1999, p. 93).

Kenneth A. Kavale, PhD. Is Professor of Special Education at Regent University,

Virginia Beach, VA 23454.


Table 1

Effectiveness of Process Training

Method Mean Effect Size Percentile Rank Equivalent Power Rating

Irlen Lenses -.02 49 Negative Perceptual-Motor Training .08 53 Negligible Diet Modification (Feingold) .12 55 Small Modality-Matched Instruction .14 56 Small Social Skills Training .21 58 Small Psycholinguistic Training .39 65 Small-Medium


Table 2

Effective Instructional Practices

Practice Mean Effect Size

Common Language Effect Size

Binomial Effect Size Display

Success Rate Increase From % To % Mnemonic Instruction 1.62 .87 18 82 Self-Monitoring 1.36 .83 22 78 Reinforcement 1.17 .80 25 75 Self-Questioning 1.16 .79 25 75 Drill & Practice .99 .76 28 72 Strategy Instruction .98 .75 28 72 Feedback .97 .75 28 72 Direct Instruction .93 .75 29 71 Applied Behavior Analysis .93 .75 29 71 Visual Displays .90 .74 29 71 Computer-Assisted Instruction .87 .73 30 70 Repeated Reading .76 .71 32 68 Error Correction .72 .70 33 67 Formative Evaluation .70 .69 33 67 Peer Mediation .64 .67 35 65 Diagnostic-Prescriptive Teaching .64 .67 35 65 Peer Tutoring .62 .67 35 65 Positive Class Morale .60 .66 36 64 Grouping .43 .62 40 60 Increased Time .38 .61 41 59


Table 3

Effective Special Education Instruction

Subject Area Mean Effect Size

Percentile Rank

Equivalent

Common Language Effect Size

Binomial Effect Size Display Success Rate

Increase From % To % Handwriting 1.32 91 .82 22 78 Oral Reading 1.31 90 .82 22 78 Language 1.27 90 .82 23 77 Reading Comprehension 1.04 85 .77 27 73 Word Recognition .98 84 .75 28 72 Narrative Writing .97 83 .75 28 72 Math .96 83 .75 28 72 Spelling .87 81 .73 30 70 Vocabulary .85 80 .73 30 70 Problem-Solving .82 79 .72 31 69


Table 4

Effective Special Education Related Services and Activities

Service Mean Effect Size Common Language Effect Size

Binomial Effect Size Display

Success Rate Increase From % To % Memory Training 1.12 .79 25 75 Prereferral 1.10 .78 26 74 Cognitive Behavior Modification .74 .70 32 68

Psychotherapy .71 .69 33 67 Stimulant Medication .62 .67 35 65 Counseling .60 .66 35 65 Consultation .55 .65 36 64 Rational-Emotive Therapy .50 .64 38 62 Attribution Training .43 .62 39 61 Placement .12 .53 47 53


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