Professional Practice

Did Endrew F. Change the “A” in FAPE? Questions and Implications for School Psychologists

By Shawn K. O'Brien

pp. 1, 31

Volume 46 Issue 5

By Shawn K. O'Brien

This past March, the Supreme Court issued a decision, Endrew F. v. Douglas County School District RE-1, that has the potential to change the definition of “appropriate” in what constitutes a free and appropriate education (FAPE), at least within some U.S. jurisdictions. This article briefly summarizes the ruling, with a focus on specific issues and questions that are relevant to the practice of school psychology.

Endrew was diagnosed with autism at age 2 and attended his public school from preschool through fourth grade, where he received special education. Endrew's behavior, which included screaming, climbing over furniture and other students, and running away from school, interfered with his ability to learn. He also demonstrated intense fears of common items in his environment. By fourth grade, his parents became dissatisfied with his progress, which they believed had stalled (because the same basic goals and objectives remained on his IEP from one year to the next). His parents then placed him in a private school that specialized in educating students with autism. The new school implemented a behavioral intervention plan, and added new academic goals. Within months, Endrew's behavior improved significantly, and he made more academic progress than he had been making in his public school (Endrew F., 2017, pp. 6-7). The court decision is available online (, as is an excellent discussion of the ruling by Perry Zirkel (2017;

The Substantive Standard for FAPE Revisited

Prior to Endrew F., the Supreme Court had only addressed FAPE standards one time, in Board of Education of Hendrick Hudson Central School District v. Rowley, in 1982. In that split decision, the Supreme Court determined that an IEP was adequate if it was “reasonably calculated to enable the child to receive educational benefits” (Rowley, 1982, p. 207). Subsequent lower court decisions generally fell into one of two categories in further refining the definition of “educational benefit.” Some jurisdictions defined it as “meaningful” benefit, while others required only “some” benefit, referred to as merely more than “de minimis” (Zirkel, 2017). The latter, lower standard was applied by the U.S. Court of Appeals for the Tenth Circuit in the case of Endrew F. before the plaintiffs appealed to the U.S. Supreme Court. Endrew's parents argued for an even higher standard than that found in jurisdictions requiring “meaningful benefit,” as they argued for one that would provide a disabled child with “… opportunities to achieve academic success … substantially equal to the opportunities afforded to children without disabilities.” (Endrew F., 2017). In a unanimous decision, the Court rejected both the lower “some benefit” standard argued for by the school district, and also the higher “equal” standard argued for by the parents. They instead settled on a seemingly intermediate standard, declaring that the IEP must be “reasonably calculated to enable a child to make progress appropriate in light of the child's circumstances” (Endrew F., 2017, pp. 999 & 1002). The Court remanded the case to the lower court to apply this refined standard to Endrew's IEP. A final outcome has not been determined as of this writing.

Many advocates and parents of students with disabilities hailed the Court ruling as a victory that will meaningfully improve special education, while at least some representatives of school districts predicted that the decision will have little to no impact (McKenna, 2017). One might speculate that the ruling will have a greater impact on LEAs that have been using the lower “some benefit” standard than on those that have been using the higher “meaningful benefit” standard. However, even the latter may be affected, since the ruling not only addressed the standard, but it also provided guidance, albeit ambiguous, as to how this standard for the “appropriateness” of an IEP will be evaluated in legal disputes going forward. It is now up to the lower courts to evolve the nature of the substantive standard of FAPE by further refinement of the broad language in Endrew F. I would argue that, at the very least, school psychologists should be aware of the central issues raised by Endrew F. Ideally, we should also be involved in advising courts and hearing officers on those issues in our areas of expertise, as well as conducting further research into some of the questions raised by the ruling on which there is not yet a consensus in our profession.

Focus on Student Progress

A noteworthy feature found in the Court's accompanying dicta was that “… the new predicate starts with ‘progress’ rather than merely benefit …” (Zirkel, 2017, p. 7). IEP teams may now want to pay closer attention to student progress. A few years ago, I did an internship at a state agency that oversaw special education disputes, to learn more about the process. My impression was that the most common source of conflict was the student's failure to progress as the parent expected, even if that was not explicitly stated as the complaint. Given the Court's ruling that Endrew's progress was inadequate based on his IEP goals remaining essentially the same from one year to the next, it would seem that IEPs in which the goals do not significantly change from the previous year do not meet the Court's standard for FAPE, unless there are exceptional circumstances (e.g., missing significant amounts of instruction due to serious illness). But even meeting IEP goals may not be sufficient if the goals are not well written, not individually constructed based on a comprehensive evaluation, not meaningful, or not sufficiently ambitious (Yell, Katsiyannis, Ennis, Losinski, & Christle, 2016).

Beyond goal attainment, there is minimal legal guidance on how progress should be measured (e.g., normative or curriculum based measures), and with what frequency. IDEA merely requires the IEP to describe how progress will be measured and when periodic reports will be provided to parents. Although both Rowley and Endrew F. indicated that passing grades and grade promotion may be one relevant measure of progress for students who are fully integrated into general education, they also warned that such measures do not automatically imply that a child is receiving FAPE.

It is even less clear what measures of progress are acceptable for students who are not fully integrated into general education, or who need a modified curriculum. Yell et al. (2016) reported that some court decisions and state agencies have warned against using measures such as grades and vague references to “teacher observation,” and they recommend that IEP progress monitoring be implemented systematically and frequently. They provide a useful checklist to ensure such a process. Given the possibility of greater scrutiny on student progress as a yardstick for educational benefit, combined with a lack of specific guidance, IEP teams may want to implement the same progress monitoring system they have in place for their students in RTI, if they are not already doing so. The National Center on Intensive Intervention, which is part of the Technical Assistance and Dissemination Network of the U.S. Department of Education's Office of Special Education (OSEP), advocates using frequent progress monitoring data to inform instruction for students with disabilities who are consistently not making adequate progress in meeting their IEP goals (National Center on Intensive Intervention, 2013). They offer numerous tools and resources for doing so. Although still not a procedural requirement of IDEA, the Court ruling also appears to support the argument that when student progress is stalled due to impeding behavior, a behavioral intervention plan based on a comprehensive functional behavioral analysis should be implemented.


While the ruling seems to place greater emphasis on student progress, the Court chose not to elaborate on what constitutes appropriate progress, noting that it would vary from child to child. The Court held that “the degree of progress contemplated by the IEP must be appropriate in light of the child's circumstances” (Endrew F., 2017, pp. 999 & 1002). They went on to explain that “A child's IEP need not aim for grade-level advancement if that is not a reasonable prospect. But that child's educational program must be appropriately ambitious in light of his circumstances….” (p. 3). Presumably, there are numerous “circumstances” that could impact the degree of progress that is appropriate for a given child, but the Court mentioned only a few such factors for IEP teams to consider: “An IEP … is constructed only after careful consideration of the child's present levels of achievement, disability, and potential for growth” (p. 12), and this reiteration from Rowley: “the benefits obtainable by children at one end of the spectrum will differ dramatically from those obtainable at the other end, with infinite variations in between” (Endrew F., 2017, p. 12).

Since IEP teams routinely consider present levels of performance as the initial referent when setting goal levels, and the disability when selecting goal content, these do not seem to warrant further discussion. The other two factors mentioned are “potential for growth,” and variations based on where the child falls along the disability continuum, which implies level of severity. These are both assessment issues and as such, it would seem to be our profession's responsibility to evaluate them and render our professional opinion. I would argue that we must first operationally define them, and then determine whether our measurements of these constructs demonstrate sufficient predictive validity, since that is their purpose in this context of evaluating “potential” and estimating a reasonable range of future performance.


One way to view “learning potential” that has been suggested by numerous court decisions is “window of opportunity” (Crane, 2017), particularly in students with autism. Based on the testimony of expert witnesses, courts have come to recognize a critical developmental window for autistic children. Failure to provide sufficiently intense intervention during this developmental period may prevent later meaningful access to education, and jeopardize their opportunity to make appropriate progress. Crane suggests that this could also apply to reading, in that insufficient early intervention might not only prevent a child from learning to read fluently, but also subsequently prevent the student from reading to learn, resulting in a permanently negative impact on learning potential.

Although taking a child's learning potential into consideration when evaluating FAPE was reaffirmed by Endrew F. (according to Crane, 2017), this principle was first set forth in Rowley and has been adopted by nearly all Federal circuit courts. One might think that by now, there would be at least a few assessments that have been validated for the purpose of evaluating this construct. To research this, I examined the Buros Center for Testing online database of their Mental Measurements Yearbook, which lists 18 different categories of tests; “learning potential” is not one of them (Buros Center for Testing, 2017). I then entered “Learning Potential Assessment” in their website search box, which yielded a total of 1,269 tests. I examined the title of each test, and when the construct measured was unclear from the title, I examined its purpose. I did not find a single test that purportedly measured either “learning potential” or “educational potential.” Most of the tests appeared to measure one or more of the following: aptitudes/abilities, academic/vocational skills, personality traits, mental disorders, behavior, motivation, developmental milestones, learning style/preferences, creativity, college/graduate school entrance readiness, or personnel/HR-related constructs (e.g., leadership). A few appeared totally unrelated, such as measures of pain.

According to Hamers, Sijtsma, and Ruijssenaars (as cited in Lauchlan & Elliott, 2001), the concept of evaluating “learning potential” is more common in Europe than in the United States. It is generally measured through dynamic assessments (DA), which are based upon Vygotsky's theory of the zone of proximal development. Such methods were developed primarily due to concerns over the validity of traditional, static IQ tests among subgroups such as the socially disadvantaged and students with learning difficulties (Lauchlan & Elliott, 2001). It was felt that such individuals may fail to demonstrate their true learning capacity unless provided appropriate support (“scaffolding”). However, DA instruments designed to measure the broad general construct of “learning potential” tend to be complex, leading to difficulties in establishing their validity and reliability. Perhaps this explains why they have not been widely adopted in the United States, at least by test publishers. However, more recent DA research has focused on specific narrow domains, which will be discussed shortly.

Given the dearth of “learning potential” assessments, what do hearing officers and judges use when taking this aspect of a “child's circumstances into consideration,” as they are supposed to do? I recently spoke to a supervisor of hearing officers, who told me that she primarily uses IQ tests for that purpose, so long as there is no indication in the psychological report that the IQ score may not be valid (e.g., child appeared fatigued). Another hearing officer wrote that he found it nearly always necessary for an education expert to address this question in his hearings (Crane, 2017). Unfortunately, he did not elaborate on what such experts generally testified. However, he did include a footnote with references to two federal court cases which did provide specifics; one referred to the child's “low IQ” (Lessard v. Wilton Lyndeborough School District) and the other referred to the student's “intellectual potential” (Shore Regional High School Board of Education v. P.S.). Thus, it would seem that, in lieu of tests that have been validated for the purpose of measuring “learning potential,” some hearing officers and federal judges are using IQ test scores for this purpose, at least in part, if not in whole.

Although test publishers include a brief description of a test's purpose in their manual, which is included in the Buros Mental Measurements Yearbook, upon examination it would appear that they are actually providing a description of the construct(s) measured, and not the purpose. As illustration, here are some examples of IQ tests commonly used in schools, and their “purpose” per Buros: Wechsler Intelligence Scale for Children-Fifth Edition (WISC-V): “Designed as a clinical instrument for assessing the intelligence of children”; Stanford–Binet Intelligence Scales, Fifth Edition (SB-5): “Designed to assess intelligence and cognitive abilities”; Differential Ability Scales, Second Edition (DAS-II): “To profile a child's strengths and weaknesses in a wide range of cognitive abilities”; and Woodcock–Johnson IV Cognitive Battery: “Designed as a set of norm-referenced tests for measuring intellectual abilities….” (Buros Center for Testing, 2017). It would seem that the purpose for giving the test is left up to the test administrator. According to the National Association of School Psychologists Principles for Professional Ethics, “School psychologists select assessment instruments … that are reliable and valid for the child and the purpose of the assessment” (National Association of School Psychologists, 2010, p. 7; emphasis mine).


The use of an IQ test as a measure of a student's learning potential to determine whether or not a child is receiving FAPE would seem to be a very high-stakes use of the test indeed, and warrants special consideration of its validity for that purpose. Since I could not find a consensus definition for “learning potential,” I will do my best to define it based on how the courts most often seem to use it: Learning potential refers to future achievement if the child learns all she is capable of learning. Neither Rowley nor Endrew F. requires that students be given an ideal education that will lead to maximum expression of their learning potential, but they must be given an education that is reasonably calculated to allow them to make a degree of progress that is appropriate in light of their learning potential. Therefore, if IQ tests are used for this purpose, it would seem important to examine how well they predict future school achievement for the special education population. If a child has a specific learning disability, one would only need to examine how well IQ tests predict achievement in the area(s) of disability addressed by his IEP, since that is the focus of student progress being predicted.

Unfortunately, it appears that most such research has been conducted on typical students, and not those in special education (when a study does not indicate whether subjects were disabled or typical, it seems fair to assume that a majority of the students included were typical). We cannot assume that IQ tests predict achievement for students with disabilities to the same degree as for typical students without supporting evidence.

According to Roth et al. (2015), numerous studies over many years indicate that intelligence is the strongest predictor of scores on tests of academic achievement, with correlations ranging from .30 to .70. Extending this literature, these authors examined the strength of the correlation between IQ scores and school grades through a meta-analysis of 240 independent samples with 105,185 participants. After correcting for sampling error, error of measurement, and range restriction in IQ score, they found a mean correlation of .54, which was very consistent with previous research predicting achievement test scores. However, there was considerable variation, and the degree of the correlation was found to be significantly related to the following moderator variables: school subject matter (correlations were highest for math and science), grade level (correlations were lowest in elementary school and highest in high school), the type of intelligence test (correlations were highest in mixed tests that measured both verbal and nonverbal intelligence), and the year of publication (correlations in studies since 1983 are lower than before 1983). Setting aside all of the caveats one must consider when using IQ as a predictor of school grades, we can cautiously conclude that, in the general population of students, IQ scores account for approximately 29% of the variance in this measure of educational performance. In other words, even our best predictor of future achievement accounts for less than one third of actual achievement in typical students. It should be noted that the researchers did not report the time interval, if any, between collection of the IQ scores and grades. If they were collected simultaneously, this correlation would reflect concurrent validity, not predictive validity, which may overestimate the latter.

The more pertinent question for our current topic is how well IQ scores predict future achievement in students with disabilities. Reliability is a necessary but not sufficient condition for validity, and there is evidence that IQ tests demonstrate lower test–retest reliability in this population. Watkins and Smith (2013) found that 25% of 344 special education students earned Full Scale IQ scores that differed by 10 or more points from their earlier scores upon re-testing on the WISC-IV at an average later interval of 2.84 years. Aside from the issue of stability, one only need to consider the traditional definition of “specific learning disability” (IQ–achievement discrepancy) to know that the predictive validity of IQ is lower in this population. This has been supported by a number of studies. In the Connecticut Longitudinal Study, 232 students were given individual tests of cognitive abilities and achievement annually in grades 1 through 12, starting in 1983 (Ferrer, Shaywitz, Holahan, Marchione, & Shaywitz, 2010). Although none of the participants were placed in special education, test scores identified a subgroup of students that researchers classified as persistently poor readers, based on having a composite reading score at least 1.5 standard deviations below the score predicted by their Full Scale IQ, or with a reading standard score below 90. Analyses over time found differences between typical readers and poor readers in the predictive power of IQ scores. In typical readers, reading and IQ scores were “coupled,” in that IQ scores predicted subsequent reading scores, and reading scores predicted subsequent IQ scores, in what appeared to be a symbiotic, bidirectional relationship. In poor readers, by contrast, this relationship became “uncoupled” over time.

Autism is another disability in which the predictive validity of IQ appears to be problematic. According to Estes, Rivera, Bryan, Cali, and Dawson (2011), better diagnosis and early intervention have led to more children (up to 70%) being classified in the high-functioning range. In examining this population, they found significant discrepancies between IQ and achievement scores in reading, spelling, and math in 27 out of 30 nine-year-olds. Both lower than expected and higher than expected achievement was demonstrated. Test selection may be a critical factor in autism. Dawson, Souliéres, Gernsbacher, and Mottron (2007) found a discrepancy of 30 percentile points, on average, between scores on the WISC-III and the Raven's Progressive Matrices (RPM), in favor of the latter, among a sample of 38 autistic children. No such discrepancies were found in a sample of 24 typical children. They observed similar discrepancies in a sample of 13 autistic adults between the RPM and the WAIS-III, while no such discrepancies were found in a sample of 19 typical adults. A follow-up study by Bölte, Dziobek, and Poustka (2009) also found discrepancies between the two tests in a sample of 48 children and adults with autism, but smaller (9 percentile points on average), and only in those who had IQ scores in the lower range. Of note is that neither of the studies examined the predictive validity of either IQ test, but given the discrepancies between the two tests, this should be explored in future research, as there could be an interaction between type of test, IQ range, and predictive validity in autism.

Some of the research on the predictive validity of IQ scores has been conducted within an RTI context, examining whether IQ predicts response to intervention. This type of research seems especially relevant to this discussion, since we are trying to predict appropriate progress of students who are receiving an intervention (special education). A meta-analysis of 22 studies examined the relationship between IQ scores and four types of reading outcomes (phonological awareness, word reading accuracy, fluency, and comprehension) in response to intervention (Stuebing, Barth, Molfese, Weiss, & Fletcher, 2009). Researchers found that, at most, IQ accounted for 1% to 3% of the unique variance in intervention response. Factors such as type of IQ test, age, and type of reading outcome did not significantly influence results.

As reading researchers Share and Stanovich (1995) noted more than 20 years ago, perhaps the most critical finding from years of research on reading problems is that a major impediment to growth is a deficit in skilled word identification. Although reading comprehension deficits exist that are not attributable to difficulty with word recognition, they are much less common compared to word-level reading difficulties; they accounted for less than 1% of reading problems in a study of 425,000 elementary students (Spencer, Quinn, & Wagner, 2014). In a review of five studies designed to improve word recognition skills in students with an IQ of at least 80, Torgesen (2000) reported that as long as phonological variables were included in the prediction equation, IQ did not explain any of the unique variance in response to intervention. In another study that investigated response to a validated math intervention in 24 third-graders, a shift in strategies from counting to fact retrieval was observed in response to tutoring. However, there were individual differences in improvement, and IQ was not a significant predictor of such differences (Supekar et al., 2013).

Disability Severity and Dynamic Assessment

As previously mentioned, the Court also referred to a child's placement on the spectrum as another relevant factor when evaluating whether progress is appropriate, which suggests level of disability severity. This would seem relatively straightforward when the disability is purely academic, such as a specific learning disability, since the deficient skills are fairly narrow. However, when the disability is one that affects multiple domains of functioning, and impacts them differentially such that there are extreme peaks and valleys, as often occurs in autism, it seems less clear how one gauges level of severity, since it can vary greatly across domains. In fact, one could make the argument that Douglas County School District used measures of maladaptive behavior as an indicator of severity in the case of Endrew F., as his behavior as presented in the Court opinion suggests severe impairment. It may have only been in retrospect, after he changed schools and experienced significant improvement, that his disability appeared to be less severe. This suggests that type of intervention may be a more influential factor than level of severity, which was supported by a study that examined four types of early interventions for autism (Reed & Osborne, 2012). Their analyses showed an interaction between level of severity and time-input. In three of the interventions, gains made were inversely related to the severity of autism, and positively associated with time-input. For the fourth intervention, applied behavioral analysis, the converse was true. However, as pointed out by Vivanti, Prior, Williams, and Dissanayake (2014), the response to early intervention in autism is variable even within a single type of intervention. Even though group level data suggest that early intensive behavioral interventions are effective in improving various domains of functioning in autism, analysis of individual responsiveness shows that gains made by children vary from dramatic to minimal or none. There are also variations in outcomes between studies of the same type of intervention. These authors conclude that current knowledge of the variables that predict individual differences in response to early intervention in autism is limited due to inadequate theoretical and methodological approaches.

So, what is a school psychologist to do? Tell the hearing officer her crystal ball is broken? Search for more valid tests? As mentioned previously, dynamic assessments (DA) were developed in the hope they would be more accurate in predicting academic performance of students with learning difficulties than static, traditional IQ tests. However, it was difficult to establish adequate psychometric properties in such tests designed to measure the general, broad construct of “learning potential.” In recent years, there has been a resurgence in the development of narrower, skill-focused DAs in the context of RTI. One common format is to provide scaffolding in the form of “graduated prompts” in an increasing level of explicitness to aid in task completion. The number of prompts needed, along with the level of explicitness required for successful performance, are viewed as indicators of responsiveness to intervention. DA has been investigated to predict performance in various measures of phonological awareness, decoding, and math; the amount of unique variance in academic performance explained by such measures varies from about 3–13% (Cho et al., 2017; Cho, Compton, Fuchs, Fuchs, & Bouton, 2014; Seethaler, Fuchs, Fuchs, & Compton, 2012). Although this is relatively small, when entered into a multiple regression equation with other validated predictor variables, it increases our overall ability to predict performance, albeit perhaps not enough. Another problem is that, to date, all of the tests used in the studies of DA appear to be authored by the researchers and not published.


The Endrew F. ruling by the Supreme Court has significant implications for the practice of school psychology. It remains to be seen, based on future lower court decisions, just how much and what type of impact it will have. At least one person with 15 years of recent experience as a hearing officer (now retired) believes that it will be harder for school districts to defend IEPs which essentially repeat the same goals from year to year, especially if “… parents present credible expert testimony that the student is capable of making substantially greater progress and, therefore, that the IEP is not appropriately ambitious and that the IEP lacks sufficiently challenging learning objectives” (Crane, 2017). It will be up to school psychologists to examine the validity of the assessments used by such outside experts for the particular child and for the purpose for which they are being used. As the IEP team's assessment expert, school psychologists should also be prepared to present evidence that a student's IEP is sufficiently challenging “in light of the child's circumstances,” and to be explicit in their reports and testimony about the limitations of predictive assessments.


Board of Education of the Hendrick Hudson School District v. Rowley, 458 U.S. 176 (1982).

Bölte, S., Dziobek, I., & Poustka, F. (2009). Brief report: The level and nature of autistic intelligence revisited. Journal of Autism and Developmental Disorders, 39, 678–682.

Buros Center for Testing. (2017). Retrieved from

Cho, E., Compton, D. L., Fuchs, D., Fuchs, L. S., & Bouton, B. (2014). Examining the predictive validity of a dynamic assessment of decoding to forecast response Tier 2 to intervention. Journal of Learning Disabilities, 47(5), 409–423. doi:10.1177/0022219412466703

Cho, E., Compton, D. L., Gilbert, J. K., Steacy, L. M., Collins, A. A., & Lindström, E. R. (2017). Development of first-graders’ word reading skills: For whom can dynamic assessment tell us more? Journal of Learning Disabilities, 50(1), 95–112. doi:10.1177/0022219415599343

Crane, B. (2017). Bill's blog: The Supreme Court's decision in Endrew F. Retrieved from Massachusetts Advocates for Children,

Dawson, M., Souliéres, I., Gernsbacher, M. A., & Mottron, L. (2007). The level and nature of autistic intelligence. Psychological Science, 18(8), 657–662. doi:10.1111/j.1467-9280.2007.01954.x

Endrew, F. v. Douglas County School District RE-1, 137 S. Ct. 988 (2017).

Estes, A., Rivera, V., Bryan, M., Cali, P., & Dawson, G. (2011). Discrepancies between academic achievement and intellectual ability in higher-functioning school-aged children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 41(8), 1044–1052.

Ferrer, E., Shaywitz, B. A., Holahan, J. M., Marchione, K., & Shaywitz, S. E. (2010). Uncoupling of reading and IQ over time: Empirical evidence for a definition of dyslexia. Psychological Science, 21(1), 93–101. doi:10.1177/0956797609354084

Lauchlan, F., & Elliott, J. (2001). The psychological assessment of learning potential. British Journal of Educational Psychology, 71, 647–665.

Lessard v. Wilton Lyndeborough Cooperative School District, 518 F.3d 18 (First Circuit, 2008).

McKenna, L. (2017, March 23). How a new Supreme Court ruling could affect special education. The Atlantic. Retrieved from

National Association of School Psychologists. (2010). Principles for professional ethics. Retrieved from

National Center on Intensive Intervention. (2013). Data-based individualization: A framework for intensive intervention. Retrieved from

Reed, P., & Osborne, L. (2012). Impact of severity of autism and intervention time-input on child outcomes: Comparison across several early interventions. British Journal of Special Education, 39(3), 130–136.

Roth, B., Becker, N., Romeyke, S., Schäfer, S., Domnick, F., & Spinath, F. M. (2015). Intelligence and school grades: A meta-analysis. Intelligence, 53, 118–137. doi:10.1016/j.intell.2015.09.002

Seethaler, P. M., Fuchs, L. S., Fuchs, D., & Compton, D. L. (2012). Predicting first graders’ development of calculation versus word-problem performance: The role of dynamic assessment. Journal of Educational Psychology, 104(1), 224–234.

Share, D. L., & Stanovich, K. E. (1995). Cognitive processes in early reading development: Accommodating individual differences into a model of acquisition. Issues in Education, 1(1), 1–57.

Shore Regional High School Board of Education v. P. S., 381 F.3d 194 (Third Circuit, 2004).

Spencer, M., Quinn, J. M., & Wagner, R. K. (2014). Specific reading comprehension disability: Major problem, myth, or misnomer? Learning Disabilities Research & Practice, 29(1), 3–9. doi:10.1111/ldrp.12024

Stuebing, K. K., Barth, A. E., Molfese, P. J., Weiss, B., & Fletcher, J. M. (2009). IQ is not strongly related to response to reading instruction: A meta-analytic interpretation. Exceptional Children, 76(1), 31–51.

Supekar, K., Swigart, A. G., Tenison, C., Jolles, D., Rosenberg-Lee, M., Fuchs, L. & Menon, V. (2013). Neural predictors of individual differences in response to math tutoring in primary-grade school children. Proceedings of the National Academy of Sciences of the United States of America, 110(20), 8230–8235. doi:10.1073/pnas.1222154110

Torgesen, J. K. (2000). Individual differences in response to early interventions in reading: The lingering problem of treatment resisters. Learning Disabilities Research & Practice, 15(1), 55–64.

Vivanti, G., Prior, M., Williams, K., & Dissanayake, C. (2014). Predictors of outcomes in autism early intervention: Why don't we know more? Frontiers in Pediatrics, 2(58). doi:10.3389/fped.2014.00058. Retrieved from

Watkins, M. W., & Smith, L. G. (2013). Long-term stability of the Wechsler Intelligence Scale for Children-Fourth Edition. Psychological Assessment, 25(2), 477–483. doi:10.1037/a0031653

Yell, M. L., Katsiyannis, A., Parks Ennis, R., Losinski, M. & Christle, C. A. (2016). Avoiding substantive errors in Individualized Education Program development. Teaching Exceptional Children, 49(1), 31–40. doi:10.1177/0040059916662204

Zirkel, P. (2017). The Supreme Court's decision in Endrew F. v. Douglas County School District RE-1: A meaningful raising of the bar? West's Education Law Reporter, 341, 545–554. Retrieved from

Shawn K. O'Brien, PsyD, NCSP, has been employed as a school psychologist in six school districts in five states, including Douglas County School District (although she was not involved in the case of Endrew F.) and is recently retired. She can be reached at

© 2018, National Association of School Psychologists
January/February 2018, Volume 46, Number 5