Identifying and Serving Gifted and Talented Students: Are

https://doi.org/10.1177/0016986220988308

Gifted Child Quarterly

2021, Vol. 65(2) 115 –131

Gifted Children

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Feature Article

Designing and implementing programs for gifted and tal-

ented students requires careful thought and planning about

four key programming elements (a) identification and place-

ment, (b) intervention, (c) infrastructure and resources, and

(d) program and student outcomes (Eckert & Robins, 2017;

Reis, 2006; Reis & Gubbins, 2017). Within each of these ele-

ments, basic focus questions include the following: Who are

the students in our school district who exhibit gifts and tal-

ents? How do we find them? What intervention approaches,

including curricular, instructional, and service delivery strat-

egies, are most appropriate to meet their academic needs?

What human and material resources will support the imple-

mentation of programming plans? And, finally, what pro-

gram and student outcomes are expected based on program

design elements?

These questions are equally important, and decisions

about one question affect others. Districts and schools must

address the interdependence and interconnectedness among

these questions forming the foundation for creating challeng-

ing academic opportunities for students with identified and

potential gifts and talents. The questions also maintain the

focus on the ultimate objectives of designing, developing,

and implementing programs for gifted and talented students

(a) to develop a defensible identification system reflecting

students’ academic needs and (b) to match student learning

needs with appropriate interventions (e.g., curricular, instruc-

tional, and service delivery strategies).

Alignment of Identification and

Programming

The importance of alignment between identification and pro-

gramming is widely noted in gifted and talented education

literature, and we would hope to find this to be the norm in

988308GCQ

XXX10.1177/0016986220988308Gifted Child QuarterlyGubbins et al.

research-article2021

University of Connecticut, Storrs, CT, USA

California Baptist University, Riverside, CA, USA

University of Virginia, Charlottesville, VA, USA

Program Analyst, Overland Park, KS, USA

Cleveland State University, Cleveland, OH, USA

Corresponding Author:

E. Jean Gubbins, University of Connecticut, 2131 Hillside Road, Unit 3007,

Storrs, CT 06269-3007, USA.

Email: [email protected]

Identifying and Serving Gifted and

Talented Students: Are Identification

and Services Connected?

E. Jean Gubbins

, Del Siegle

, Karen Ottone-Cross

D. Betsy McCoach

, Susan Dulong Langley

Carolyn M. Callahan

, Annalissa V. Brodersen

and Melanie Caughey

Abstract

The importance of alignment between identification processes and program design is widely noted in gifted and talented

education literature. We analyzed publicly available district gifted program plans (Grades 3-5) from two states to examine

the extent to which district identification practices matched intervention strategies. Our team developed a coding scheme

matrix with 133 items for State 1 (n = 115) and State 2 (n = 178). The results of this study indicated that, at least in terms

of planning, districts in the two states we examined appeared to be aligning identification and programming practices to

meet the needs of gifted students identified in mathematics and/or reading/English language arts. In State 1, at least 60%

of the districts reported the following intervention strategies in mathematics and reading/English language arts: faster pace

of coverage, regular grade-level standards, in-depth coverage, preassessment, above grade-level standards, and expanded

grade-level standards. In contrast, State 2 districts reported faster pace of coverage; however, with less commonly utilized

interventions, subject-matter identification significantly influenced their usage. Differentiation was the primary learning

environment strategy utilized by districts in both states.

Keywords

academically gifted, content analysis, elementary, gifted identification, programming, qualitative analyses of district program

plans

116 Gifted Child Quarterly 65(2)

practice. At least three decades of literature have referenced

this fundamental connection.

Treffinger (1988) emphasized

the focus on student needs to facilitate appropriate instruc-

tion rather than categorizing and labeling students. Callahan

(1996) addressed the disconnect between identification and

programming by urging us to ask whether enough time is

spent on matching services to student needs or simply identi-

fying needed services. Matthews (1997) concurred by empha-

sizing the importance of matching identification to program

delivery on an ongoing basis appropriate to the individual.

Because general education classrooms serve many gifted and

talented students, Schroth and Helfer (2008) encouraged

general education teachers to both identify student needs

and develop appropriate instruction to meet them. Finally,

Callahan et al. (2014) drew a clear connection between the

need for a definition of giftedness and an identification plan

to guide services, curriculum, instruction, and resources.

Although many states define giftedness and how to iden-

tify gifted and talented students, the link between identifica-

tion and programming is less obvious (Adams, 2006; Brown,

2016; Shaunessy, 2003). Reis (2006) and Reis and Gubbins

(2017) identified consistency as one of the traits of high-

quality programming for high-ability learners. One mini-

mum test for consistency is alignment between a district’s

identification procedures (which in effect operationalize the

definition of giftedness) and programming through curricu-

lum and instruction. As Callahan et al. (2013) asserted,

“Congruence between identification and programming is so

important it might be viewed as ‘the golden rule’ of gifted

education” (p. 88).

Consistency across identification and programming

options becomes even more important when considering stu-

dents from culturally, linguistically, and economically diverse

populations. Peters and Engerrand (2016) emphasized the

importance of matching identification systems with program-

ming to increase equity in gifted and talented education. The

researchers recommended domain-specific identification

and intervention. For example, longitudinal data from Project

EXCITE (a collaboration between Northwestern University

and Evanston/Skokie School District, Evanston, IL) with an

emphasis on identification and programming in mathematics

reported 70% of student participants begin high school hav-

ing already completed 1 to 2 years of high school mathemat-

ics coursework (S.-Y. Lee et al., 2009). Students were eligible

for Project EXCITE as of Grade 3 if they were from under-

represented groups and

have the potential to achieve at high levels as demonstrated by

their ability to think critically and engage in problem solving,

demonstrate the ability to work beyond their current grade level,

and demonstrate a high level of interest, curiosity, and

enthusiasm for learning mathematics and science. (p. 141)

Providing services in specific areas of talent requires a

deliberate connection between identification and program-

ming. Peters et al. (2014) called for linking justifiable

identification to programming for students to be successful.

However, this connection has not been documented as occur-

ring in practice in the gifted and talented literature. According

to Callahan et al. (2017), program survey data indicated only

one fourth of elementary schools designated curriculum-

directed learning in the program. In a study conducted by the

National Center for Research on Gifted Education (Hamilton

et al., 2018), 69% of districts in three states identified stu-

dents as advanced in reading/English language arts (ELA),

and 66% identified students as advanced in mathematics, yet

fewer than 11% of districts in those states designated specific

reading/ELA or math curriculum designed for gifted and tal-

ented students.

The same survey data indicated most teachers of gifted

and talented students have wide latitude in determining the

content of the gifted and talented program. This instructional

freedom may contribute to the mismatch between identifica-

tion and services and result in a shotgun approach with no

alignment among identification practices, curriculum, instruc-

tion, and/or service practices.

The misalignment of identification, services, and outcome

measures hinders the evaluation of gifted program effectiveness,

and ultimately undermines arguments justifying services for

gifted and talented students. This situation limits the field’s

ability to measure the benefits of gifted services, let alone justify

them. (Siegle, 2020-2025, p. 1)

Identification and placement, intervention (including

curriculum and instruction), and program service options

(grouping) are interconnected. Identification and placement

are multistep procedures often guided by state and local

policies including definitions of giftedness. According to

Gubbins (2006), defensible identification systems answer

the following questions:

Who are the gifted and talented students? Why are we striving

to identify them? How do we find them? What are the most

appropriate tools for identifying students’ gifts and talents?

How are data from various tools analyzed and interpreted?

Who is responsible for identifying students’ gifts and talents?

(pp. 50-51)

Answers to these questions are important guidelines for

appropriate placement decisions. Data gathered in the identi-

fication process should lead to the decisions guided by the

identified students’ areas of talent and advanced achieve-

ment through direct interventions, such as pullout classes or

push-in services in general education classes. These inter-

ventions need to be accompanied by curriculum extensions

in the area of giftedness or special accelerated content classes

in areas of academic strength.

The programming standards established by the National

Association for Gifted Children (2010) summarize the evi-

dence and best practice in the field in relating identification

practices to programming services. Evidence-based practices

include the following [emphasis added]: Educators use

Gubbins et al. 117

evidence-based instructional and grouping practices to allow

students with similar gifts, talents, abilities, and strengths to

learn together (1.3.1). Educators use universal screening and

multiple indicators of potential and achievement at various

grade levels from preK through Grade 12 to provide multiple

entry points to services designed to meet demonstrated needs

(2.1.3). Educators use and interpret quantitative and qualita-

tive assessment information to develop a profile of the inter-

ests, strengths and needs of each student with gifts and

talents (2.4.3). Educators use models of inquiry to engage

students in critical thinking, creative thinking, and problem-

solving strategies, particularly in their domain(s) of talent,

both to reveal and address the needs of students with gifts

and talents (3.4.3). Educators develop a preK through Grade

12 continuum of programming and services relevant to stu-

dent talent areas that is responsive to students’ different lev-

els of need for intervention (5.2.2).

These guidelines confirm the identification process as a

vehicle for identifying students who have characteristics

warranting adjustment to the curriculum and instruction

these students receive and the setting in which the instruction

is delivered. This position serves as the analytic lens through

which our data collection and analysis proceeded (Caelli

et al., 2003). We posed the general research question:

Is the recommendation of a clear relationship between the

domain of talent identified and programming response

played out in practice?

Our specific research questions about potential connec-

tions between identifying and serving students with gifts and

talents included

(a) What are the reported practices in identifying and

modifying programming in reading/ELA and mathe-

matics by districts in two states with gifted and tal-

ented identification and programming mandates, as

documented in district gifted program plans?

(b) To what extent do reported identification practices

align with interventions (e.g., curricular, instructional,

and service delivery strategies), as recommended in

the gifted and talented education literature and docu-

mented in district gifted program plans?

The focus on mathematics and reading/ELA reflects the

dominance of these two areas of identification and services

from among academic content areas reviewed in surveys of

gifted and talented programs (e.g., Callahan et al., 2017).

Theoretical Framework

Our theoretical framework is a talent development model

proactively addressing the importance of recognizing and

nurturing students’ gifts and talents through an aligned series

of steps (Siegle et al., 2016). The five major components of

the model include the following:

1. Preidentification: Identify students who would bene-

fit from talent emergent experiences

2. Preparation: Opportunities for students to enhance

knowledge and skills to develop talents and abilities

3. Identification: Systematic procedures to identify and

select students who need services beyond those avail-

able in general education classrooms

4. Intervention: Services commensurate with identified

talent areas

a. Curriculum and instruction: “address the pace

and depth of learning commensurate with the

learning differences of identified gifted students

. . .” (p. 117)

b. Service delivery: “grouping arrangements under

which curriculum and instruction are delivered”

(p. 117)

5. Outcomes: Cognitive and affective outcomes based on

codified program and student goals and objectives.

Each component is designed to move students toward clearly

defined cognitive and affective program and student out-

comes. The extent to which these outcomes will be achieved

through federal or state policy initiatives is variable. In this

study, we examine the alignment between the third and

fourth components of the model (i.e., identification and

intervention).

Literature Review

Gallagher (2002) stated, “Social policy sets the rules and

standards by which we provide special education experi-

ences for gifted students” (p. vii). Policies evolve from fed-

eral or state legislation, regulations, and guidelines. Such

information influences program elements:

(a) Who receives the special resources?—the eligibility question,

(b) Who delivers the resources?—the teacher qualification issue,

special program, and (d) What are the conditions under which

the resources are delivered?—service delivery parameters.

(Gallagher, 2002, p. vii)

Because there is no federal mandate in the United States for

the identification of and programming for students with

identified or potential gifts and talents, states determine

whether to promulgate identification and programming man-

dates and create corresponding laws, regulations, and guide-

lines for districts.

Stephens (2008) completed an historical overview of fed-

eral legislation affecting the education of gifted and talented

students and described the attention “as a pendulum which

swings from interest to disinterest depending on the degree to

which the nation feels vulnerable. . .” (p. 388). She posed the

following questions: “Why are the academic development

and social-emotional nurturance of our nation’s brightest

students continuing to be neglected? Why has interest in the

118 Gifted Child Quarterly 65(2)

special population been so sporadic?” (p. 387). Without fed-

eral legislation, states may or may not choose to consider or

enact legislation focusing on identifying and serving stu-

dents with gifts and talents.

The National Association for Gifted Children and The

Council of State Directors of Programs for the Gifted (NAGC

& CSDPG, 2014-2015) reported 32 of the 40 responding

states have state mandates related to gifted and talented edu-

cation. Of the states, 28 require identification and services

and four states require only identification. With such incon-

sistencies from state to state, students who need access to

challenging learning opportunities may be limited because of

their geographic location. When four states commit legally

only to identification, data collected for decision-making

purposes may indicate programs and services that do not

exist in practice. Plucker et al. (2013) recommended we pay

attention: “When any new education policies are created

policymakers should ask themselves two questions: How

will the proposed policy impact our highest achieving stu-

dents? How will the proposed policy help more students

achieve at the highest levels?” (p. 24). They continued with a

warning: “Each state should quickly examine its policies that

may help or hinder the promotion of high achievement in its

K-12 schools” (p. 25).

Identification

Multiple researchers recommend a multifaceted approach

to identification including portfolios; dynamic assessment;

curriculum-based performance; observations; nonverbal

assessments; teacher checklists; and peer, parent, teacher,

and self-nominations; in addition to cognitive assessments

and achievement tests (Borland, 2014; Borland et al., 2000;

Callahan et al., 2013; Frasier et al., 1995; Gallagher &

Gallagher, 2013; McBee, 2006; Plucker & Callahan, 2014;

Wiley & Brunner, 2013). Furthermore, according to best

practices in identification, as identified in the literature,

well-defined selection criteria should be included in the

process, accompanied by professional development, to

ensure implementation fidelity (Callahan et al., 2013; Little

& Paul, 2011). All phases of program design should include

an emphasis on program evaluation to determine the what,

why, how, and where of decision making (Robinson et al.,

2014). Callahan and Hertberg-Davis (2013) emphasized the

importance of utilizing measurable goals assessing identifi-

cation, curriculum development and implementation, pro-

gram administration, and staff selection processes during the

program evaluation process.

Intervention: Curriculum and Instruction

For curriculum and instruction, the literature in gifted

and talented education supports including domain-specific

curriculum; process skills development (Purcell & Eckert,

2006; Rogers, 2007; VanTassel-Baska, 2006; VanTassel-

Baska & Little, 2011, 2017); greater depth, breadth, and

complexity of curriculum (Kaplan, 2013); enrichment

(Gubbins, 2014; Renzulli & Reis, 2014); adherence to stan-

dards; assessment and curriculum compacting (Reis et al.,

2016; VanTassel-Baska, 2013); and culturally responsive

practices (Worrell, 2013). Such a long list of approaches to

curriculum and instruction is more like a menu rather than a

coordinated set of opportunities responsive to students’

needs informed by a defensible, research-based identifica-

tion system. This results in multiple service delivery options.

It can also lead to a misalignment between the identification

criteria used to locate students who need additional services

and the types of services those students receive.

Intervention: Service Delivery

Recommended service delivery options include general and

domain-specific pullout programs (Gubbins, 2013), in-class

programming, ability grouping (Steenbergen-Hu et al.,

2016), cluster grouping (Gentry, 2014; Gentry et al., 2014),

differentiated instruction (Tomlinson, 2013), acceleration

(Assouline et al., 2013, 2014; Colangelo et al., 2013;

Subotnik et al., 2015), and homogeneous grouping (Schroth,

2014).

NAGC and CSDPG (2014-2015) prepared an analysis of

state survey policy and practice data. Of the respondents, the

following service delivery models were frequently used in

early Elementary Grades 1 to 3: cluster classrooms,

resource room, and regular classroom. Cluster classrooms

and resource rooms were also frequently used in Grades 4

to 6; additional models included subject acceleration and

self-contained classroom. In a more recent study, Hodges

and Lamb (2019) analyzed service delivery models in

Washington State from 2006 to 2012. The following models

were prevalent among districts: part-time grouping (also

known as pullout programs, 39%), advanced subject place-

ment (type of acceleration, 28%), and differentiated instruc-

tion in regular classrooms (27%). As noted earlier (NAGC &

CSDPG, 2014-2015), these models are also used in elemen-

tary schools.

Method

Selection of States for In-Depth Review

In this study, we examined reported practice in 293 district

gifted program plans from two of three states in the larger

study (Siegle et al., 2017). Because this study was part of a

larger federally funded study in which we promised anonym-

ity to the participating states, we cannot disclose the states’

identities. Initially, we sent an email survey to state coordina-

tors of gifted and talented programs and screened their

responses using the following criteria:

Gubbins et al. 119

 Mandated services for gifted and talented students

 Available data sets allowing identification of impor-

tant student-level outcomes for gifted and talented

students in general and traditionally underserved

gifted and talented students in particular. To be con-

sidered for inclusion, the state’s data set comprised

student achievement scores over time on standard-

ized assessments, indicated whether a student was

identified as gifted and talented, the school the stu-

dent attended, and student demographics (e.g., date

of birth)

 Required districts to submit written plans describing

how they serve gifted and talented students to the state

department of education (hereafter referred to as dis-

trict gifted program plans)

Of the states responding to the email survey, 11 met all

criteria. To arrive at purposive sample of states to include

wherein the characteristics of the selected sample could be

contextualized and described (Patton, 2002), we used several

steps. We identified the final set of state partners as those

meeting both criteria above and being reasonable candidates

for the second and third stages of the study. These stages

included a comprehensive survey of district- and local-level

practices and site visits to examine practices. We further

examined the public documents on each state with these

guiding criteria:

 A state director with advanced training in gifted and

talented education who was familiar and actively

involved with schools around the state

 A commitment to historically underserved popula-

tions of gifted and talented students, evidenced by the

presence of a notable number of students from these

groups in gifted and talented programs and evidence

in policy or practices of efforts to identify students

from those populations

 Easily accessible information about state laws and

policies on gifted and talented education

 Vertically scaled student achievement data

 Diversity of allowable and recommended service

delivery options

 A reputation for educational innovation and reform

and for using applied research to guide and support

the innovation

Three states appeared to meet all these criteria. Members

of the research team contacted gifted and talented education

specialists in those states to confirm whether our interpreta-

tion of the collected data appeared to be consistent with their

experiences. These three states appeared to meet the criteria

and were invited to participate.

These states implemented

gifted and talented identification and programming mandates,

required districts to submit a district gifted program plan for

identifying and serving gifted and talented students, and

maintained publicly available district gifted program plans on

the Internet. The district gifted program plans for Grades 3 to

5 represented current and future plans for multiple years.

Unfortunately, on downloading the district gifted program

plans, we discovered the district plans from one state were

incomplete. Therefore, our results reflect district gifted pro-

gram plans from two of the three states originally selected.

Each district gifted program plan followed specific

reporting framework categories, such as addressing com-

munication, definition identification, programming, program

accountability, student accountability, personnel, and budget.

The framework categories of interest were identification and

programming. All district gifted program plans were down-

loaded from the state or district website at the same point in

time and were the district gifted program plans guiding pro-

grams. The district gifted program plans included both cur-

rent operations and plans for future changes.

For the remainder of this article, we refer to the states as

State 1 and State 2. As part of the university and other insti-

tutional review boards, as well as data collection agreements,

we committed to the nondisclosure of specific states and dis-

tricts. The combination of publicly available data, state

achievement test data, survey data, observation data, and

interview data allowed us to create a data corpus with per-

mission at multiple levels of departments of education and

districts. Therefore, only general demographics are provided

for State 1 and State 2.

State 1 is located in the Southeast, and State 2 is located

in the Midwest. In each state, the districts comprise urban,

rural, and suburban communities with widely varied pop-

ulation sizes, racial/ethnic makeup, and socioeconomic

conditions.

Identification procedures related to gifted and talented

students are similar in State 1 and State 2 as districts use

multiple criteria (e.g., achievement data, cognitive ability

tests, teacher and parent nominations/referrals, performance

data, including student work samples). State 1 allows the

identification procedures to be designed and implemented by

local education agencies. In contrast, State 2, among other

criteria, includes guidance about a specific criterion for the

designation of students as gifted and talented. In State 2, stu-

dents must achieve the 95th percentile or above on a nation-

ally normed test, observation instrument, or performance

assessment. In both states, one purpose of the identification

procedures is to identify gifted and talented students from

traditionally underrepresented groups (e.g., students from

culturally, linguistically, and economically diverse commu-

nities and students who are twice exceptional) who require

differentiated educational opportunities.

Document Analysis

Our research team completed a document analysis of each

district gifted program plan for students in Grades 3 to 5. To

guard against potential bias of the researchers in creation of

120 Gifted Child Quarterly 65(2)

the coding scheme (Caelli et al., 2003), we examined texts

and journal articles representing a broad base of views in

the field and the position of the most prestigious profes-

sional associations in the field (e.g., Callahan, 2013; McBee,

2006; NAGC, 2010; Rogers, 2007; Siegle et al., 2016;

Steenbergen-Hu & Olszewski-Kubilius, 2016; VanTassel-

Baska, 2006). This review process helped us focus on best

practices in identification and programming. From the texts

and journal articles, we documented relevant components

of gifted and talented programs to establish the credibility

of the coding scheme.

Recognizing that document analysis represents a subset

of the general field of qualitative analysis, we proceeded

deductively (Frey, 2018) with the first step of developing a

coding scheme. To ensure the trustworthiness and credibil-

ity of the deductive coding scheme (Frey, 2018; Peterson,

2019), we started with a theoretical framework of two of the

principal components of gifted and talented programming

described above (Siegle et al., 2016). We were addressing

identification of gifted students and services that are pro-

vided. Accordingly, we built on the model and established

key components of identification and programming from

the literature. According to Frey, once a codebook is

established, with codes organized by categories and sub-

categories, the researcher should test the codebook using a

subsample of the documents to assess its appropriateness

and completeness. We conducted an initial analysis of a

subset of three district gifted program plans in each state

and compared the codebook to the district gifted program

plan categories and content. We then submitted the coding

scheme to two experts in qualitative data analysis outside

gifted and talented education for review. We asked them to

review consistency between the theoretical framework and

the categories and subcategories on the matrix and to assess

the appropriateness of applying the matrix to the plans as

required by state guidelines. We modified the matrix accord-

ing to their feedback. The resulting matrix consisted of 139

topics with definitions and descriptions, as needed, to clar-

ify possible exclusion criteria for the topic with a rating

scale of 0 = practice is not present and 1 = practice is

present.

From the original 139 categories of ratings across

all dimensions of program planning and operation, we iden-

tified those relating only to identification of giftedness in

the domain of mathematics or reading/ELA, curriculum in

the domain of mathematics or reading/ELA, other identifi-

cation, and learning environment.

The individuals who rated the district gifted program

plans were either senior faculty in gifted and talented educa-

tion at institutions offering doctoral programs in gifted and

talented education or doctoral students at those institutions.

Each doctoral student’s curriculum vitae documented back-

ground and experience in teaching in and/or gifted and tal-

ented program administration and the completion of core

courses in gifted and talented education. Additionally, the

doctoral students completed at least one course in the collec-

tion and analysis of qualitative data.

Our nine-member research team met for 8 hours on each

of 2 days to test the coding scheme matrix by rating district

gifted program plans, calculating interrater agreement, and

revising the matrix, as needed. Each coder rated a selected

district gifted program plan independently. For each matrix

item, we divided the sum of ratings by the number of raters

and multiplied by 100 to calculate the mean percentage.

Then we determined the mode distribution to establish

whether the agreed rating would be “0” or “1.” If the mode

among raters was “1,” the results would immediately yield

the percentage of agreement among raters. However, if the

mode among raters was “0,” the inverse percentage agree-

ment was calculated by subtracting the calculated percentage

mean rating from 100. An overall percentage agreement of

100% indicated the same rating across all raters, 88.9% indi-

cated one rating was different among raters, 77.8% indicated

two ratings were different among raters, 66.7% indicated

three ratings were different among raters, and 55.6% indi-

cated four ratings were different among raters. We calculated

the overall percentage for inter-rater agreement for all items

by finding the average of the percentage agreement among

raters for all items (Gisev et al., 2013).

Prior to analyzing all district gifted program plans, we

conducted another review of the coding scheme matrix and

deleted six items not representative of new or critical topics.

Then we assigned individuals to review subsets of the 293

district gifted program plans using the reduced set of 133

coding scheme matrix items based on the group consensus

process described above and the interrater agreement process

(see Figure 1).

To ensure continued agreement, all coders on both teams

individually rated every 10th plan, and we then calculated

the interrater agreement. The criterion for continuing the rat-

ing process was set at 80% interrater agreement for each item

of the 133 items reviewed on the sample of every 10th plan.

Additionally, the entire team of nine members analyzed 15 of

the 115 district gifted program plans for State 1. For State 2,

four team members analyzed 23 of the 178 district gifted

program plans and nine team members analyzed two district

gifted program plans.

The average interrater agreement across all plans and all

items evaluated by more than one rater was 87.8% for State

1 and 91.9% for State 2. The resulting data from the analysis

of district gifted program plans serve as the basis for our

study of the match between identification and intervention

strategies.

For the purpose of this study, we selected two specific

identification items from the coding scheme matrix (“Identify

students in reading/ELA, e.g., a student is identified as gifted

in reading/ELA, but not necessarily gifted in other areas”

and “Identify students in mathematics, e.g., a student is iden-

tified as gifted in mathematics, but not necessarily gifted in

Gubbins et al. 121

other areas”). In particular, we examined the degree to which

districts identified students as gifted and talented in mathe-

matics or reading/ELA and provided specific services in

those domains. The eight intervention strategies we exam-

ined were faster pace, more in-depth or greater breath of cov-

erage, use of regular education standards, preassessment of

content knowledge, above grade-level content, expanded

grade-level standards, separate curriculum, and culturally

responsive curriculum. We also used both identification

items to check the match to learning environments provided

for subject area giftedness. We examined the five learning

environments of differentiation, cluster grouping, tiered

instruction, push-in services, and pullout classes based on

survey data from NAGC and CSDPG (2014-2015) and

works by Borland (2005), Coleman and Hughes (2009), and

Tomlinson (2013).

Data Analysis

Our data analyses were descriptive in nature. We examined

whether districts reporting the use of mathematics or read-

ing/ELA domain-specific identification were more likely to

provide advanced content and/or differentiated learning

experiences for those students in those respective subject

areas. We calculated effect sizes for each 2 × 2 contingency

table using the phi coefficient. Generally, phi coefficients

less than .10 are considered negligible ( = 0.00-0.10); phi

coefficients between 0.10 and 0.20 are small, phi coefficients

between 0.20 and 0.40 are considered moderate, phi coeffi-

cients between 0.40 and 0.60 are relatively strong, and phi

coefficients above 0.60 are considered strong (D. K. Lee,

2016). We also report the odds ratios (ORs) for each of these

comparisons. The OR representing the odds of endorsing a

given strategy for districts using domain-specific identifica-

tion divided by the odds of endorsing the strategy for dis-

tricts not using domain-specific identification (see Table 1).

ORs greater than 1 indicate districts using domain-specific

identification were more likely to report using the technique;

ORs less than 1 indicate schools not using domain-specific

identification were more likely to report using the technique,

and ORs of 1.0 indicate the two groups were equally likely to

report using the technique. If the confidence interval of the

OR contains 1, then the difference in the OR would not be

statistically significant, setting the Type I error rate (alpha) to

.05, the conventional criterion for statistical significance.

However, given the descriptive nature of our data and

because we have information for the entire population of dis-

tricts within each of the two states, we have elected not to

engage in statistical hypothesis testing.

Results

Most districts in the two states reported using domain-

specific identification. Domain-specific identification may

include any or all the following types of assessments: (a)

state standardized testing, (b) norm-referenced achievement

tests, (c) teacher nominations/referrals, (d) parent nomina-

tions/referrals, (e) teacher rating scales, (f) student work

samples, and (g) dynamic assessment. Furthermore, with

only one exception across the two states, districts identify-

ing students in one domain also identified students in the

other domain. In State 1, of the 115 districts, 90 districts

(78.3%) identified students specifically in mathematics

and 89 of those 90 (77.3% overall) identified students

Figure 1. Development of the coding scheme matrix.

Note. NAGC = National Association for Gifted Children; G/T = gifted/talented; ELA = English language arts.

122 Gifted Child Quarterly 65(2)

specifically in reading/ELA. In State 2, of the 178 districts,

75.8% (n = 135) identified students in both mathematics

and reading/ELA.

Separate Curriculum

Even though most districts identified students as gifted and

talented in mathematics and/or reading/ELA, very few dis-

tricts reported using separate curricula for gifted and talented

students in mathematics or reading/ELA. In State 1, only

19.1% of districts identifying students as gifted and talented

in mathematics (and 16% of the districts not identifying stu-

dents specifically in mathematics) reported offering a sepa-

rate mathematics curriculum for gifted and talented students.

In State 2, the percentages were even lower: only 4.4% of

districts identifying students in mathematics (and 2.3% that

did not) reported using a separate mathematics curriculum

for gifted and talented students. In reading/ELA, the same

trend emerged. In State 1, 36.7% of districts identifying stu-

dents as gifted and talented in reading/ELA (and 28% of dis-

tricts that did not) reported using a separate reading/ELA

curriculum for those students. In State 2, 12.6% of districts

identifying students as gifted and talented in reading/ELA

(and 2.3% that did not) reported using a separate reading/

Table 1. Odds Ratios Represent the Odds of Endorsing a Strategy in Districts With Domain-Specific Identification Versus the Odds of

Endorsing a Strategy in Districts Without Domain-Specific Identification.

Strategy

State 1 State 2

Math OR [CI] ELA OR [CI] MATH OR [CI] ELA OR [CI]

Faster pace of coverage in the gifted

curriculum

3.51 [1.06, 11.61] 2.96 [0.85, 10.32] 11.40 [4.22, 30.80] 14.63 [4.94, 43.29]

Regular education standards for gifted

students

3.86 [0.89, 16.69] 3.95 [0.75, 20.95] 0.81 [0.38, 1.72] 4.25 [1.43, 12.68]

More in-depth or greater breadth of

coverage in grade-level content in

curriculum for gifted students

3.63 [1.43, 9.25] 3.37 [1.31, 8.72] 1.45 [0.69, 3.02] 4.61 [1.71, 12.48]

Preassessment of content knowledge and

skills in curriculum for gifted students

1.53 [0.63, 3.70] 2.06 [0.84, 5.06] 0.77 [0.38, 1.54] 0.77 [0.38, 1.54]

Above grade-level standards for gifted

students

1.08 [0.45, 2.62] 1.00 [0.40, 2.47] 12.52 [1.66, 94.69] 12.00 [1.59, 90.85]

Extended or expanded grade-level

standards for gifted students

3.21 [1.28, 8.01] 3.22 [1.28, 8.12] 0.71 [0.31, 1.64] —

Separate curriculum (purposely designed

for gifted students)

1.24 [0.38, 4.09] 1.49 [0.56, 3.94] 1.95 [0.23, 16.69] 6.05 [0.78, 46.88]

Culturally responsive curriculum 0.42 [0.07, 2.65] 0.53 [0.09, 3.10] — —

Differentiation — — 1.72 [0.82, 3.62] 1.72 [0.82, 3.62)

Cluster grouping 2.85 [1.09, 7.43] 3.08 [1.17, 8.10] 2.57 [1.11, 5.98] 2.57 [1.11, 5.98]

Tiered instruction 1.27 [0.53, 3.08] 1.43 [0.58, 3.53] 1.67 [0.46, 6.06] 1.67 [0.46, 6.06]

Push-in 1.78 [0.65, 4.90] 1.66 [0.60, 4.59] 3.99 [0.90, 17.73) 3.99 [0.90, 17.73)

Pullout (subject area) 3.56 [1.13, 11.23] 3.50 [1.11, 11.05] 3.15 [0.70, 14.19] 3.36 [0.75, 15.05]

Note. Odds ratios greater than 1 indicate districts engage in domain-specific identification in the area are more likely to use the strategy than districts

that do not. Odds ratios of 1.0 indicate two types of districts are equally likely to use the specified strategy. Odds ratios less than 1.0 indicate districts

do not identify specifically in the domain are more likely to use the strategy. Any confidence interval containing 1.0 would not be statistically significant at

 = .05. Missing odds ratios are undefined because they have a denominator of 0 within the odds ratio. ELA = English language arts.

ELA curriculum for those students. Across the two states,

very few of the districts reported using culturally responsive

curriculum in reading/ELA or mathematics. Only six of the

115 districts in State 1 and none of the districts in State 2

reported using culturally responsive curriculum in either

mathematics or reading/ELA (see Tables 2, 3, 4, and 5 for

these results).

Classroom Learning Environments/Service

Delivery Models

We also investigated what types of classroom learning envi-

ronments districts planned to serve students identified as gifted

and talented and whether they differed across districts utiliz-

ing/not utilizing domain-specific identification. Across the

two states, differentiation was by far the most commonly used

learning strategy: The majority of districts across the two

states reported using differentiation. In State 1, virtually all

districts mentioned differentiation (100% of those with domain

identification and 92% of those without). In State 2, 76.3% of

the districts identifying in a domain and 65.1% of the districts

not reporting utilizing differentiation (see Tables 6 and 7).

In State 1, cluster grouping was a common service

delivery option, and it was more prevalent in districts using

Gubbins et al. 123

Table 2. State 1: Prevalence of Services in Districts Not Using Domain-Specific Identification in Math (n = 26) and Using

Domain-Specific Identification in Math (n = 89).

Mathematics curricular content

No math identification Math identification

n % n %

Faster pace of coverage in the gifted mathematics curriculum

(acceleration, advanced content in shorter time frame, above

grade-level curriculum)

20 76.9 82 92.1 .20

Regular education mathematics standards for gifted students (e.g.,

district standards, Common Core Standards, unless they specify

acceleration or use of an above grade level use of standard,

assume they are using the regular education standards)

22 84.6 85 95.5 .18

More in-depth or greater breadth of coverage in grade-level

content in mathematics curriculum for gifted students (digging

deeper into the content, extended mathematics activities, not

covered in the standards)

14 53.9 72 80.9 .26

Preassessment of content knowledge and skills in mathematics

curriculum for gifted students (use informal or formal

assessment techniques, the use of curriculum compacting, may

be inferred as using preassessment)

14 57.7 57 64.0 .09

Above grade-level mathematics standards for gifted students

(choose standards/topics at higher grade level as the

mathematics focus)

15 57.7 53 59.6 .02

Extended or expanded grade-level mathematics standards for

gifted students (going beyond typical grade-level standards)

9 34.6 56 62.9 .24

Separate mathematics curriculum (purposely designed curriculum

for gifted students)

4 16.0 17 19.1 .03

Culturally responsive curriculum in mathematics (responsive to

students’ culture, language, expectations, experiences)

2 7.7 3 3.4 .09

Table 3. State 1: Prevalence of Services in Districts Not Using Domain-Specific Identification in Reading/ELA (n = 25) and Using

Domain-Specific Identification in Reading/ELA (n = 90).

Reading/ELA curricular content

No identification in ELA Identification in ELA

n % n %

Faster pace of coverage in the gifted reading/ELA curriculum

(acceleration, advanced content in shorter time frame, above

grade-level curriculum)

20 80.0 83 92.2 .16

Regular education reading/ELA standards for gifted students (e.g.,

district standards, Common Core Standards; unless they specify

acceleration or use of an above grade level use of standard,

assume they are using the regular education standards)

22 88.0 87 96.7 .16

More in-depth or greater breadth of coverage in grade-level content

in reading/ELA curriculum for gifted students (digging deeper into

the content, extended reading/ELA activities, not covered in the

standards)

14 56.0 73 81.1 .24

Preassessment of content knowledge and skills in reading/ELA

curriculum for gifted students (use informal or formal assessment

techniques, the use of curriculum compacting, may be inferred as

using preassessment)

12 48.0 59 65.6 .15

Above grade-level reading/ELA standards for gifted students (choose

standards/topics at higher grade level as the reading/ELA focus)

15 60.0 54 60.0 .00

Extended or expanded grade-level reading/ELA standards for gifted

students (going beyond typical grade-level standards)

9 36.0 58 64.4 .24

Separate reading/ELA curriculum (purposely designed curriculum for

gifted students)

7 28.0 33 36.7 .08

Culturally responsive curriculum in reading/ELA (responsive to

students’ culture, language, expectations, experiences)

2 8.0 4 4.4 .07

Note. ELA = English language arts.

124 Gifted Child Quarterly 65(2)

domain-specific identification (82.2%) than those that did

not (60%;  = .22; see Table 6). However, in State 2, cluster

grouping was far less common: Only 37% of the districts

using domain-specific identification, and 18.6% of those not

using domain-specific identification, reported using cluster

grouping ( = .17; see Table 7).

Push-in services and tiered instruction were less com-

monly used, especially in State 2. In State 1, under 50% of

the districts in each group reported using tiered instruction,

and 34.4% of districts with domain-specific identification

and 24% of districts without reported using push-in services,

in which gifted and talented specialists serve gifted and tal-

ented students in their classrooms rather than pulling them

out (see Table 6). In State 2, less than 20% of districts with

domain-specific identification and less than 10% of districts

without used either push-in services (16.3% vs. 4.7%) or

tiered (11.1% vs. 7%; see Table 7).

Pullout Instruction

State 1. In State 1, 71.1% of districts with math-specific

identification and 60% of districts without report using

some form of pullout programming for their gifted and

talented students. However, only 39.3% of districts iden-

tifying students in mathematics used pullout programs in

math and 40% of districts identifying students in reading/

ELA used pullout programs in reading/ELA. Over 32% of

districts with domain-specific identification delivered con-

tent other than reading/ELA or mathematics during part or

all of their pullout instruction. In other words, only 56%

of the districts identifying students as gifted and talented

in a domain and using pullout programming actually offer

pullout programming in the domain. In districts without

domain-specific identification, 15.4% reported using pull-

out instruction in mathematics and 40% covered content

other than reading/ELA or mathematics during the pullout

instruction.

State 2. In State 2, over 40% of the districts reported

using pullout programming. Regardless of whether they

identified students in mathematics or reading/ELA, with

very few districts indicating pullout instruction was focused

on mathematics or reading/ELA. Over 30% of the districts

reported using pullout programming in subject areas other

than mathematics or reading/ELA. In fact, of the 60 districts

reported using domain-specific identification and pullout

programming, only 19 districts (31.7%) reported deliver-

ing content in either mathematics or reading/ELA during the

pullout instruction.

Table 4. State 2: Prevalence of Services in Districts Not Using Domain-Specific Identification in Math (n = 43) and Using

Domain-Specific Identification in Math (n = 135).

Mathematics curricular content

No identification in math Identification in math

n % n %

Faster pace of coverage in the gifted mathematics curriculum

(acceleration, advanced content in shorter time frame,

above grade-level curriculum)

5 11.6 81 60.0 .41

Regular education mathematics standards for gifted students

(e.g., district standards, Common Core Standards; unless

they specify acceleration or use of an above grade level use

of standard, assume they are using the regular education

standards)

13 30.2 35 25.9 .04

More in-depth or greater breadth of coverage in grade-level

content in mathematics curriculum for gifted students

(digging deeper into the content, extended mathematics

activities, not covered in the standards)

13 30.2 52 38.5 .07

Preassessment of content knowledge and skills in

mathematics curriculum for gifted students (use informal

or formal assessment techniques; the use of curriculum

compacting, may be inferred as using preassessment)

19 44.2 51 37.8 .06

Above grade-level mathematics standards for gifted students

(choose standards/topics at higher grade level as the

mathematics focus)

1 2.3 31 23.0 .23

Extended or expanded grade-level mathematics standards

for gifted students (going beyond typical grade-level

standards)

10 23.3 24 17.8 .06

Separate mathematics curriculum (purposely designed

curriculum for gifted students)

1 2.3 6 4.4 .05

Culturally responsive curriculum in mathematics (responsive

to students’ culture, language, expectations, experiences)

0 0.0 0 0.0

Gubbins et al. 125

Content Standards

State 1. Tables 2 and 3 contain descriptive information on

content standards for State 1. Regardless of whether or not

they identified students as gifted and talented in mathemat-

ics, the majority of districts in State 1 reported using faster

pace of coverage in the mathematics curriculum (92.1%

vs. 76.9%), preassessments of content knowledge (64%

vs. 57.7%), and above grade-level mathematics standards

(59.6% vs. 57.7%). However, districts identifying students

as gifted and talented in mathematics were more likely to

Table 5. State 2: Prevalence of Services in Districts Not Using Domain-specific Identification in Reading/ELA (n = 43) and Using

Domain-specific Identification in Reading/ELA (n = 135).

Reading/ELA curricular content

Identification in ELA No identification in ELA

n % n %

Faster pace of coverage in the gifted reading/ELA curriculum

(acceleration, advanced content in shorter time frame,

above grade-level curriculum)

4 9.3 81 60.0 .43

Regular education reading/ELA standards for gifted students

(e.g., district standards, Common Core Standards; unless

they specify acceleration or use of an above grade level use

of standard, assume they are using the regular education

standards)

4 9.3 41 30.4 .21

More in-depth or greater breadth of coverage in grade-level

content in reading/ELA curriculum for gifted students

(digging deeper into the content, extended reading/ELA

activities, not covered in the standards)

5 11.6 51 37.8 .24

Preassessment of content knowledge and skills in reading/

ELA curriculum for gifted students (use informal or formal

assessment techniques; the use of curriculum compacting,

may be inferred as using preassessment)

19 44.2 51 37.8 .06

Above grade-level reading/ELA standards for gifted students

(choose standards/topics at higher grade level as the

reading/ELA focus)

1 2.3 30 22.2 .22

Extended or expanded grade-level reading/ELA standards for

gifted students (going beyond typical grade-level standards)

0 0.0 22 16.3 .21

Separate reading/ELA curriculum (purposely designed

curriculum for gifted students)

1 2.3 17 12.6 .15

Culturally responsive curriculum in reading/ELA (responsive

to students’ culture, language, expectations, experiences)

0 0.0 0 0.0 —

Note. ELA = English language arts.

Table 6. State 1: Prevalence of Learning Environments in Districts Not Using Domain-Specific Identification (n = 25) and Using

Domain-Specific Identification (n = 90).

Learning environments

No identification Identification

n % n %

Differentiation 23 92.0 90 100.0 .25

Cluster grouping 15 60.0 74 82.2 .22

Tiered instruction 10 40.0 44 48.9 .07

Push-in services 6 24.0 31 34.4 .09

Pullout (overall) 15 60.0 64 71.1 .10

Pullout services in ELA 4 16.0 36 40.0 .21

Pullout services in math

4 15.4 35 39.3 .21

Pullout services (Other) 10 40.0 29 32.2 .07

Note. ELA = English language arts.

Ninety districts used domain-specific identification in either ELA or mathematics. Of the districts, 89 of the 90 also used domain-specific identification

in mathematics. The table above compares districts using any domain-specific identification (n = 90) to those not using domain-specific identification (n

= 25), with the exception of pullout services in mathematics, where only the 89 districts with domain-specific identification in mathematics appear in the

identification column for that variable.

126 Gifted Child Quarterly 65(2)

report using more in-depth or greater breadth of coverage

(80.9% vs. 53.9%), and extended or expanded grade-level

standards (62.9% vs. 34.6%,  = .24).

In State 1, reading/ELA exhibited the same general pat-

tern. The majority of districts in State 1 reported using faster

pace of coverage in the reading/ELA curriculum (92.2% vs.

80.0%) and above grade-level reading/ELA standards (60%

vs. 60%). However, districts identifying students as gifted

and talented in reading/ELA were more likely to report using

more in-depth or greater breadth of coverage (81.1% vs.

56%,  = .24), and extended or expanded grade-level stan-

dards (64.4% vs. 36%,  = .24).

State 2. Tables 4 and 5 contain descriptive information

on content standards for State 2. In State 2, districts using

domain-specific identification were more likely to report

a faster pace of content coverage in both mathematics and

reading/ELA. Specifically, in districts using domain-specific

identification, 60% reported using faster pacing of mathemat-

ics content and 60% reported using faster pacing of reading/

ELA content. In contrast, in districts without domain-specific

identification, only 11.6% reported using faster pacing of

content in mathematics and only 9.3% reported using faster

pacing of content in reading/ELA ( = .41 for mathemat-

ics and  = .43 for reading/ELA). The majority of districts

in State 2 did not report using more in-depth coverage of

material, above grade-level standards, extended or expanded

standards, or preassessment, and there did not appear to be

meaningful differences in the usage of these three strategies

across districts using domain-specific identification versus

those not using domain-specific identification (see Tables 4

and 5). However, districts using domain-specific identifica-

tion were more likely to endorse the use of above grade-level

standards in both mathematics (23% vs. 2.3%,  = .23) and

in reading/ELA (22.2% vs. 2.3%,  = .22).

Discussion

Although a majority of districts indicated they identified stu-

dents as gifted in the domains of mathematics and reading/

ELA, we found limited use of a separate curriculum in math-

ematics and reading/ELA for students identified as gifted in

those domains. These findings violate the basic tenet of

gifted education: Identification of gifted and talented stu-

dents and services should be connected. Too often, however,

detailed screening, nomination, identification, and place-

ment practices are established without considering the ques-

tion: Identification for what? When state and district policy

makers set guidelines for identifying gifted and talented stu-

dents, they may fail to take into account the ramifications

their decisions have for the types of services students identi-

fied by those guidelines should receive. Therefore, gifted and

talented advocates should carefully consider the services

they hope to provide for students identified as gifted and tal-

ented before they establish identification criteria, and they

should ensure state and district identification recommenda-

tions take those services into account when setting identifi-

cation criteria. In many cases, this may require broader

conceptions of giftedness reflecting the variety of services

schools can provide.

At a time when most federally funded education research

emphasizes the importance of improving mathematics and

reading/ELA achievement, identifying students who would

most benefit from advanced content in those subjects is one

imperative. The results of this study indicated, at least in

terms of planning, that districts in the two states we exam-

ined did report they identified gifted and talented students

in the areas of mathematics and reading/ELA, and they dif-

ferentiated or extended the general curriculum in mathe-

matics or reading/ELA for gifted and talented students.

Differentiation was the primary learning environment men-

tioned by districts in both states. However, as we noted,

very few districts reported having designated curricula for

gifted and talented students in mathematics or reading/

ELA. Additionally, when districts reported having desig-

nated curricula, we found little difference in the availability

of curriculum designed specifically for gifted and talented

in mathematics or reading/ELA between districts identify-

ing/not identifying students in mathematics or reading/

ELA. These findings beg the questions worthy of additional

Table 7. State 2: Prevalence of Learning Environments in Districts Not Using Domain-Specific Identification (n = 43) and Using

Domain-Specific Identification (n = 135).

Learning environments

No identification in ELA Identification in ELA

n % n %

Differentiation 28 65.1 103 76.3 .11

Cluster grouping 8 18.6 50 37.0 .17

Tiered instruction 3 7.0 15 11.1 .06

Push-in services 2 4.7 22 16.3 .15

Pullout (overall) 18 41.9 60 44.4 .02

Pullout services in ELA 2 4.7 19 14.1 .13

Pullout services in math 2 4.7 18 13.3 .12

Pullout services (other) 16 37.2 42 31.1 .06

Note. ELA = English language arts.

Gubbins et al. 127

research, “How are educators differentiating without dedi-

cated related curricula?” and “When content related cur-

riculum is available, how do educators determine who

receives it?” In general, districts identifying in the subject

areas proposed utilizing faster pace of coverage as the pri-

mary method to meet the instruction needs for students

identified in both mathematics and reading/ELA, which

might reduce the pattern of not mentioning a designated

curriculum in the district gifted program plan.

Substantial differences did exist both between and within

states. Districts in State 1 were more likely to report cluster

grouping of identified students than districts in State 2. In

addition, districts identifying students in mathematics and

reading/ELA were more likely to report the use of cluster

grouping. Cluster grouping has been shown to be an effec-

tive strategy for delivering advanced content to identified

students (Gentry, 2014). However, we are still left to wonder

what differentiated learning occurs within those clusters.

This is also an area that future researchers will wish to

explore.

Districts identifying students as gifted and talented in

mathematics and reading/ELA in both states reported using

faster pacing. However, in State 1, districts not identifying

students as gifted and talented in mathematics and reading/

ELA, as well as those who identified students as gifted and

talented in those subjects, reported using faster pacing. In

State 2, districts identifying students in those subjects as

gifted and talented were more likely to provide faster pacing

than schools not identifying students in those subject areas.

Perhaps gifted and talented students in mathematics and

reading/ELA in both states are receiving advanced instruc-

tion in these subjects, but only students in State 2 carry the

gifted and talented label.

Borland (2005) has advocated for gifted and talented edu-

cation without gifted and talented identification. Given the

controversy of determining who is and who is not gifted and

talented, and the issues related to the under identification of

underserved populations, perhaps alignment between gifted

and talented students’ academic needs and services to meet

those needs might be better driven by identifying students

within the districts and schools rather than by a state policy

definition of giftedness.

The results from this study also appear to indicate push-in

services may be gaining traction. The push-in model, in par-

ticular, became popular in special education as a result of the

Individuals with Disabilities Education Act’s (2004) Least

Restrictive Environment requirement (McLeskey et al.,

2012). Push-in models favor an emphasis on differentiating

academics as gifted and talented services in the classroom,

which supports alignment of services for districts identifying

students in mathematics and reading/ELA.

As noted earlier, the results of this analysis of the connec-

tions between identification practices and intervention strate-

gies have policy implications. If identification data were

purposely collected to determine students’ domain-specific

gifts and talents, the plausible assumption is there should be

a match between identification and programming interven-

tions. As Peters et al. (2014) cautioned: “An identification

plan or policy cannot be developed in isolation from the pro-

gramming or curriculum that will be provided to those stu-

dents who are identified” (p. 22). Although the results of this

study are promising in a certain subset of districts (with

detailed state-level gifted and talented education policies), a

closer look at the match/mismatch between identification

and programming in other states with less favorable gifted

and talented education policies is warranted.

Limitations and Future Research

While a strength of the study is the availability of documents

from all districts in the states examined, thus removing sam-

pling issues (Frey, 2018) within the states, the data analyzed

in this study are limited to these two states. These two states

are geographically very far apart, represent a small sample,

and are states that met very stringent study criteria. Further

research is warranted to determine how generalizable the

results are to other states by conducting a study of district

gifted program plans for other states with identification and

programming mandates, even if the state candidates do not

meet specific criteria outlined for our research study.

Second, the data represent reports of practice as perceived

by district personnel (most likely at the central office level);

hence, we cannot extend our conclusions to practice. The

extent to which district gifted program plans are actually

applied in practice cannot be discerned from these data.

Future researchers should explore the actual alignment

occurring in practice. Because individual schools and teach-

ers vary across districts, implementation of district gifted

program plans likely fluctuates significantly across settings.

Schools and/or teachers may be implementing services not

reported in the district gifted program plans or may not be

implementing practices reported. We cannot document

whether the alignment between identification and service

model was provided. District gifted and talented coordina-

tors and administrators are generally responsible for devel-

oping and submitting district gifted program plans, while

building level administrators and teachers of gifted and tal-

ented students are responsible for implementing practices.

One potential limitation of document analyses, such as

those in this study with mandated reporting of practice

according to specific state guidelines, is the temptation of

the creators of the documents to either copy state guidelines

and regulations into the documents to ensure compliance or

to copy the products of other districts, which are not repre-

sentative of their own district gifted program plans (Frey,

2018). In our examination of the district gifted program

plans, we did not observe districts simply copying rules and

regulations of the state (which were available online) nor

did we see any instances of commonality suggesting dupli-

cation of others’ reports. In these two states, identification of

128 Gifted Child Quarterly 65(2)

giftedness in specific academic domains was allowed, but

not mandated, and no particular grouping or curricular

requirements were included in state law or policy.

Finally, although a district indicates it identifies students

as gifted and talented in mathematics or reading/ELA and

reports having a special curriculum for advanced students in

those subjects, we cannot definitely state the curriculum is

being used for those identified students.

Concluding Statement

If one of the goals of gifted and talented education is to

increase academic achievement in the core content areas of

mathematics and reading/ELA, then gifted and talented pro-

grams must identify students with strengths in these areas

and provide them with advanced content beyond what stu-

dents normally receive in their general education classrooms.

Our findings indicate districts are cognizant of this need to

match identification procedures with gifted and talented ser-

vices to maximize students’ academic growth, and many

incorporated this perspective in their district gifted program

plans. However, a nonnegligible number of districts do not

report using specific curriculum to meet advanced students’

needs. Additional research is warranted to determine the

extent to which districts actually implement what is reported

to the state.

Acknowledgments

The authors would to thank Mona Alimin and Maria El Abd for

their involvement with the calculations of interrater agreement

throughout the review of gifted and talented district program

plans.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect

to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support

for the research, authorship, and/or publication of this article: This

study was conducted by the National Center for Research on Gifted

Education (http://ncrge.uconn.edu), which is funded by the Institute

of Education Sciences, U.S. Department of Education PR/Award

No. R305C140018.

Open Science Disclosure Statement

The data analyzed in this study are not available for purposes of

reproducing the results. The protocol used to generate the findings

reported in the article is available at https://ncrge.uconn.edu/

Program_Plan_Codes/ for purposes of replicating the study. There

were no newly created, unique materials used to conduct this research.

ORCID iDs

E. Jean Gubbins https://orcid.org/0000-0002-8957-0637

Del Siegle

https://orcid.org/0000-0001-5579-9217

Carolyn M. Callahan

https://orcid.org/0000-0001-5056-1357

Annalissa V. Brodersen

https://orcid.org/0000-0001-6618-0612

Notes

1. In the interest of ensuring openness in the review of literature

and subsequent analysis, our bias in accepting this premise

should be noted (Caelli et al., 2003).

2. The third state was not included in this analysis stage because

districts reported only their identification process in the docu-

ments examined, and those processes were largely identical in

wording to the state law governing identification.

3. The actual coding scheme is too long for inclusion in this

article. It is posted on our website (https://ncrge.uconn.edu/

Program_Plan_Codes/).

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Author Biographies

E. Jean Gubbins, PhD, is a professor of educational psychology at

the University of Connecticut and an associate director of the

National Center for Research on Gifted Education. She teaches

graduate courses in gifted education and talent development related

to identification, programming, curriculum development, and pro-

gram evaluation. Through grant funding from the U.S. Department

of Education, she implemented research studies on curricular strate-

gies and practices in science, technology, engineering, and mathe-

matics (STEM) high schools, reading and mathematics education,

professional development, identification and programming, and

gifted education pedagogy for all students.

Del Siegle, PhD, is Director of the National Center for Research on

Gifted Education and the Renzulli Center for Creativity, Gifted

Education, and Talent Development. He is a past-president of the

National Association for Gifted Children (NAGC) and past coeditor

of Gifted Child Quarterly. He was a recipient of the 2018 NAGC

Distinguished Scholar Award and the 2011 NAGC Distinguished

Service Award. He holds the Lynn and Ray Neag Endowed Chair

for Talent Development at the University of Connecticut.

Karen Ottone-Cross, PhD, is an assistant professor and Program

Coordinator of School Psychology at California Baptist University

in Riverside, California. Having recently completed her diplomate

in School Neuropsychology, she conducts assessments and pro-

vides counseling as a psychological assistant at Psychological

Services of Riverside. While at California Baptist University, she

redesigned the program to meet national accreditation standards.

She is currently receiving training in Parent Child and Teacher

Child Interaction Therapy in the hopes of developing programming

that decreases behaviorally based referrals to special education. Her

research interests include gifted education, twice-exceptionality,

early behavior interventions, and the effects of adverse childhood

experiences on learning. She completed her doctorate in Giftedness,

Creativity, and Talent Development and School Psychology from

the University of Connecticut in 2018.

D. Betsy McCoach, PhD, is professor of research methods, mea-

surement, and evaluation in the Educational Psychology Department

at the University of Connecticut. She is co–principal investigator

for the National Center for Research on Gifted Education and has

served as principal investigator, co–principal investigator, and/or

research methodologist for several other federally funded research

projects/grants. Her research interests include latent variable

modeling, multilevel modeling, longitudinal modeling, instrument

design, and gifted education.

Susan Dulong Langley, PhD, is a postdoctoral research associate

at the University of Connecticut for Project BUMP UP. She is a

former teacher of the gifted in Massachusetts. She served as presi-

dent of the Massachusetts Association for Gifted Education and as

the parent representative and governance secretary for the National

Association for Gifted Children. Her research interests include

identification, services, and program retention for culturally and

linguistically diverse gifted learners.

Carolyn M. Callahan, PhD, Commonwealth Professor of

Education Emeritus at the University of Virginia has been principal

investigator on projects of the National Center for Research on

Gifted Education (formerly NRC/GT), and principal investigator on

five Javits grants including Project PLACE, focusing on the identi-

fication and provision of services to rural gifted students. Her work

with the NRC/GT and with the Javits projects has focused on cur-

riculum development and implementation. She has been recognized

as Outstanding Professor of the Commonwealth of Virginia and

Distinguished Scholar of the National Association for Gifted

Children (NAGC) and has served as president of the NAGC and the

Association for the Gifted, and as editor of Gifted Child Quarterly.

She has published over 250 articles and 50 book chapters. She is the

coeditor of the recently published book Fundamentals of Gifted

Education: Considering Multiple Perspectives and Critical Issues

in Gifted Education.

Annalissa V. Brodersen, PhD, is an education consultant and former

research associate with the National Center for Research on Gifted

Education and Project PLACE at the University of Virginia. Her

research expertise is in gifted education policies and practices, experi-

ences of teachers and students in high-poverty, rural schools, and the

intersections between preK-12 gifted education and higher education.

Melanie Caughey, PhD, is the visiting assistant professor for gifted

and talented education and the coordinator of the gifted education

program at Cleveland State University. She received her doctorate

from the University of Virginia, where she worked as a graduate

research assistant and served as the academic coordinator for the

Saturday and Summer Enrichment Program. Her research interests

include advanced secondary coursework and fidelity of implemen-

tation of curriculum.

Manuscript received: December 20, 2019; Final revision received:

November 9, 2020; Accepted: December 21, 2020