Grouping The Gifted: Yearly Academic Growth in Homogeneous and Heterogeneous Grade 3 Reading

GROUPING THE GIFTED: YEARLY ACADEMIC GROWTH IN HOMOGENEOUS AND HETEROGENEOUS GRADE 3 READING by Kathryn Pabst Schaeffer DR. CAROLYN PRICE, Ph.D., Faculty Chair _________________________ Committee Member DR. SHARON LEE, Ph.D., Director of Research in K-12 Education Neil Dugger, Ed.D. Director of Ed.D. in Educational Leadership K-12 Dean, Dorothy M. Bush College of Education A Treatise Presented in Partial Fulfillment Of the Requirements for the Degree Doctor of Education in Educational Leadership K-12 Dallas Baptist University June 2015 Kathryn Pabst Schaeffer, 2015 We hereby recommend that the submitted Treatise Title: Grouping the gifted: Yearly academic growth in homogeneous and heterogeneous grade 3 reading By: Kathryn Pabst Schaeffer Be accepted in partial fulfillment of the requirements for the Degree of: Doctor of Education in Educational Leadership Ed.D. Program Director Date Dissertation Committee Dr. Carolyn Price, Ph.D., Committee Chair Date (Full Name and Title) Committee Member Date Dr. Sharon Lee, Ph.D. Director of Research in K-12 Education Date Dean, Dorothy M. Bush College of Education Neil Dugger, Ed.D. Date Abstract The researcher investigated differences in yearly academic growth rates in Grade 3 reading for gifted students in two different grouping programs in the participating school district. Gifted yearly academic growth rates were examined for a highly gifted homogeneous and a gifted and moderately gifted cluster grouped program using yearly progress measures from students’ Measures of Academic Progress (MAP) scores. The secondary data measures formative progress to better determine academic growth due to instructional effectiveness. The sample consisted of 19 highly gifted at a single elementary campus and 138 gifted to moderately gifted children at 23 elementary campuses throughout a single suburban school district. Texas accountability now reports annual growth rates for all students, including gifted and talented students based on grade level state assessments. The new state incentive to focus on annual growth for gifted students served as the impetus for this current study. There was an absence of research on evaluating gifted grouping practices for annual growth. Annual growth for gifted students might best be measured as a formative progress measure adaptive beyond grade level rather than a grade level state assessment achievement data point. Grade level achievement ceilings do not measure the incremental growth of already high achieving students. Keywords: gifted, grouping, differentiation, annual growth, reading iv Dedication I would like to dedicate this work to my parents who have always remained by my side to give me confidence and hope for a fruitful future. A special thank you for my sister, Anne, for without her I would have been lost and never begun this journey. My four sons: Douglas, Karl, Brian, and Jeffrey have sustained me as I do them throughout life’s adventures. I remain true to these words of wisdom: “Take your roots with you if you ever have to leave. Transplant carefully, just like a tree. Dig a new hole. Water it well.” Kathryn Pabst Schaeffer – TAKEN: A Lament for a Lost Ethnicity v Acknowledgments (Acknowledgments entry does appear in the Table of Contents.) vi Table of Contents Page List of Tables ......................................................................................................... xi List of Figures ....................................................................................................... xii CHAPTER 1. INTRODUCTION ....................................................................................... 1 Introduction to the Problem .................................................................................... 1 Background of the Study ........................................................................................ 2 Statement of the Problem ........................................................................................ 3 Purpose of the Study ............................................................................................... 4 Rationale ................................................................................................................. 5 Research Questions and Null Hypotheses .............................................................. 9 Research Question 1 (RQ1) ........................................................................ 9 Research Question 2 (RQ2) ...................................................................... 10 Significance of the Study ...................................................................................... 10 Definition of Terms............................................................................................... 11 Ability Grouping ....................................................................................... 11 Acceleration .............................................................................................. 11 Cognitive Growth...................................................................................... 11 Affective Growth ...................................................................................... 11 Cluster Grouping ....................................................................................... 12 Curriculum Compacting............................................................................ 12 Differentiation ........................................................................................... 12 Differentiated Curriculum ......................................................................... 12 Differentiated Instruction .......................................................................... 12 vii Enrichment ................................................................................................ 13 Flexible Grouping ..................................................................................... 13 Gifted and Talented Students .................................................................... 13 Gifted Identification .................................................................................. 13 Heterogeneous Grouping .......................................................................... 13 Homogeneous Grouping ........................................................................... 14 Identification ............................................................................................. 14 Norm-referenced Testing .......................................................................... 14 Off- or Above-grade Level ....................................................................... 14 Pull-Out Program ...................................................................................... 15 Social-Emotional Needs............................................................................ 15 Talent Development .................................................................................. 15 Underachieving/Underachievement .......................................................... 15 Assumptions and Limitations ............................................................................... 15 Nature of the Study ............................................................................................... 16 Organization of the Remainder of the Study ........................................................ 16 CHAPTER 2. LITERATURE REVIEW .......................................................................... 18 Gifted Programming Standards: Best Practices .................................................... 18 Background and Current Gifted Common Ground ................................... 18 National Association for Gifted Children’s Plan ...................................... 21 Texas State Plan for the Education of Gifted and Talented Students ....... 22 Gifted Curriculum: Models for Gifted Students ................................................... 23 Historical Perspective of Gifted Program and Curriculum Models .......... 25 Acceleration .............................................................................................. 28 viii Gifted Grouping Practices for Academic Growth ................................................ 30 Criticism of Grouping Gifted Students ..................................................... 31 Research: Informing Appropriate Grouping of Gifted and Talented Students ..................................................................................................... 33 Homogeneous Grouping ........................................................................... 35 Heterogeneous Grouping .......................................................................... 49 Discussion of Recent Trends ................................................................................ 66 Assessing Gifted Students’ Growth with MAP .................................................... 68 Summary of Literature Review ............................................................................. 76 Rationale for Action Research .............................................................................. 77 CHAPTER 3. METHODOOLOGY ................................................................................. 79 Introduction ........................................................................................................... 79 Research Question 1 (RQ 1) ..................................................................... 79 Research Question 2 (RQ2) ...................................................................... 79 Research Design.................................................................................................... 80 Setting ................................................................................................................... 80 Target Population and Sample .............................................................................. 81 Instrumentation and Measures .............................................................................. 81 Data Collection ..................................................................................................... 85 Procedures and Data Analysis .............................................................................. 86 Limitations ............................................................................................................ 88 Ethical Considerations .......................................................................................... 89 CHAPTER 4. RESULTS .................................................................................................. 90 Description of the Variables ................................................................................. 90 ix Results for Research Question 1 ........................................................................... 91 Results for Research Question 2 ........................................................................... 95 Summary ............................................................................................................... 98 CHAPTER 5. DISCUSSION, IMPLICATIONS, RECOMMENDATIONS ................. 100 Introduction ......................................................................................................... 100 Summary of Study .............................................................................................. 100 Research Question 1 (RQ1) .................................................................... 101 Research Question 2 (RQ2) .................................................................... 102 Summary of Findings and Interpretation of Results ........................................... 102 Implications......................................................................................................... 104 Limitations .......................................................................................................... 112 Recommendations ............................................................................................... 114 Conclusions ......................................................................................................... 115 REFERENCES ............................................................................................................... 118 x List of Tables Table 1. Guidelines Describing Giftedness Based on IQ Scores .................................... 19 Table 2. Gifted Curriculum Design Models ................................................................... 26 Table 3. Prevalence of Gifts Program Delivery Models ................................................. 31 Table 4. Delivery Models’ Strengths and Weaknesses ................................................... 32 Table 5. Academic Effect Sizes of Program Options for Gifted Students...................... 36 Table 6. Descriptive Statistics for the Sample of 157 Gifted and Talented Students ..... 92 Table 7. CGI Descriptive Statistics for ACE and LEAP ................................................ 94 Table 8. RIT Score Descriptive Statistics by ACE Versus LEAP .................................. 94 Table 9. Independent Samples Test for Hypothesis 1..................................................... 95 Table 10. Descriptive Statistics by ACE Campus ............................................................ 97 Table 11. ANOVA Results for Hypothesis 2 Test Between the 223 ACE Campuses ..... 98 xi List of Figures Figure 1. Histogram of CGI values for the Gifted and Talented Student Sample. .......... 93 Figure 2. Comparison of the data points for RIT scores between fall and spring by ACE and LEAP groupings. ......................................................................... 96 xii CHAPTER 1. INTRODUCTION Introduction to the Problem Academic content acceleration and curriculum enrichment for gifted and talented students are strategies to address the needs of exceptional ability and high achieving students. However, debate continues between proponents of homogeneous programming for students identified for gifted services and proponents of de-tracking. Heterogeneous grouping relies on individualized or group differentiation, if cluster grouped, with enrichment opportunities provided within general education classrooms. Research results have been inconsistent for identifying a single grouping model offering the best instructional setting for academic growth for gifted students. National standards for gifted education offer no guidance regarding the design of appropriate and consistent grouping to best meet the needs of exceptional students. States such as Texas offer gifted and talented education standards but also leave program designs, including grouping options, up to individual school districts. Recently, some states, including Texas, have added yearly academic gains to their accountability measures. Yearly academic gains refers to a measure of academic growth. Achievement tends to be measured as a summative one-time event on an annual basis, while a growth measure is used periodically within a single academic year or across academic years to measure progress at a formative level between each repetition of measurement (Teach for America, 2011). Those yearly academic gains include gifted and talented students. Because of Texas’ recent yearly academic gain accountability requirements for all 1 children, research was needed to identify whether homogeneous grouping or heterogeneous grouping offers the highest yearly academic gains for gifted and talented students. The data were best measured with an instrument that was adaptive to above grade level for high achieving students who may not show growth with a criterion referenced grade level assessment. The current study was an action research project conducted to add to the body of research on this topic. Background of the Study Gifted students have traditionally been high academic achievers (Colangelo, Assouline, & Gross, 2004). In the era of No Child Left Behind, little attention has been paid to the academic growth of the highest achieving students (Xiang, Dahlin, Cronin, Theaker, & Durant, 2011). Funding resources have largely been designated for struggling students given the accountability requirements for ensuring all students meet basic state assessments (Duffett, Farkas, & Loveless, 2008). However, the 2014-2015 Texas state educational accountability reports was focused on yearly academic gain measures of all students including gifted and talented students. Thus, designing gifted and talented programming to maximize continuous academic growth is, for the first time, essential to Texas school districts’ overall accountability ratings. At the time of the current study, the participating school district served its gifted and talented student population through two different program models known as Leading Exceptional Aptitude and Performance (LEAP) for highly gifted students and Advancing Creativity in Education (ACE) for gifted and moderately gifted students. The district’s goal was to offer the optimal learning environment for highly gifted students in homogeneous classes through LEAP and cluster grouping in general education classrooms for gifted and moderately gifted students in its ACE program. This research 2 project, by virtue of the participating school district’s unique commitment to two different programs to meet the needs of all its gifted and talented students, offered a research opportunity for evaluating formative progress as an annual growth measure in Grade 3 reading. The results might help with assessing the academic effectiveness of each program based on differences in grouping. Gifted students, by virtue of their early reading ability, might already enter Grade 3 with high achievement. Because high achieving students lack an appropriate measure for their reading growth on an annual criterion referenced summative achievement assessment, an instrument that provided a formative progress growth measure allowed for better addressing the nuanced growth of high achieving gifted students (Duffet et al., 2008). Using a grade level summative achievement measure for the research project would have revealed less about the impact of academic growth in the educational setting for students who often perform above grade level (NAGC, 2009). The current study used data from two separate district programs that both served gifted students but differently based on the gifted students’ positions along the intelligence continuum. The current study’s action research purpose enabled the researcher to ascertain formative progress as a result of the programming model and not achievement. To evaluate the two different programs, the growth measure captured the cumulative impact of the educational setting on students. Statement of the Problem Research focused on yearly academic growth measures for gifted students was needed to evaluate programming design options. This in turn might inform districts about resource allocation for serving gifted students. Particularly in Texas, yearly progress measures for all students, in addition to required passing rates on state assessments, have 3 become part of the accountability rating system. This new state-mandated measure offered gifted educators an opportunity to evaluate programming effectiveness. Reading is an essential skill in early elementary and a determiner of future educational success for all students, including gifted and talented students (Hernandez, 2010, p 1). Providing data on the yearly academic growth of Grade 3 gifted and talented students participating in two grouping programs within the same school district might indicate which grouping practices provides gifted and talented students with the greater opportunity for reading growth. Homogeneously grouped highly gifted students might experience similar or different yearly academic growth rates in Grade 3 reading than gifted and moderately gifted students enrolled in a cluster group programming model when assessed by the measure known as Measures of Academic Progress (MAP) over the course of an academic year. The yearly academic gains of gifted students needed to be measured with an appropriate instrument that was adaptive to above grade level achievement and measured formatively over time. Purpose of the Study The purpose of the current study as a quantitative action research project was to examine differences. The research problem was that the participating school district did not know whether the homogeneous or heterogeneous gifted programming model offered their gifted students the strongest opportunity for yearly academic growth. In order to answer this problem, this research project was focused on gain scores for students in the two different gifted programs. The results might help determine the best grouping option for academic development of the gifted for the participating school district. The participating school district’s gifted and talented students participated in one of two programming models using homogeneous or heterogeneous classroom 4 assignments. The differences were tested using the gifted and talented students’ Grade 3 reading MAP scores from the 2013-2014 academic school year. Gifted and talented students either experienced homogenous programming with accelerated reading instruction or cluster grouped heterogeneous classrooms with enriched reading instruction in the participating school district’s LEAP and ACE programs. The current study furthered the research of grouping effectiveness for gifted and talented students evaluated by yearly academic growth. Rationale Current gifted programming research studies have not linked differences in yearly academic gains to different grouping practices. The current study as an action research project addressed grouping as the variable and involved two gifted and talented student groups’ yearly gains on the same assessment measure in the same grade and the same district. Rarely does a single district have two distinct programming options for students along the gifted and talented continuum. Looking at the Grade 3 reading formative growth rates for homogeneously grouped highly gifted students and for heterogeneously cluster grouped moderately gifted students allowed for a closer evaluation of differences in annual academic growth based on programming model. Hernandez (2011), supported by The Annie E. Casey Foundation and the Center for Demographic Analysis, studied the correlation of Grade 3 reading to high school graduation. “Students who fail to reach the critical milestone of mastering reading by the end of third grade often falter in the later grades” (Hernandez, 2011, p. 3). In the longitudinal study, Hernandez found that “one in six children who are not reading proficiently in third grade do not graduate from high school on time, a rate four times 5 greater than that for proficient readers” (p. 3). The data, while not specific to gifted students, informed the same need for continuous reading growth for all students. Hernandez (2011) conducted a study borne out of the importance of early reading skills documented in the No Child Left Behind Act. Since the enactment of NCLB, each state is required to test reading skills, beginning in Grade 3, and for each year for every student. The particular significance of Grade 3 reading is acknowledged because it is the time when a child “shifts from learning to read and begins reading to learn” (Hernandez, 2011, p. 4). Non-proficient readers, after Grade 3, encounter difficulty in learning in all content areas because of the comprehension gap that persists over time (Chapin Hall at the University of Chicago, 2012). Comprehending written material is critical to learning for every grade following Grade 3 (Chapin Hall at the University of Chicago, 2012). Grade 3 is pivotal in a child’s education because it is the year that simply decoding words transfers to informational texts (Wennersten, 2012). That shift never abates, but is accelerated as the child is promoted through each grade. Gifted students who experience either accelerated or enriched courses can only reach their academic potential with continuous reading improvement even if that extends well beyond grade level. “Slow reading acquisition has cognitive, behavioral, and motivational consequences that slows the development of other cognitive skills and inhibits performance on all academic tasks” (Wennersten, 2012, p. 1). The connection between reading proficiency and later academic success is so critical that in 2010, the Obama Administration reinforced “putting reading first” and increased the federal funding for early reading instruction and intervention for all students (Hernandez, 2011, p. 4). Minority children are even more at risk if they fall behind in reading proficiency by Grade 3. Black and Hispanic students who are non-proficient in Grade 3 reading are 6 “11 to 12 percentage points less likely to graduate from high school than White students with similar reading skills” (Hernandez, 2011, p. 4). This disparity in demographics persists even among proficient Grade 3 readers. Four percent of White students who do read with proficiency in Grade 3 fail to graduate from high school. Black students who are proficient in Grade 3 reading fail to graduate at a rate of 6% and Hispanics at 9% (Hernandez, 2011). Minority students may be less likely to be identified as gifted due to language and reading deficiencies. An update to Hernandez’s research (2011) was published by Fiester (2013) also under the sponsorship of the Annie E. Casey Foundation. The findings once more found that if students, especially students from low income families, are proficient readers by Grade 3, they are more on target to graduate from high school and be college and career ready. Fiester (2013) emphasized that her research not only confirmed the results of the earlier study, but promoted a “heightened sense of urgency around Grade 3 reading proficiency” (p. 2). In this follow-up study, Fiester drew links between lack of proficient reading in Grade 3 to “ongoing academic difficulties in school, failure to graduate from high school on time, chances of succeeding economically later in life—including an individuals’ ability to break the cycle of intergenerational poverty and the country’s ability to ensure global competitiveness, general productivity, and national security” (p. 3). An additional finding by Feister was that beyond Grade 3, a student who is not at grade level for reading does not catch up. Rather, they fall further and further behind. Feister’s conclusion encourages all educators to focus on Grade 3 reading growth because of its strong predictive measure for later academic success. The importance of early reading to a child’s academic outcome extends even to lifetime earnings. “Educational attainment and cognitive skills become more predictive of 7 adults’ earnings” with the strongest predictive data measure from Grade 3 reading (Fiester, 2013, p. 7). Fiester (2013) concluded that Grade 3 reading is the critical factor needed to solve our nation’s income inequality. Teachers of Grade 3 gifted reading need to approach growth in reading proficiency with the same urgency as general education students. Researchers at Chapin Hall at the University of Chicago used longitudinal data to examine the relationship between Grade 3 reading and educational outcomes much like the previous two studies. The Chapin Hall university study’s researchers used Grade 3 data from the Iowa Tests of Basic Skills (ITBS) with 26,000 Chicago Public School (CPS) students in three cohorts: those reading below grade level (0 to 24th national percentile), those reading at grade level (25th to 74th national percentile), and those reading above grade level (75th to 100th national percentile) in Grade 3 (Chapin Hall at the University of Chicago, 2012). The Chapin Hall researchers concluded that those students at or above grade level reading in Grade 3 attended college at higher rates than those who were below grade level. Only 20% of above-grade level readers in Grade 3 failed to graduate from high school with 38% failing to do so at-grade level and 55% at below grade level (Chapin Hall at the University of Chicago, 2012). In addition, the Chapin Hall researchers reported that Grade 3 reading is also a “significant predictor of Grade 8 reading achievement, which is a significant predictor of Grade 9 course performance, which ultimately predicts high school graduation and college attendance” (p. 2). The cumulative effect of Grade 3 reading proficiency is correlated to the success and final outcome of a child’s academic career. “Experts call Grade 3 a critical turning point for learning” for all students (Lu, 2013, p. 1). 8 Consequently, based on both federal and state mandates for reading proficiency in Grade 3 reading and the landmark studies linking Grade 3 reading proficiency to a student’s academic and lifetime outcomes, the current study was focused exclusively on Grade 3 reading as the pivotal measure for future academic outcomes. While Hernandez (2011) and Fiester (2013) did not disaggregate data for gifted students, Grade 3 reading remains a highly scrutinized data point with implications for future student success. Gifted students are not exempt from the research findings. Like all students, progress and growth in Grade 3 reading is critical for gifted students’ ability to proceed through their academic career to reach their cognitive potential. Research Questions and Null Hypotheses Two research questions were answered in the current study. The hypotheses H10, H1, H20, and H2 were tested as part of answering the two RQs in the current study. Research Question 1 (RQ1) Do Grade 3 gifted students enrolled in the homogenous elementary LEAP reading program show different yearly academic growth, the dependent variable, measured by the MAP reading assessment than ACE gifted students housed in 23 separate elementary schools in a heterogeneous cluster model that relies on classroom enrichment? H10: Grade 3 gifted students enrolled in the homogeneous elementary LEAP reading program show no differences in yearly academic growth measured by MAP reading assessment than ACE gifted student’s housed in 23 separate elementary schools in a heterogeneous cluster model that relies on classroom enrichment, μLEAP = μACE. H1: Grade 3 gifted students enrolled in the homogeneous elementary LEAP reading program show differences in yearly academic growth measured by 9 MAP reading assessment than ACE gifted student’s housed in 23 separate elementary schools in a heterogeneous cluster model that relies on classroom enrichment, μLEAP ≠ μACE. Research Question 2 (RQ2) Do Grade 3 ACE program gifted students enrolled in 23 elementary schools show different growth rates, the dependent variable, from the fall to spring MAP scores by elementary school? H20: Grade 3 ACE program gifted students enrolled in 23 elementary schools show no differences in growth rates from the fall to spring MAP scores by elementary school, μ1 = μ2 = . . . . = μ23. H2: Grade 3 ACE program gifted students enrolled in 23 elementary schools show differences in growth rates from the fall to spring MAP scores by elementary school, μ1 ≠ μ2 ≠ . . . . ≠ μ23. Significance of the Study Major stakeholders of the current study include gifted students in the participating school district, the district’s gifted teachers, the district administration, Texas state gifted administrators, Texas state gifted students, national gifted administrators, and national gifted students. Programming for gifted students in the participating school district was reviewed to determine best grouping practices for yearly student progress within two distinct programs: LEAP (highly gifted) and ACE (gifted to moderately gifted). The current research findings might better inform districts about the academic impact of homogenous and heterogeneous cluster grouping for the gifted. Educational leaders might use the current study’s action research findings to support and evaluate their own 10 programming design decisions for gifted and talented students with new accountability requirements for academic gains for all students in mind. Definition of Terms The following terms were used as part of the current study conducted as a quantitative action research project. Definitions for terminology were those used by The National Association for Gifted Children (NAGC). Since the stakeholders for the current study extended beyond the participating school district and the state of Texas, a national source was desired. NAGC was considered the clearinghouse for research for the gifted and their definitions were those also referenced by the state of Texas. A single source also offered the continuity of interpretation desired for the current study especially since gifted education lacks direct federal or state definition of terms. Ability Grouping When students of a similar ability or achievement level are placed in a class or group based on observed behavior or performance. Ability grouping is not the same as tracking (National Association for Gifted Children [NAGC], 2010, para.1). Acceleration A strategy of progressing through education at rates faster or ages younger than the norm. This can occur through grade skipping or subject acceleration (NAGC, 2010, para.2). Cognitive Growth Cognitive growth refers to the development of concepts and thinking skills (NAGC, 2010, para. 5). Affective Growth Affective growth relates to the development of social-emotional needs (NAGC, 11 2010, para. 6). Cluster Grouping A grouping assignment for gifted students in the regular heterogeneous classroom. Typically, five or six gifted students with similar needs, abilities, or interests are clustered in the same classroom, which allows the teacher to more efficiently differentiate assignments for a group of advanced learners rather than just one or two students (NAGC, 2010, para. 14). Curriculum Compacting An instructional technique that allows teachers to adjust curriculum for students by determining which students already have mastered most or all of the learning outcomes and providing replacement instruction or activities that enable a more challenging and productive use of the student’s time (NAGC, 2010, para. 20). Differentiation Modifying curriculum and instruction according to content, pacing, and/or product to meet unique student needs in the classroom (NAGC, 2010, para. 21). Differentiated Curriculum Adaptation of content, process, and concepts to meet a higher level of expectation appropriate for advanced learners. Curriculum can be differentiated through acceleration, complexity, depth, challenge, and creativity (NAGC, 2010, para. 21). Differentiated Instruction Multiple ways to structure a lesson so that each student is challenged at an appropriate level. Differentiated instruction may include such features as learner 12 centeredness; planned assignments and lessons based on pre-assessment; and flexible grouping, materials, resources, and pacing (NAGC, 2010, para. 21). Enrichment Activities that add or go beyond the existing curriculum. They may occur in the classroom or in a separate setting such as a pull-out program (NAGC, 2010, para. 24). Flexible Grouping An instructional strategy where students are grouped together to receive appropriately challenging instruction. True flexible grouping permits students to move in and out of various grouping patterns, depending on the course content. Grouping can be determined by ability, size, and/or interest (NAGC, 2010, para. 25). Gifted and Talented Students Students, children, or youth who give evidence of high achievement capability in areas such as intellectual, creative, artistic, or leadership capacity, or in specific academic fields, and who need services and activities not ordinarily provided by the school in order to fully develop those capabilities (NAGC, 2010, para. 26). Gifted Identification In the participating school district, Gifted is operationalized as an Intelligence Quotient (IQ) of 125 to 129; Moderately Gifted as an IQ of 130 to 139; Highly Gifted as an IQ of 140 to 144; and Profoundly Gifted as an IQ of 145 and higher ("What is Gifted," 2014). Heterogeneous Grouping Grouping students by mixed ability or readiness levels. A heterogeneous classroom is one in which a teacher is expected to meet a broad range of student needs or 13 readiness levels. Also referred to as inclusion or inclusive classrooms (NAGC, 2010, para. 27). Homogeneous Grouping Grouping students by need, ability, or interest. Although variations between students exist in a homogeneous classroom, the intent of this grouping pattern is to restrict the range of student readiness or needs that a teacher must address (NAGC, 2010, para. 28). Identification The process of determining students qualified for gifted or advanced programming, identification most commonly occurs through the use of intelligence or other testing. Many researchers place emphasis on using multiple pathways for identification, adding teacher, parent, or peer nominations or authentic assessments such as portfolios of student work to the process (NAGC, 2010, para. 29). Norm-referenced Testing An assessment that compares an individual’s results with a large group of individuals who have taken the same assessment and are referred to as the “norming group” (NAGC, 2010, para. 40). Off- or Above-grade Level Tests normed for students at a higher grade level than the students who are being tested. Used to provide an accurate picture of the relative ability level of students whose abilities exceed those that can be measured using on-grade level instruments (NAGC, 2010, para. 40). 14 Pull-Out Program A program that takes a student out of the regular classroom during the school day for special programming (NAGC, 2010). Social-Emotional Needs Gifted and talented students may have affective needs that include heightened or unusual sensitivity to self-awareness, emotions, and expectations of themselves or others, and a sense of justice, moral judgment, or altruism. Counselors working in this area may address issues such as perfectionism, depression, low self-concept, bullying, or under achievement (NAGC, 2010, para. 44). Talent Development Programs, curricula, and services for gifted and talented students that can best meet their needs, promote their achievements in life, and contribute to the enhancement of our society when schools identify students’ specific talent strengths and focus educational services on these talents (NAGC, 2010, para. 50). Underachieving/Underachievement A term used to describe the discrepancy between a student’s performance and his or her potential or ability to perform at a much higher level (NAGC, 2010, para. 53). Assumptions and Limitations In the current study, it was assumed that ACE and LEAP students have equal learning opportunities and effective curricula. The MAP was assumed to be a valid reading assessment. All MAP data from Northwest Evaluation Association (NWEA) and the participating school district were assumed to be accurate. Some limitations also exist. The current study’s findings might not be representative of other districts and might not be generalizable. Gifted and talented 15 students are uniquely identified and served by each district according to its policies and procedures. Student demographics might limit the current study’s findings as the participating school district was suburban and served a high number of low socioeconomic status students as well as a large minority population. Finally, the results were limited by the number of LEAP students being 19 at one elementary representing a small sample and the larger sample of 138 heterogeneously grouped ACE students representing 23 elementary schools. Nature of the Study The current study was a causal-comparative quantitative action research targeting gifted and talented students based on homogeneous versus heterogeneous classroom groupings. The current study was conducted to determine cause and effect between the grouping of gifted students and their yearly growth progress in Grade 3 reading. This cause and effect had already occurred and was examined after the fact. Non-gifted students were not included in the current study. The secondary data were Grade 3 gifted students’ MAP reading scores for the 2013-2014 academic school year. Grade 3 reading MAP scores were used by the participating school district because previous research has shown Grade 3 to be the pivotal year in predicting future student academic success in school. The secondary data was used to fulfill the design of the current study and was limited to one academic year in one district. However, the available data represented the structure of the gifted program and the issue of measuring yearly academic growth at the participating school district most appropriately. Organization of the Remainder of the Study This chapter has introduced the quantitative research project designed to measure the differences in formative progress in Grade 3 reading for gifted students between two 16 different grouping programs: homogeneous versus heterogeneous cluster grouped classrooms based on MAP scores from the 2013-2014 academic school year. The second chapter provides the literature review. The third chapter displays the methods and procedures for data analysis. The fourth chapter presents the results. Finally, the fifth chapter concludes the current study with a discussion of the findings. 17 CHAPTER 2. LITERATURE REVIEW The purpose of the current research project was to examine academic growth differences by grouping of gifted and talented students. The gifted and talented students participated in one of two programming models. In the participating school district, highly gifted students were served through homogeneous classes whereas gifted and moderately gifted students were served through cluster grouped heterogeneous classroom assignments. The differences were tested using the gifted and talented students’ formative progress on Grade 3 reading Measures of Academic Progress (MAP) scores from the 2013-2014 academic school year. This review explores the relevant literature on gifted program standards, program models for gifted education, grouping practices for academic growth, and the practice of differentiation as a strategy for serving gifted students in the general education classroom. Gifted Programming Standards: Best Practices This section of the review addresses the background and features of current gifted programs. It also provides context at the national and state levels. Background and Current Gifted Common Ground Giftedness, one quality considered pervasive within an individual for a lifetime, is equated with the ability to learn at a fast rate, to master complex ideas, and to reason at a high level of abstraction (Dai & Chen, 2013). The gifted label generally applies when an ability level exceeds that of the average population of peers by two standard intelligence quotient (IQ) deviations (Gagne, 2007). The cognitive abilities of giftedness can be 18 measured from an early age by assessments intended to distinguish above average intelligence and academic potential. Those who rank two standard deviations (SD) above the mean on an intelligence assessment are likely to become the cognitive elite and to make significant contributions to society (Dai & Chen, 2013). For proponents of gifted education, selecting a certain strata of student and designing educational programming to enhance high potential is heralded as promoting the welfare and vitality of a nation or state (Dai & Chen, 2013). When discussing gifted students, it is important to note that no standardized measures have been developed for determining students’ levels of giftedness as moderate, high, exceptional, and profound. Table 1 offers guidelines by each of the assessment instrument publishers for categorizing different levels of giftedness based on IQ scores. However, because of unique demographics (peers), districts have the flexibility to determine IQ criteria that best represents the upper 5% of the student population’s IQ. For the purposes of the current project, the participating school district has designated their gifted IQ at 125 to 129, their moderately gifted IQ at 130 to 139, and their highly gifted IQ at 140 and above. Table 1 Guidelines Describing Giftedness Based on IQ Scores Full Scale IQ Score WISC-IV/WPPSI-III Extended IQ Score WISC-IV Full Scale IQ Score SB-5 Gifted or moderately gifted (G or MG) 130-138 130-145 124-133 Highly gifted (HG) 138-145 145-160 133-145 Exceptionally gifted (EG) 145-152 160+ 145+ Profoundly gifted (PG) 152-160 175+ 145++ Level of Giftedness Note. Data retrieved from "What is Gifted?" (2014, p. 1). 19 The speculation that gifted students are uniquely different from the general population harkens back to Terman’s (1925) seminal gifted research. Terman defined giftedness as having high potential. Because of exceptional intelligence, these students require a distinct set of educational programs including affect-related curriculum. The concept of a unique personhood is bound within the identification of giftedness. Building upon Terman’s research, Gagne (2005) postulated that high intelligence is indeed a necessary but not sufficient condition for ultimate achievement and adult prominence. Factors such as “motivation, personality, environmental opportunities, and instructional and technical support” are so interrelated that gifted and talented education programs need to address multiple areas of children’s early educational development, beyond simply academic and intellectual needs (Gagne, 2005). How to best educate students with exceptional intelligence was addressed by Colangelo, Assouline, and Gross (2004) in an extensive policy brief about the practice of grade and subject acceleration as an intervention that moves students through programs at rates faster, or at younger ages, than typical. Colangelo et al. (2004) encouraged educators to match the level, complexity, and pace of the curriculum to the readiness and motivation of the targeted students. They also suggested offering high-ability students the flexibility to move at the pace of their talents and not at the pace of their grade-level peers. Colangelo et al.’s (2004) argument for academic flexibility based upon student readiness would have gifted students experience schooling alongside their intellectual peers. Those who support differentiation for gifted students have maintained that educational equity does not mean educational sameness. Gifted students need not be educated with average-ability peers nor constrained to be confined to age peers. “Equity 20 respects individual differences in readiness to learn and recognizes the value of each student” as well as variations in ability and readiness even among gifted and talented students (Colangelo et al., 2004, p. 2). The impetus for individualization, even within gifted programs, was echoed by Rogers (2007) who not only challenged gifted and talented programs to individualize instruction with differentiated pacing but also to consider personalization of all educational experiences. Rogers argued for the need to develop students’ unique areas of passion and to offer cognitive challenge in the classroom to foster individual intellectual intensities. National Association for Gifted Children’s Plan Callahan, Moon, and Oh (2014) presented a national overview of the current state of gifted programs at both the elementary and secondary levels. This report reignited the interest in gifted education at the national level. Since 2011, the federal government has not allocated any funding for gifted programs. Gifted programs are funded only at the state level, and not every state allocates funds for gifted students (Callahan et al, 2014). The National Association for Gifted Children (NAGC) (2010) revised programming standards that were first developed in 1998. These adopted standards helped to promote consistency for gifted programming across states and districts. A significant shift in the new national standards provided student outcome goals as the basis for evaluation and not teacher or program practices (NAGC, 2010). Aligning itself with the accountability trend, the NAGC shifted focus from teachers to student performances and student growth measures. In order to adequately identify academic development, the NAGC recognized the problem of measuring gifted students’ yearly academic growth using traditional measurements and strongly urged educators to use off-level standardized assessments to measure the academic progress of gifted students (2009). 21 Because of the nature of the gifted student, achievement does not inform educators about the impact of their own teaching. Gifted students may already come to the classroom with high achievement. Therefore, formative progress growth measures offer researchers the opportunity to analyze the impact the educational setting has on further developing the potential of the gifted student. Texas State Plan for the Education of Gifted and Talented Students Texas passed its first legislation concerning the education of gifted students in 1977, and in 1979 state funds were allocated to support the education of gifted and talented students (Scott, 2009). In 1987, the Texas Legislature mandated that all districts must identify and serve gifted students at all grade levels (Scott, 2009). In 1990, the Texas State Plan for the Education of Gifted/Talented Students was adopted and included a commitment to providing opportunities for high-level learning to gifted and talented learners (Scott, 2009). The Texas state plan provided requirements for gifted programming and guidance to meeting gifted and talented students’ unique needs (Scott, 2009). The Texas state plan also established a common definition of giftedness as: Gifted and talented student means a child or youth who performs at or shows the potential for performing at a remarkably high level of accomplishment when compared to others of the same age, experience, or environment and who: (1) exhibits high performance capability in an intellectual, creative, or artistic areas; (2) possesses an unusual capacity for leadership; or (3) excels in a specific academic field. (Batenburg, 2014, p. 29) In addition to a common definition and the provision of gifted student outcomes, Texas offered an outline for districts to design programming based on these standards, and in 2013-2014, the state’s program evaluations for each district were made public 22 through the Texas Education Agency. Thus, the Texas State Plan for Gifted and Talented students has had a significant impact on clarifying the definition of giftedness and guiding appropriate and effective educational programming with gifted and talented students. The Texas State Plan for Gifted and Talented students is organized around five core competencies: student assessment, service design, curriculum and instruction, professional development, and family/community involvement (Scott, 2009). The state goals include student outcomes similar to the NACG goals. The outcomes include selfdirected learning, research, communication, innovative products and performances, creativity, and professional products and performances (Scott, 2009). Gifted Curriculum: Models for Gifted Students While many gifted students can be high academic achievers, some underachieve due to mismatches between students’ needs and district programming (High Reliability Organizations in Education, 2011). The loss of intellectual capital due to this mismatch has repercussions not only at the district level but also at the state and national levels. In referring to gifted students who may have the greatest potential to contribute to the nation, the McRel High Reliability Organization (2011) consortium challenged the U.S. educational system to focus resources on high achievers since educational institutions who reliably address the needs of gifted students may generate profound effects on the nation in the long term. Additionally, the McRel High Reliability Organization consortium argued that: The ability of the United States to remain a global leader in innovation, science, technology, patents conferred, business, and social entrepreneurship will depend 23 on the ability of its educational systems to not only raise the floor, but also the ceiling. (p. 2) Educators have advocated for different instructional programs for gifted and talented students based on deep philosophical views of the purpose and desired outcomes of gifted education (Batenburg, 2014). They have sought support for the unique social and emotional needs of such children. However, researchers themselves have not reached consensus on a single definition of gifted nor have they agreed on the best instructional programming model to best serve gifted and talented students (Batenburg, 2014). In Texas, school districts identify gifted students using various assessment instruments and design programming to match their specific educational philosophies or their specific financial constraints (Batenburg, 2014). Lack of consistency results in students being inconsistently identified as gifted and talented between school districts within the state of Texas (Batenburg, 2014). Consequently, a student may be identified as gifted in one district but not in another. Even when a student may be identified as gifted and talented in a given district, he or she can be served through a myriad of programming models. Programming and curricular decisions at the district level are further influenced by ethical, social-political, cultural, and pragmatic considerations. In the politicized educational climate, designing and providing educational services to a selected group of students ignites contentious debate. The discussion occurs between those who believe gifted and talented programs are elitist examples of undemocratic tracking and those who believe that gifted and talented students are a fragile intellectual resource that should not be squandered nor improperly nurtured (Batenburg, 2014). 24 Historical Perspective of Gifted Program and Curriculum Models Meeting the needs of gifted and talented students through well-designed programming rests upon curriculum models responding to intellectual talent within philosophical frameworks. The individualization movement gained prominence with Van Tassel-Baska, Zuo, Avery, and Little’s (2002) Integrated Curriculum Model for Gifted Learners (ICM), a model aligned to the characteristics for the gifted learner with an interdisciplinary focus. The five major frameworks endorsed by researchers of gifted and talented programming are listed in summary format in Table 2. Van Tassel-Baska et al.’s (2002) ICM is discipline specific and generates advanced content reliant on higher order thinking and overarching themes for its curriculum design. The ICM model has been well researched with longitudinal studies. It has been shown as effective for differentiation in the general education classes but relies heavily on independent study by gifted students for their own learning (Feng, Van Tassel-Baska, Quek, Bai, & O’Neill, 2004). More recently, Tomlinson et al. (2006) advocated for their Parallel Curriculum Model (PCM). PCM is composed of four facets: core, connections, practice, and identity The PCM’s foundations were steeped in current gifted research and characteristics of the gifted learner. Tomlinson et al. designed an ascending level of intellectually demanding processes, products, and performance tasks even within gifted classrooms. They believed in the necessity of associative thinking across content areas as vital and appropriate for gifted learners along with a strong differentiated approach to learning for the gifted (Tomlinson et al., 2006). The lack of research data on the PCM makes it a theoretical possibility, but one that may be difficult to adopt because of cross-curricular demands especially at the secondary level. 25 Table 2 Gifted Curriculum Design Models Author’s Profile/Philosophy Author/Researcher Van Tassel-Baska’s (2002) Integrated Curriculum Model for Gifted Learners (ICM; p. 46) Research based and aligned to characteristics of the gifted and talented learner Uses constant and on-going revision and review Calls for constant updating based on standards and outcomes Influenced by Adler and Paidaeia: Academic Rationalist Discipline specific with some inter-disciplinary focus Renzulli’s (1988) Multiple Menu Model (MMM) for Differentiated Curriculum for the Gifted and Talented (p. 1) Teacher is autonomous with ultimate freedom for design of curriculum having choices from a menu of options for each curricular unit Outcomes should be concrete and abstract for summative assessment Relies on curriculum developer to determine degree of complexity as appropriate to age and content area Teachers select their instructional techniques from a menu Creative products are a personal reflection of the student learning and thus are authentic in nature Teacher passion is translated into student engagement and success Gagne’s (2002) Differentiated Model of Giftedness and Talent (DMGT; p. 1) Distinguishes a difference between gifts and talents Performance is valued over potential Many catalysts spur individuals to talent Natural abilities may remain dormant if catalysts are not encountered Complex model of why some students achieve and some do not Recognizes that a gifted student may not be talented and a talented student may not be gifted Asks if classes be created for gifted or talented instead of gifted and talented Upper 10% of students hold the gifts and talents Uses a once-gifted perspective as an always-gifted approach to course entrance requirements Wiggins & McTighe’s (2012) Understanding by Design (UBD; pp. 2-20) Begins with the end in mind Allows for enduring understanding and works outward from central content to less central content Implements a backward design with assessments aligned to instruction Works backward from skill-based expectations Performance tasks and projects are well-designed, open-ended, complex, and authentic Four facets are core, connections, practice, and identity Foundations are steeped in current gifted research and characteristics are appropriate Ascending level of intellectual demand is needed even within the gifted community of learners Tomlinson, et al. (2006) Parallel Curriculum Model (PCM; p. 15) Renzulli’s (2000) Multiple Menu Model (MMM) for differentiated curriculum for gifted and talented students placed the teacher in an autonomous position to design 26 curriculum by choosing among a menu of options. MMM relies on the curriculum developer to determine the degree of complexity that is appropriate to the students’ age and the content area. This model allows for the generation of creative products as a personal expression of learning (Renzulli, 2000). The weakness with MMM may involve the lack of assurance that the teacher is able to design a balanced unit, because the menu options may cause teachers to choose instructional techniques or products that they themselves are drawn to without offering their students an entire array of product choices (Renzulli, 2000). Davis and Rimm (1998) discussed Kaplan’s “icons of depth and complexity” (p. 139). This curriculum model has permeated the gifted community with a common understanding of the intellectual pathways needed to provide curricular differentiation for gifted learners. According to Davis and Rimm, Kaplan described depth as the “language of the discipline, big ideas, essential details, rules, patterns, trends, unanswered questions, and ethics” (p. 139). Additionally, complexity includes “change over time, multiple points of view, and across the disciplines” (Davis & Rimm, 1998, p. 139). Gifted educators are well acquainted with Kaplan’s work and this model shapes the most widely implemented differentiation strategies for students (Davis & Rimm, 1998). As a flexible model, the teacher may design the curriculum with the gifted student in mind drawing from in-depth content knowledge in order to ensure students’ extended learning. The various curriculum design models presented represent only a few offered to gifted teachers. Creating a learning program from the various models is time intensive and expensive for districts to support. Curriculum creation and review remains a critical but often overlooked facet of gifted curriculum (Davis & Rimm, 1998). Dedicated 27 teachers consistently innovate and experiment with different models of instruction in the classroom and serve as responsible parties for gifted and talented curriculum design. Acceleration Acceleration disconnects student age from grade-level academic matriculation. Gifted students are more intellectually advanced than their peers of the same age. Therefore, it makes logical sense to think about readiness, rather than age, as the main determinant for grade and program placement (Colangelo et al., 2004). Reis (2004) argued that “acceleration by grade or content is an appropriate curriculum adaptation for the gifted and the child at a level commensurate with [his or her] demonstrated readiness and need” (p. 70). Kept at the same academic pace as peers, gifted students sometimes become bored or unmotivated as a condition that over time may lead to underachievement (Colangelo et al., 2004). Acceleration can sometimes save certain gifted children from suffering years of loneliness and social isolation, especially those students who fail to find a fit with their same-age peers and seek intellectual peers with similar interests (Colangelo et al., 2004). Acceleration allows multi-aged students to learn together and challenge each other at the same cognitive level. Acceleration is a form of differentiation for gifted students and has long term beneficial effects, both academically and socially. Educators who have long feared that acceleration places a child at emotional risk have been silenced by the summary research supporting the long term benefits of allowing students to learn with their intellectual peers (Xiang, Dahlin, Cronin, Theaker, & Durant, 2011). As an added benefit, acceleration is cost effective for school districts. Knowing how and when to accelerate a child involves a holistic review of the child including assessment data to provide an array of information about a students’ readiness 28 for accelerated programming. In addition, observational indicators of readiness include: motivation, daily academic performance, and parent and teacher input, all of which are available to schools (Colangelo et al., 2004). When acceleration is an option, its implementation can take many forms. Students can accelerate by a grade, multiple grades, or simply be accelerated by academic content. Those who skip grades need emotional maturity as well as academic ability in order to be successful. However, with single-subject acceleration, academic ability overrides social-emotional readiness in importance (Colangelo et al., 2004). Proponents of acceleration at the grade or content level advocate for accelerated individualized instruction that should not be “sacrificed in the name of political correctness” (Colangelo et al., 2004, p. 3). Flexibility is lost when political and cultural pressures homogenize the learning needs of individuals in the attempt to pretend that there are no meaningful learning differences between students (Colangelo et al., 2004). Retaining flexibility in course sequencing and grade placement is most needed for gifted students and aligns with the individualization needed when students are beyond the academic readiness of their age equivalent peers. Not accelerating students who have academic readiness may be “a violation of equal opportunity” that is not tolerated for other students in need of academic remediation (Colangelo et al., 2004, p. 9). Another way to discuss and defend acceleration is to consider it a form of intervention, similar to that offered to special needs students (Colangelo et al., 2004). Obvious forms of acceleration for gifted students can include starting the school day early, skipping grades in elementary school, enrollment in Advanced Placement courses before high school, and enrolling in college before high school graduation (Colangelo et al., 2004). 29 Acceleration is not a routine practice despite research supporting its use with gifted students. “When we say no to acceleration, we are quietly and ironically with good intentions lowering our national standards from excellence to baseline competence; excellence is simply disregarded” (Colangelo et al., 2004, p. 3). However, in Texas, new regulations enabling credit by exam (CBE) offer school districts a mechanism by which to accelerate all students, including the gifted, by grade or content. With new CBE state guidelines, acceleration may become a more prominent form of differentiation for gifted students in Texas (Scott, 2009). Gifted Grouping Practices for Academic Growth Grouping is a foundational academic practice especially when serving high achieving students (Reis, 2004). Purposeful grouping along with curriculum enhancement or differentiation is a best practice for any gifted program. Gifted learners achieve stronger academic outcomes when they have the opportunity to learn with those at their academic level in all academic contents (Reis, 2004). Because districts have been free to adopt their own programming delivery models for gifted and talented students, educators rely on the best practices brought forth from the research community (Scott, 2009). Many programs are endorsed by the researchers closest to educating gifted and talented students even though financial and cultural issues continue to have an effect on the educational practices targeting gifted students. A summary of the prevalence of the four most used delivery models at the national level by grade level are listed in Table 3. The different models’ strengths and weaknesses are listed in Table 4. 30 Table 3 Prevalence of Gifts Program Delivery Models Program Elementary Middle High Resource room pull-out 48% 32% 17% Within regular classroom 36% 37% 30% Separate classes 7% 20% 28% Summer or Saturday program 6% 5% 6% Note. Data compiled from Van Tassel-Baska (2006). Criticism of Grouping Gifted Students Some of the criticism of gifted programs that involve homogeneous grouping may be because the terms ability grouping and tracking have been used interchangeably even though they refer to different practices. “Tracking implies assignment to a special sequence or program of classes with other students of similar general ability for a relatively long period of time” (Reis, 2004, p. 85). Grouping is a flexible process, based mainly on formative assessments in particular content areas and movement into and out of groups is fluid (Reis, 2004). The negative connotations associated with tracking have led gifted programs to be charged as elitist, a familiar criticism of homogeneous gifted student grouping (Reis, 2004). It is common to describe giftedness in quantitative, intellectual, or IQ ability expressions. Addressing the academic needs of gifted students takes into consideration a wide breadth of IQ ranges. Giftedness is generally identified at an Intelligence Quotient (IQ) score of 130, or two SDs above the mean IQ score of 100 (Gagne, 2007). However, an ever increasing need for individualization occurs as students’ IQ scores fall farther away from the mean. In other words, the dissimilar academic needs between a student 31 whose IQ is 100 and a student whose IQ is 130 is as significant as the different educational needs of a student whose IQ of 130 and another student whose IQ is 140 (Gagne, 2007). Therefore, there is the ever increasing need for personalization as IQ scores deviate further from the mean even within gifted programming (Gagne, 2007). Table 4 Delivery Models’ Strengths and Weaknesses Model Strengths Weaknesses Pull-Out Model Built-in opportunities for peer interaction Focus on in-depth study or new area of learning One instructional plan required Limited contact time Part-time differentiation of curriculum Lack of integration with regular classroom work Push-In Model Integration into the regular classroom Focus on in-depth study or new area of learning Flexibility to group and regroup based on instructional need Gifted peer interaction limited to same grade level Limited contact time Cluster Grouping Full-time opportunity for curriculum differentiation Build-in peer group Flexibility to group and regroup based on instructional need Full-time grouping Assumes students represent the same level Gifted peer interaction limited to same grade level Multiple instructional plans required Full time Classes Ability to deliver comprehensive differentiated curriculum and programs Intellectual peer group interaction Flexibility to group and re-group based on several variables Teachers can focus on talent development Curriculum can be individualized to a high degree Perceived as more extreme than other forms May not differentiate curriculum sufficiently Note. Material adapted for table from Van Tassel-Baska (2006). Gagne (2007) advised curriculum directors to program for mildly and moderately gifted students who comprise 90% of the gifted population and program differently for the remaining highly gifted (Gagne, 2007). For example, within the top 10% of Grade 3 gifted students, the range of basic academic knowledge spans no less than five school 32 years (Gagne, 2007). Therefore, the need for differentiation and customization at the upper range of IQ levels cannot be discussed without understanding the need for further individualization within any gifted grouping model whether that is homogenous grouping or heterogeneous grouping with differentiation. Research: Informing Appropriate Grouping of Gifted and Talented Students Ability grouping should be defined as the “organizational mechanism by which students at proximate ability levels within a school curriculum are put together for instruction” (Reis, 2004, p. 70). Grouping gifted students together for advanced content instruction is considered a cornerstone for respected gifted programs (Kettler, Sayler, & Stukel, 2014). “To reject the practice of ability grouping is tantamount to denying the special instructional needs of gifted children” (Reis, 2004, p. 71). Less time spent in ability grouping promotes a unitary approach to programming that is dominated by gradelevel outcomes (Reis, 2004). Heterogeneous classrooms that deliver only a limited adjustment in course content for high-ability students usually have little or no effect on student achievement (Reis, 2004). Programs that constitute substantial adjust of curriculum to academic readiness produce positive effects (Reis, 2004). Programs of homogeneous grouping and acceleration usually involve the greatest amount of curriculum adjustment and tend to have the largest effect on students’ yearly academic growth (Reis, 2004). Outside of Reis’ study, the evidence for grouping gifted students in particular ways for academic gains remains inconsistent. Most studies report homogeneous gifted grouping as having a modest positive effect even though few grouping studies specifically have focused on the grouping factor. Thus, no clear recommendation for 33 grouping has been suggested by current research findings. Perhaps that is why a multitude of grouping practices is observed nationwide (Reis, 2004). Reis (2004) suggested that achievement for gifted students in the elementary grades is enhanced with cluster grouping even as the general education population remains unaffected by the presence of a small group of above-level gifted students. Reis (2004) concluded that low-ability students within the general education classroom in which gifted students are clustered do not model their behavior or raise their academic achievement by virtue of being in close association with gifted learners. Therefore, the common argument that the cluster grouping of gifted students helps to raise the intellectual level of an entire class by virtue of association is not supported by research (Reis, 2004). The question remains as to which program model offers the largest yearly academic gain for gifted students. The yearly academic gain effect sizes of different gifted programming models are not consistent across studies as seen in Table 5. However, Rogers (1993) study originally cited historical research and mean or median effect sizes for different gifted program styles. Rogers (1993) considered the effect size of .30 as the level at which yearly academic gains should be valued as practically significant. Even though yearly academic gains have generated significance for several grouping options, the many variables encountered in an educational experience have reduced these effect sizes’ generalizability. Rogers (1993) cautioned that an effect size might represent a one-time comparative gain in one study and for others that gain could have been cumulative. Rogers (1993) added that a large effect size does not identify a particular program as superior to other programs. “The individual variations in organization, personnel, 34 population demographics, and culture from school to school may be more important to the success of a particular program option than the effect sizes reported ” (Rogers, 1993, pp. 3-4). Homogeneous Grouping The philosophy of homogeneous grouping as a program model rests upon the belief that gifted and talented students by virtue of their increased intellectual capacity are significantly dissimilar to other same-age students and, because of that distinction, their cognitive differences and social and emotional needs are unlike those of same-age general education students (Weinbrenner, 1992). Nevertheless, homogenous programming is the grouping model historically disparaged as elitist (Weinebrenner, 1992). Elitism might well be defined as arbitrarily giving preference to some group based on a misperception of superiority (Weinebrenner, 1992). Being able to function at an advanced level intellectually does not automatically make an individual better than anyone else. It does, however, imply a difference that requires an educational response that may be erroneously interpreted by some as giving one group an unfair advantage. In fact, educators of gifted and talented students consistently work to develop an understanding of giftedness in the context of individual differences rather than as an issue of superiority versus inferiority (Pennsylvania Association for Gifted Education, 2014). The research on academic outcomes for homogenous grouping of gifted students is more consistent than the research on heterogeneous grouping of gifted students. Some unequivocal statements supporting homogenous grouping have come from respected researchers in the field, such as Gagne (2007) who bid “educators to aim as much as possible for full-time grouping of gifted students” (p. 109). Gagne provided this call to 35 arms only after the findings of earlier studies showed greater academic gains occurred with gifted students who were grouped homogeneously. Table 5 Academic Effect Sizes of Program Options for Gifted Students Option Academic Effect Size Early Entrance to School .39 Subject Acceleration .49 Curriculum Compaction .45 Grade Skipping .78 Enrichment (pull-out) .65 Enriched Classes Ability Grouped .33 Cross-grade Grouping (reading, math) .45 Nongraded Classes .38 Concurrent Enrollment .36 Regrouping for Specific Instruction (reading, math) .43 Advanced Placement .29 Credit by Examination .75 Cluster Grouping (specific differentiation) .33 Cooperative Learning Johnsons "Learning together" .0 Classes Ability Grouped .33 Slavin's TGT .38 Slavin's STL(combination) .30 Grade Telescoping .56 Mentorship .42 Note. Data adapted from Rogers (1993, p. 3-4). Earlier, Cox, Daniel, and Boston (1985) found heterogeneous classrooms with enrichment to be an effective academic practice for supporting gifted students’ academic 36 needs in the regular general education classroom. When heterogeneous grouping was used for gifted students, only 58% of teacher respondents from 4,000 school districts reported that enrichment activities occurred for gifted students at a rate of less than 3 hours a week (Cox et al., 1985). Additionally, “25% of the enrichment activities were for the whole class” and were not specifically geared to the gifted students clustered in the classroom (Cox et al., 1985, p. 95). Cox et al. concluded that little effort to offer enrichment specifically for the most able learners tended to be offered in classrooms with heterogeneous grouping. In 1993, Archambault et al. of the National Research Center of the Gifted and Talented conducted a survey of ongoing enrichment practices in U.S. school districts. Archambault et al.’s results with a representative sample of more than 7,000 third and fourth grade teachers who received a detailed questionnaire designed to determine the extent to which gifted students were receiving differentiated instruction in the regular classroom were similar to Cox et al.’s (1985) findings (Archambault et al., 1993). The results revealed that “most of the enriching activities were offered less than a few times a month, and these activities were usually targeted to the whole class,” leaving little specific enrichment for gifted students (Archambault et al., 1993, p. 5). In sum, no matter how well intentioned the heterogeneous grouping model, gifted students receive little modification to the curriculum in regular third and further grade classrooms when cluster grouped (Archambault et al., 1993). Westberg and Daoust (2003) replicated Archambault’s (1993) study in order to document whether Archambault’s findings of limited differentiation for gifted students in heterogeneous classrooms still existed. Westberg and Daoust generated similar findings. Third and fourth grade teachers offered relatively insignificant efforts at differentiated 37 instruction or content acceleration of curriculum to meet the needs of gifted students clustered in general education classrooms (Westberg & Daoust, 2003). During the 10 years between these research studies, differentiation as an instructional strategy ironically gained ground in both the research community as well as in education. Westberg and Daoust (2003) tracked gifted students in two states, one in the Southeast with a gifted and talented state mandate and one in the Midwest without such a mandate. Their total district sample size of 1,366 represented 17% of the third and fourth grade teachers in those two states (Westberg & Daoust, 2003). Westberg and Daoust tallied teachers’ responses as to the instructional practices they used with gifted students compared those they used with general education students. They used the same six factors studied in the original Archambault et al. (1993) study: teacher training, teacher willingness, self-evaluation of differentiated strategies over time, teachers who worked within a supportive gifted district, and teachers who reported working in a district free of restrictive practices (Westberg & Daoust, 2003). Westberg and Daoust observed minor mean differences of teachers’ responses to the amount of differentiation applied specifically with gifted students and the mean of the amount of differentiation provided to on-level or struggling students. Inferential statistics showed no statistical difference among any of the cited six factors (Westberg & Daoust, 2003). An additional demographic data analysis revealed no statistical difference between differentiation practices from rural, urban, or suburban districts (Westberg & Daoust, 2003). As a sidebar to the Westberg and Daoust (2003) study, no correlation existed between teachers’ professional learning experiences about gifted students and their ability or willingness to implement differentiation in their classrooms. However, teachers who had college or university training in gifted education (n = 179) were more 38 likely to differentiate the curriculum for gifted students than teachers (n = 337) who had no university of college training in gifted education (Westberg & Daoust, 2003). Westberg and Daoust (2003) concluded that even with differences in educational background in gifted education, actual classroom instructional practices showed no difference (Westberg & Daoust, 2003). Without homogeneous grouping, gifted students have little probability to experience any significant differentiation from their same-age, same-grade peers (Gagne, 2007). Full time homogeneous grouping of gifted students has generated academic achievement and growth data to support this model. Positive socio-affective results have also been observed for homogeneously grouped gifted students (Kulik, 2003). Gagne (2007) articulated that: It can be generalized from the research that full-time grouping is the only way to create appropriate conditions for an enriched curriculum. It answers a permanent problem with a full-time solution; it facilitates the enrichment of all subject masters in the regular curriculum and it does not require adding a teacher to the school’s personnel. (p. 111) Even the highest achievers in a homogeneous grouped classroom benefit from having to compete with one another (Kulik, 1992). In addition, when gifted high achievers are removed from the classroom environment, general education low achievers benefit from not having to compete with their more able peers (Kulik, 1992). These early findings still remain relevant and mitigate the concern that low-achieving students are harmed academically when grouped with their academic peers (Brulles, Saunders, & Cohen, 2010). Swiatek (2001) showed that gifted students in like-ability classrooms had larger academic gains in a year than students who had classmates of varied academic 39 ability. Goldring’s (1990) and Swiatek’s (2001) findings supported the conclusion that gifted students in like-ability classrooms achieve statistically significantly higher scores on state assessments than their gifted counterparts in heterogeneous cluster-grouped classrooms. Rogers (2007) conducted a meta-analysis and supported homogenous grouping for ensuring the academic growth of gifted students. Rogers (2007) also reviewed 13 research studies on homogeneous grouping and concluded that gifted students grouped among intellectual peers “produced marked academic achievement gains as well as moderate increases in attitude toward academic subjects” (p. 9). The researchers in Rogers (2007) review cited these benefits of homogeneous grouping of gifted students: academic achievement improved (Gentry, 1999; Tieso, 2005), students having a more realistic perception of their academic strengths and weaknesses and increased academic challenge that was more consistent in the classroom (Kulik 2003, Rogers, 2002), teachers had the ability to meet the emotional and social needs of gifted students (Kulik 2003, Rogers, 2002), and teachers were better able to address cognitive demands when the range of student abilities was narrower (Rogers, 2007). Kulik (2003) and Rogers (2002) both noted that gifted students benefitted from homogeneous grouping if the curriculum was adjusted to the cognitive demand and readiness of the students. In addition, student interest is stimulated by advanced content resulting in increased motivation (Gentry & Mann, 2008). In conclusion, homogenous grouping removes the ceiling for gifted students and diminished underachievement over time (Gentry & Mann, 2008). Homogeneous gifted programming has been shown in the literature to benefit the highest ability students when they are served through multiple pathways: academically, 40 socially, and emotionally (Kulik, 2003). In addition, when grouped with their intellectual peers gifted students are given the opportunity to experience similar high performing students (Rogers, 2007). In contrast, while in heterogeneous groupings, these same students may be able to perform below their potential and still be academically excelling beyond their classmates (Rogers, 2007). By grouping more homogeneously, the “façade of effort and ability can be removed and replaced with more appropriate challenge and rigor” (Gentry & Mann, 2008, p. 15). The ever increasing range of academic abilities within a typical classroom makes it difficult for a lone teacher to teach a single curriculum to all students (Farrar, 2003). Analysis of the impact of homogeneous grouping suggests that increases in academic achievement can be credited collectively to the more complex instruction by teachers knowledgeable in gifted education in tandem with challenging curriculum (Kulik, 1992). To reiterate, grouping similar-ability students full time through homogenous grouping has the highest effect on academically talented students regardless of the clash with public education’s democratic assumptions (Kulik, 1992). The academic benefits are especially acute for highly gifted homogeneous student grouping (Reis, 2004). Conflicting evidence of homogeneous grouping. To provide a balanced view, some research reveals contrary evidence to the positive benefits of homogenous grouping of gifted students. Oakes (1985) reported no academic benefits for gifted students in the upper or higher achieving tracks when grouped homogenously. In addition, Oakes also found gifted students in lower tracks lost academically and socially because they were segregated from their high achieving gifted student peers. Oakes’ descriptive research findings were not consistent with the quantitative results previously cited. 41 Kulik (1992, 2003) also presented conflicting evidence that multi-ability grouping can have positive effects on both academic growth and self-concept of the general population as well as the gifted population (Reis, 2004). The inconclusive results of these studies, however, came from data received from classrooms in which above-level testing measurements were not used. The stretch in academic ability can only be measured for gifted students by such above-level instruments. The emotional and socio-political issues surrounding grouping keep this area of gifted research limited. However, Reis (2004) argued that proponents for or detractors of full-time homogeneous gifted student grouping are capable of generating data to support their particular ideologies. This may also reflect the complexities of measuring the academic growth that is knowingly based on a multitude of individual factors, many of which are not quantifiable. Adams-Byers, Whitsell, and Moon (2004) investigated gifted students’ perceptions about the academic and social effects of homogeneous and heterogeneously grouped classes. Adams-Byers et al. (2004) used a population of 44 students in Grades 5 through 11 who completed a self-developed interview or questionnaire while attending a summer residential program for gifted and talented students. Adams-Byers et al. analyzed their data using qualitative cross-case, constant comparative procedures. Perceived academic advantages were “more than 3 to 1 in favor of homogenous (high-ability) grouping (78%) as opposed to heterogeneous (mixed-ability) grouping (22%)” (AdamsByers et al., 2004, p. 10). Participants perceived “high-ability homogeneous grouping as offering greater academic advantages (57 to 16, a ratio of more than 3 to 1)” (AdamsByers et al., 2004, p. 10). Only two students responded that high-ability homogeneous grouping had no academic advantage (Adams-Byers et al., 2004). 42 Adams-Byers et al. (2004) suggested that gifted students differ in their desire to participate in mixed-ability groups. The particular participants in the Adams-Byers et al. study preferred grouping models that brought them in constant contact with their gifted peers and cited an intellectually competitive environment as an advantage. Almost onehalf (47.3%) cited academic advantage preferring the fast pace, high challenge level, and lack of repetition when educated with their peers (Adams-Byers et al., 2004). AdamsByers (2004) included comments made by respondents: You’re surrounded by people who have a similar ability level, your classes move quickly and are more challenging, you can work at a high pace and go quicker, you don’t have to dwell on one subject too long, you are not held back, teachers have quality time with me, and you don’t have to help other kids. (p. 11) Some disadvantages to homogeneous grouping in the qualitative questionnaire by Adams-Byers et al. (2004) reported one participant’s response, “Others are more intelligent than I am, stress of academic competition, some students are opinionated and overbearing in academic discussion, heavy workload, and high expectations” (p. 11). Adams-Byers et al. asserted that placing gifted students together offered the opportunity for increased performance and often developed a “mastery-orientated and self-referenced approach to learning” for more beneficial long term results (p. 16). However, 16% of the students in Adams-Byers et al.’s study preferred to maintain social contacts with friends not included in the gifted program’s groupings. Interestingly, 76% of the students preferred homogenous classes as socially preferred because the homogeneous environment offered a “safe haven, a place they could be themselves without fear of ridicule” (Adams-Byers et al., 2004, p. 16). 43 Accepting Adams-Byers et al.’s (2004) conclusions requires caution. The survey questions were specific to the students in a summer school program and might not be generalizable to students enrolled during the full academic year. In addition, the volunteers’ self-reported data indicated the need to use caution when interpreting the results (Adams-Byers et al., 2004). The research population represented an overabundance of females (61.3%) and more students were in Grades 8 through 10 (63.7%) than in Grades 5 to 7 (36.3%; Adams-Byers et al., 2004). The largest limitation of Adams-Byers et al.’s study was the population itself, which the researchers acknowledged as atypical of the general gifted population including features such as motivation, interest, financial resources, and familiar support (Adams-Byers et al., 2004). The students enrolled in the special summer program tended to be highly engaged and motivated gifted students and thus the sample might not reflect a broad spectrum of the general gifted student population. Research that supports homogeneous gifted student grouping. It is important to note that when self-reporting, gifted students prefer homogeneous grouping for improved academic outcomes (Adams-Byers et al., 2004). However, Adams-Byers et al. (2004) found some gifted students preferred heterogeneous classes because they were “easier and enabled them to attain a high class ranking with little work” (p. 7). These findings reinforced earlier research by Kulik (1992), Rogers (2000), and Moon and Roselli (2000) in which homogenous grouping led to stronger academic effects. Therefore, educational leaders may choose to offer both homogeneously grouped academic programming as well as heterogeneously grouped social experiences (AdamsByers et al., 2004). In sum, Adams-Byers et al. concluded that “regardless of grouping arrangements, each student must be considered individually and instructional 44 programming must be designed to be as flexible as possible to best serve students and the importance of within-group differentiation in gifted programs” (p. 18). Adams-Byers et al.’s (2004) findings were supported by those of Matthews and Kitchen (2007) who conducted a mixed method multiple case study with surveys of three different gifted public high schools following a homogeneous school-within-a-school model. The specialized school-within-a-school approach was considered one way to provide intensive gifted programming along with opportunities for gifted students to interact with the general population (Matthews & Kitchen, 2007). The smaller schoolwithin-a-school model created the feeling of housing a small and cohesive multi-grade level school within the context of a larger educational organization (Matthews & Kitchen, 2007). Matthews and Kitchen (2007) sought to investigate students’ perceptions of autonomous gifted programs through questionnaires, interviews, and observations of students and teachers. Matthews and Kitchen (2007) did not draw conclusions unless triangulation of the data was present. The researchers sought to discover common themes in order to report perceived strengths and weaknesses in the gifted school-within-a-school program. Responses from the three high schools varied from 80% to 97% as favorable toward the gifted school-within-a-school program, and the differences were not statistically different across the three schools (Matthews & Kitchen, 2007). Strengths of homogeneous grouping for the gifted students participating in Matthews and Kitchen’s study were similar to the elementary level research by Adams-Byers et al. (2004). Matthew and Kitchen found the following: Challenging academic programs, enrichment opportunities, a faster pace, more interesting coursework, strong teachers, good preparation for a university, better 45 environment for learning, interaction with students with the same goals, smaller classes, higher level of academic motivation, competition academically, acquiring positive learning habits, developing time management skills, and establishing closer bonds between program students. (p. 264) Teacher comments about homogeneous gifted classrooms reflected those of their students. Teachers reported higher level peer interaction between gifted students and enjoyed the autonomy to create stronger learning environments to meet the high-ability gifted students’ needs (Matthews & Kitchen, 2007). Matthews and Kitchen (2007) reported from interview findings that gifted students appreciated teachers who could create a classroom attuned to students who want both high challenge and need emotional encouragement, fairness, and support. The often cited meta-analysis report by Yiping et al. (2014) showed the benefits of homogeneous gifted student grouping by aggregating 20 independent effect sizes. The results “indicated a slight superiority of homogeneous ability groups over heterogeneous ability groups in promoting student achievement” across all content areas (Yiping et al., 2014, p. 445). The largest effects sizes were noted in ability groups for reading in which the mean effect size was (dM = .36). In conclusion, on average, gifted students in homogenous ability groups achieve higher yearly academic growth than heterogeneous ability groups (Yiping et al., 2014). Research: Direct comparison of gifted grouping models. The often cited research of Delacourt and Evans (1993) compared four different models for gifted programming: full-time special schools, homogeneously grouped classes, pull out programs, and within-class cluster grouping. Gifted students in the special schools, homogeneous classes, and pull-out programs showed substantially higher levels of 46 achievement than students in the cluster groups (Delacourt & Evans, 1994). Oddly, the gifted students in the cluster group model demonstrated lower achievement scores than gifted students receiving no programming at all (Rogers, 1993). Delacourt and Evans (1993) concluded that “unless done effectively, within-class cluster grouping can disintegrate into no programming at all for gifted students. Adelson and Carpenter (2011) studied reading growth of kindergarten students, including gifted kindergartners. “The sub-sample of kindergarteners who were in a gifted programs experienced growth ranging from -3.57 to 33.13” (Adelson & Carpenter, 2011, p. 265). Reading growth for children in a gifted program was, on average, higher and more variable than reading growth for the full sample (Adelson & Carpenter, 2011). Students in classrooms with effectively implemented grouping “experienced greater growth in reading and the effects were even greater” for gifted students with high ability (Adelson & Carpenter, 2011, p. 265). Teachers who work with students of similar achievement levels are more likely to reach students within the zone of proximal development and to more effectively instruct those gifted students (Adelson & Carpenter, 2011). “Although gifted students may enter kindergarten with a great deal of reading readiness, they make greater reading gains when provided opportunities to learn through achievement grouping, particularly in smaller groups” (Adelson & Carpenter, 2011, p. 265). Adelson and Carpenter concluded that gifted kindergarteners showed benefits in yearly academic growth only with above-level grouping. In summary, “the average teacher adjusts their teaching pace to the learning pace of students close to the 25th percentile” (Gagne, 2007, p. 104). Even the mildly gifted student is significantly different in terms of ease and speed of learning from their average peers and would benefit from a differentiated education (Gagne, 2007). “Grouping 47 heterogeneously and even providing cooperative learning in flexible groups tends to lower achievement and motivation as well as increase poor attitudes toward school” for the gifted (Reis, 2004, p. 81). Longitudinal research on gifted student homogeneous grouping. Vogl and Preckel (2014) conducted a longitudinal study of full-time gifted ability grouping from fifth grade to sixth grade. While self-concept may not be directly tied to academic achievement, Vogl and Preckel found that children in homogeneously grouped gifted classes exhibit more interest in school and demonstrate stronger student-teacher relationships than their peers (Vogl & Preckel, 2014). They argued that for gifted students to reach their potential, they must possess psychosocial strength to transform their abilities into achievements and by having the emotional support necessary for these effects to occur (Vogl & Preckel, 2014). Vogel and Preckel’s (2014) participants came from five schools located in Germany and belonged to the top performing secondary school system with full-time ability grouping of gifted students. The setting and participants might have made the Vogel and Preckel study’s findings non-generalizable. However, the findings showed that students mentioned motivation, challenge, and teacher behaviors as advantages of homogenous grouping (Vogl & Preckel, 2014). Gifted students in homogeneous classes also reported no decline of interest in school at the secondary level, something quite common among the general population (Vogl & Preckel, 2014). Student-teacher relationships remained constant in the gifted classes but declined with the general population (Vogl & Preckel, 2014). The researchers reported small to moderate affective effects for gifted students grouped with their peers and inferred that these socio-affective variables generate increased motivation, reduced stress levels, and 48 promoted higher achievement (Vogl & Preckel, 2014). In addition, belonging to the gifted cohort resulted in students demonstrating increased self-esteem (Vogl & Preckel, 2014). Heterogeneous Grouping The inclusion model, imported from special education, has spawned the impetus for heterogeneous grouping. Kulik’s (1992) seminal study of grouping asserted that the “damage to gifted students would be truly great if, in the name of de-tracking, schools eliminated enriched and accelerated classes” (p. 73). Heterogeneous grouping appeases cultural and socio-political ends, but the overall impact to gifted programs is detrimental in the long run (Reis, 2004). Ability grouping and content acceleration “must be attended to in some form in order to ensure that programs are meaningful for this special group of learners” (Reis, 2004, p. 70). The impediments inherent in moving from the theory of cluster grouped heterogeneous classes to the reality of the general education classroom make this model a difficult one to implement and maintain as the model’s success lies solely in the hands of an exceptional teacher. Enrichment instruction: A means to serve gifted students in heterogeneous grouping. Enrichment acts as an express lane for gifted students with an added benefit of increased motivation (Gagne, 2007). Also, by condensing or compacting the regular curriculum time is created for other learning activities. The amount and level of enrichment or compacting of content should be dependent on the level of giftedness and academic readiness (Gagne, 2007). The theoretical model for differentiation remains valid within gifted research, but practical classroom implementation practices reveal a less than ideal outcome. For example, Cox, Daniel, and Boston (1985) discovered a dismal outlook for the enrichment 49 practices for gifted students in general education classes. Some researchers in the field of gifted education are bold enough to declare that enrichment programs do not produce adequate academic yearly progress (Slavin, 1990). Recently, Gagne (2007) believed that if the same research survey as Cox, Daniel, and Boston (1985) were conducted at any point in the future, the academic gains for gifted students receiving enrichment in the general education classroom would be similarly categorized as “fragmented and discontinuous” (p. 107). Proper enrichment activities should be judged from two perspectives. The first is whether they are relevant with respect to the learner’s abilities, interests, needs, and personality as well as a learning vehicle to demonstrate maximum academic talent (Gagne, 2007). The enrichment curriculum must be rich cognitively in order to be academically defensible (Gagne, 2007). Especially at the elementary level, learners’ needs can be addressed through personalized activities of choice as well as additional time to pursue personal projects (Gagne, 2007). Enrichment as an instructional strategy for heterogeneous classrooms. Enrichment for gifted students in a heterogeneous classroom can be differentiated by content, process, or product (Tomlinson et al., 2002). Differentiating content allows more depth through acceleration. Theoretically, the goal of content differentiation is to remove the learning ceiling and thereby allow highly able students to move through the material at a pace the suits their ability. Content for gifted students can be altered with complexity and abstractness. High ability students can quickly move from acquisition to application and finally to transfer with increased focus on relationships and generalizations (Gentry & Mann, 2008). 50 Process can be differentiated by shared inquiry, creative problem solving, problem-based learning, and discovery learning (Gentry & Mann, 2008). Because these practices are seen as highly engaging for all students, criticism remains as to how these approaches are applied any differently for gifted students than for general education students. Gentry and Mann (2008) responded that gifted students’ academic products should strongly reflect professional standards which are much higher than grade level standards. Products that are designed to assess a students’ learning can be personalized from a menu of options or be self-created by the student’s themselves (Gentry & Mann, 2008). Unique products should also offer an assessment of the learning process itself rather than just a final performance task. Products should embed cognitive demand and “stretch students in application of understanding and skill as well as in the pursuit of quality” (Tomlinson et al., 2002). If gifted students need measurably different learning experiences, then the heterogeneous classroom, even with product development, may be insufficient if the cognitive demand is not embedded in the design of the task. Cluster grouping with differentiation in heterogeneous classrooms. The differentiated classroom is reminiscent of the one-room schoolhouse where different ages and abilities were a normal part of the classroom experience. Cluster grouping is a type of ability grouping for gifted students within the general education classroom. It is the practice of grouping four to six identified gifted students into a single classroom with a teacher who has received training on differentiation and teaching to gifted students (Walker & Seymour, 2002). Gifted students are clustered into classrooms with a teacher who has been designated as the teacher of record. The cluster grouped classroom also includes non-gifted students. Cluster grouping is cost efficient and also does not disrupt 51 the general education setting. Thus, it has becomes an attractive model for districts who may not be able to create magnet or homogenous school-within-a-school models. Cluster grouping represents a model that allows gifted students to receive services on a full time daily basis. Strategies used within the cluster-group classroom for gifted are: acceleration, compacting, enrichment, independent studies, and flexible grouping (Brulles & Winebrenner, 2011). Ongoing assessment invites flexible grouping according to the needs and results of both formative and summative assessments. Advantages for cluster grouping. Advantages of cluster grouping for teachers cited by Walker and Seymour (2002) include: the facilitation of lesson planning, curriculum pacing, class scheduling, and communication with parents. Disadvantages of cluster grouping for teachers are: time as a constraint for individualization, difficulty in managing the differentiated classroom with a wide range of instructional needs, and lack of training (Walker & Seymour, 2002). Walker and Seymour cited advantages for students as: learning at their own pace, an increased interaction with other students, and participating in learning with their intellectual peers. A disadvantage for students cited in the research was: lack of group dynamics (Walker & Seymour, 2002). Academic effects for robust differentiation can be substantial depending upon the amount of compacting and enrichment that actually occurs for learners. Under ideal circumstance, gifted students in these classes can gain from 1.5 years to 1.75 years growth in a content area for which they are grouped (Rogers, 2007). Differentiation as the key to cluster grouped success. Differentiated instruction is a best practice in gifted and general education that has emerged through research on learning and brain functioning, as well as gifted education pedagogy (Gentry & Mann, 2008). Differentiated instruction tends to maximize annual growth (Gentry & 52 Mann, 2008). Proper differentiation demands that teachers reflect on student commonalities and differences and create learning tasks that match the needs of groups of similar students (Gentry & Mann, 2008). Differentiation is not the content of the classroom but rather the manner in which it is delivered, and the strategies and techniques are used to ensure skill acquisition during engaging and cognitively complex creative activities (Gentry & Mann, 2008). Differentiation, the process of adapting instruction to the needs and abilities of students, is key to enhanced learning within a heterogeneous group model” (Van Tassel-Baska, 2006, p. 2). The differentiation paradigm argues that curriculum and instruction should be adapted to the individual needs of each gifted student (Dai & Chen, 2013). The Texas State Plan for the Gifted verbalized the need for flexible grouping patterns and increased pacing (Scott, 2009). The Texas State Plan also calls for districts to “meet the needs of gifted students by modifying the depth, complexity, and pacing of the curriculum and instruction ordinarily provided by the school” (Scott, 2009, p. 11). When instructional standards fall outside students’ zones of proximal development, differentiation is called for regardless of academic identification (Dai & Chen, 2013). Discussions regarding effectiveness of cluster grouping. Purposeful and intentional differentiation lies at the heart of the cluster-group model. Although cluster grouping for gifted students is widely promoted, varying empirical evidence exists to support its practice for improving academic growth. Because enrichment and differentiation is largely left up to the individual classroom teacher to implement, the difference between ideal and actual effectiveness is recognized. Gentry and MacCougall (2008) found that “curricular differentiation is more efficient and likely to occur when a 53 group of high-achieving students is placed with a teacher who has expertise, training, and a desire to differentiate curriculum than when these students are distributed among many teachers” (p. 12). In order to evaluate the practices of cluster grouping, Brulles, Saunders, and Cohn (2010) studied cluster grouping at Glendale Elementary School District (GESD), an urban elementary district where gifted students were cluster-grouped as the sole model for providing gifted services. Findings were that implementation was inconsistent based on site administrators (Brulles et al., 2010). However, cluster grouping with adequate implementation can have positive academic effects for gifted students. Brulles et al. found statistically significant yearly academic gains during the 2003-2004 school year using repeated math benchmark assessment data. Brulles et al. also found statistically significant higher student academic growth in math for cluster grouped gifted students versus gifted students who were not cluster grouped regardless of gender, grade level, ethnicity, and English language learner status. In Brulles et al.’s study, 72% of gifted students received services in the cluster-grouped classroom while 28% of students identified as gifted did not receive cluster grouping services in their regular heterogeneous classrooms (Brulles et al., 2010). These findings indicated that gifted student cluster grouping is academically preferable to non-cluster grouping but do not indicate whether cluster grouping represents an optimal academic accelerator over the homogeneous gifted student grouping. Cluster grouping in the earliest elementary years may be beneficial to an individual school campus as well as its individual students. “The frequency with which teachers used achievement groups in reading was positively related to mean school gains in reading across the kindergarten year” (Adelson & Carpenter, 2011, p. 267). However, 54 again, Adelson and Carpenter (2011) did not address whether or not cluster grouping is the most advantageous learning experience for gifted students. To accommodate the needs of gifted students while maintaining equity for all students, Slavin (2006) listed two important advantages of flexible heterogeneous or cluster grouping of gifted students over the homogeneous ability grouping model. First, cluster grouping reduces labeling effects, and second, achievement in content areas determines placement and not ability level (Slavin, 2006). Nonetheless, accommodating gifted students’ needs represents only a baseline measure and not an indication of the best programming for optimal academic growth for gifted students’ Other researchers viewed the issue of differentiation for gifted students within the framework of intervention, a term common in Special Education research. In a statewide best practice report for the state of Montana, Juneau (2009) offered a framework for gifted students aligned to the mission of both serving gifted students and seeing gifted students as part of Response to Intervention (RTI) implementation. The mission of gifted education in Montana involves “implementing and sustaining efforts which ensure our gifted students have access to differentiated curriculum, flexible pacing, cluster grouping, acceleration, and other universal interventions available to all students in the regular classroom” (Juneau, 2009, p. 2). However, the evidence for the general education classroom serving as the optimal learning environment for gifted students is not conclusive. Conflicting results of gifted education models using the RTI model. Juneau (2009) reported findings in regard to the cluster grouping of gifted students in Montana as fivefold. Juneau also revealed the contradictions of using the RTI model for gifted and talented programming: 55  Gifted students tend to mistrust the benefits of small group learning; care must be taken that the tasks demonstrate that the group can do better than the individual.  Gifted students perform significantly better when the majority of their time in academic core areas is spent in true peer interactions.  Gifted students show a preference for self-structured tasks and selfimposed deadlines.  Gifted students show a preference for working projects alone or with one like-ability peer.  Some gifted students do not appreciate, and actually resent, being peer tutors. This is especially true if they are called upon to tech others on a regular basis. (p. 14) Juneau’s (2009) findings challenged the merits of cluster-grouping on a full time basis, especially in light of other studies in which cluster-grouping with differentiated instruction leads to ongoing problems for gifted students. Juneau concluded that individualized and differentiated instruction for gifted students is “easy to articulate, but it is fiendishly difficult to achieve in schools where standardization is the norm and where teachers are instructed in being recipe followers, rather than flexible and reflective artisans” (p. 51). Other large public entities seem to agree with Juneau’s (2009) Montana report and to question the benefits of cluster grouping in favor of homogenous gifted classes. “Full time programs, whether they involved special schools or a school-within-aschool, give students maximal exposure to intellectual peers and thus peer support for high achievement” (Olszewski-Kubilius & Limburg-Weber, 2014, p. 3). 56 The extension to this thinking is that the differentiation movement was born of political and monetary necessary and not what research has shown offers gifted students the best opportunity for their largest yearly academic growth. Questions remain about the theory of cluster grouped differentiation for gifted students. Dr. Carol Tomlinson, the originator of the whole school cluster model, discussed the practical problems related to differentiation in an interview by Wu (2013). Wu (2013) concluded the following: With the ever expanding needs in the general education classroom, the teacher’s job to help each of the students understand that everybody has a next step in learning. When everybody’s next step is the same, great. But if the next steps differ for different students, which is typical, then it becomes the teacher’s role to create more than one ‘next step.’ (p. 130) This classic pattern of different intervention and instruction for different groups of students on a daily basis that sustains the differentiation model may be untenable and simply asking too much of a classroom teacher as students’ academic readiness range continues to expand. In defense of the differentiated cluster grouping model, Dai and Chen (2013) blamed the poor results on weak teacher training and lack of will on the part of the campus or district administration. When reacting to the criticism that differentiation is unrealistic due to the many constraints on the classroom teacher to cover grade level standards for all students, Dai and Chen (2013) stated that the theory of differentiation is not invalidated by poor implementation. However, Dai and Chen (2013) acknowledged that “as the diversity of students in the same classroom escalates, the question of how to meet precocious and advanced learners’ unique educational needs through appropriate, personalized education services in the regular classroom becomes even more salient for 57 educators” (p. 157). Dai and Chen (2013) acknowledged that the main impetus of differentiation is to “avoid gifted-non-gifted bifurcation that raises equity concerns” and not evidence of academic growth for gifted students (p. 158). Back to theory: Cluster grouping with differentiation. Cluster grouping is designed to encourage the teacher to increase the pace of instruction while providing more support to struggling students. For clustering to be successful for all groups, modification and adjustment to curriculum based on the students’ ability level and developed skills should be regularly assessed and planned for (Brulles et al., 2010). The ability to provide consistent differentiated curriculum and instruction to all subgroups is essential to student achievement within the gifted cluster-group model (Brulles et al., 2010). Teacher training in differentiation practices remains a critical component to cluster program model successes (Brulles et al., 2010). The seminal research of Kulik (1992) used a meta-analysis to examine findings on grouping from research conducted from 1916 to 1992. The authors found that academic gains for gifted students were directly influenced by the degree of curricular adjustment and not the form of grouping per se (Kulik, 1992). In other words, grouping in a cluster model is effective if the curricular differentiation is consistent and on-going. If cluster grouping is not led by a curriculum or teacher who differentiates as a matter of practice, then gifted students will not see academic gains (Kulik, 1992). The author’s conclusion was that gifted students are able to have their academic needs met through cluster grouping and that such grouping is an “appropriate and necessary function of the school system” (Kulik, 1992, p. 127). The landmark study on cluster grouping was conducted by Gentry (1999) who completed a causal-comparative, longitudinal study of cluster grouping at the elementary 58 level and provided recommendations based on the findings. Gentry used both quantitative and qualitative methodologies to examine the effects of an existing cluster grouping program on the achievement and identification of students who participated in the program from third through fifth grade and compared achievement with similar students who were not involved in a cluster grouping program. In qualitative follow-up data collection, Gentry investigated the practices of teachers to glean insight and recommendations. The treatment school implemented a total school cluster grouping program which is less common than simple classroom cluster grouping (Gentry, 1999). Gentry (1999) reported the following:   Elimination of grouping may not be beneficial to students Heterogeneous grouping may not be the best arrangement for student placement in classrooms because placing high achievers in one classroom can increase the chance that their needs will be met while offering the opportunity for talent among other students to emerge  Restricting the range of achievement levels in elementary classrooms can help teachers better address the individual needs of all learners. (p. viii) Cluster grouping to match instructional readiness was examined in a six-year research project designed by Pierce et al. (2011). Pierce et al. gathered two years of data from a large urban school district with two successive cohorts of gifted third-grade students in a cluster classroom setting with three to 10 gifted students in a full 20 to 25 sized general education classroom. Pierce et al. reported that curriculum materials, grouping practice, and level of teacher intentionality were all significant factors contributing to the gifted students’ mathematics growth. Non-identified students in the 59 cluster classroom also reported larger academic gains in support of studies by Gentry (1999) and Kulik (1992). Pierce et al. (2011) concluded that gifted students achieve academic gains over time with cluster grouped differentiated instruction as did every non-gifted child. However, gifted students’ math growth in Grade 3 may or may not have been higher given a different instructional model. Pierce et al. supported a growing body of research that cluster grouping promotes student growth but does not answer whether gifted students’ growth is maximized with cluster grouping. Pierce et al.’s (2011) findings for gifted students were mirrored in results by Miller, Latz, Jenkins, and Adams (2012). Gifted students in a classroom cluster-grouped model displayed growth in reading comprehension and evidence of social, affective, and motivational benefits (Miller et al., 2012). However, Miller et al.’s (2012) study suffered the same weakness as Pierce et al.’s (2011) study. The gifted Grade 3 students were not studied with a comparison group representing another instructional model such as homogenous grouping. The benefits of cluster grouping are well documented for gifted children, but research acknowledges that gifted students’ academic gains fail to reach higher levels than the general education population when they are cluster grouped. Van Tassel-Baska et al. (2002) researched grouping models of public school gifted students in 17 districts and in 46 schools in 10 states in Grades 2 through 8 using the William and Mary curriculum designed for gifted learners. Using a pairwise comparison for nonrandomized design and harmonic means, Van Tassel-Baska et al. (2002) found that the grouping models of self-contained, pull-out, and language arts block were each significantly different from that of heterogeneous cluster grouping, which showed the lowest growth measure (Van Tassel-Baska et al., 2002). Moreover, 60 the self-contained, pull-out, and language arts blocks were not significantly different from each other (Van Tassel-Baska et al., 2002). Therefore, Van Tassel-Baska et al. (2002) concluded that a robust curriculum over grouping models provides stronger academic gains for gifted students. Growth gains for gifted learners might be as dependent on the curricula as the grouping model, except for cluster grouping which did not show similar student growth gains as compared to self-contained, pull-out, and language arts block programming (Van Tassel-Baska et al., 2002). Despite the economic and political pressure to serve gifted students in heterogeneous classrooms via a cluster grouping model, Bernal (2003) claimed that gifted students, even in the cluster group model, are underserved because overwhelmed teachers are unable to make any meaningful modifications to the general curriculum to address the needs of a few gifted students. In order to create effective services for gifted students in a cluster model, individualization would need to occur with trained teachers and with a campus staff with limited yearly turnover (Bernal, 2003). Renzulli (as cited in Knobel & Shaughnessy, 2002) agreed with Bernal. In an interview transcription provided by Knobel and Shaughnessy (2002), Renzulli argued that gifted students are and will continue to be underserved without specially trained teachers because gifted students require differentiation. Therefore, cluster grouping represents more of a theoretical framework rather than a realistic solution to full-time gifted programming. The lack of consistency in gifted programming applied through the cluster grouping model, according to Rogers (2002), is that teachers have few resources with which to manage a wide range of student abilities in general education classrooms. Teachers lack resources for creating fluid grouping situations. Additionally, school districts decide for themselves what they believe to be the best grouping options for 61 gifted students based on teachers’ willingness to learn about gifted students, the mission of the district, and demands from the community (Rogers, 2002). This disparity problem undergirds the essential lack of fidelity for gifted and talented student programming best practices across districts, as evidenced by cluster grouping’s mixed research results. Weinebrenner (1992) promoted the cluster grouping model as a means to diminish the elitist attacks against gifted education in homogeneous classrooms. Theoretically, all students would benefit from the same differentiated instructional model. Years later, Hertberg-Davis (2009), responded to Weinebrenner’s research and reported finding a lack of differentiation in heterogeneous classrooms. Hertberg-Davis concluded that differentiation in the regular classroom in a cluster model is not an effective substitute for homogeneous grouping programming and that current instructional practices fail to meet gifted and talented learners’ needs (Hertberg-Davis, 2009). Teacher influence on differentiation in the homogenerous classroom. In an effort to determine teacher influence on the effectiveness of differentiation within the homogeneous gifted classroom, Linn-Cohen and Hertzog (2007) qualitatively examined the differentiation strategies of two teachers in fourth and fifth grade homogeneous selfcontained gifted classrooms at a public elementary school located in California. The two teachers were encouraged to develop individualized, challenging, student-centered, and relevant gifted curriculum (Linn-Cohen & Hertzog, 2007). The opportunity for professional autonomy allowed the teachers to incorporate state standards and be innovators for all instructional decisions (Linn-Cohen & Hertzog, 2007). The two teachers focused on the gifted students’ unique interests and intellectual passions to further differentiate their learning opportunities and to challenge each gifted child (LinnCohen & Hertzog, 2007). 62 Student attitudes collected through qualitative interview data were examined by Linn-Cohen and Hertzog (2007). Students responded about the self-contained gifted setting and demonstrated increased satisfaction and positive responses to heightened academic expectations. One student commented that the program “is challenging, but in a good way; being in GATE challenges me because there is competition and more homework; we don’t spend a lot of time on one thing if we understand it; I don’t get bored a lot” (Linn-Cohen & Hertzog, 2007, p. 254). A follow-up parent survey revealed that 100% of the gifted students’ parents believed that their students’ tasks were supported by a challenging curriculum (Linn-Cohen & Hertzog, 2007). While LinnCohen and Hertzog’s findings were limited as an action research project and not generalizable, the data appeared to support using homogeneous classrooms for gifted students if differentiation actually occurs with enough fidelity. Linn-Cohen and Hertzog also demonstrated that many stakeholders including teachers, students, and parents believed their gifted students benefitted homogeneous grouping. Linn-Cohen and Hertzog’s (2007) findings were expanded with a qualitative study by Hendricks (2009). Hendricks used student focus groups to study the differences in students’ self-efficacy between heterogeneously served gifted students and homogeneously served gifted students. Reported transcripts of the focus group of gifted students served in a homogeneous experimental elementary Grade 3 math classes were: “sometimes you feel behind. It is back and forth. Sometimes you feel on top. If you do feel behind, you can always talk to the teacher” (Hendricks, 2009, p. 67). These comments can be contrasted with the learning experience of the control group served in heterogeneous third grade math classes: “I am ahead of the other students. In fact, all of the other students” (Hendricks, 2009, p. 67). 63 Another small case study by Caldwell (2012) sought to understand whether teacher self-efficacy or teachers’ attitudes toward gifted students better explains their willingness to differentiate instruction with gifted students. Statistically significant results for both efficacy and attitude were predictors of teacher willingness to differentiate instruction with gifted students (Caldwell, 2012). In addition, Caldwell (2012) found that even a positive attitude toward gifted education along with the number of years of teaching experience were poor predictors of providing differentiation in classroom practice. Farrar (2003) showed that high and middle ability students who were cluster grouped produced higher quality academic products. However, Farrar did not include gifted students in the sample. Kaplan (2007) extended the discouraging results on differentiation specifically for gifted students in the heterogeneous classroom by wondering “whether the idea of differentiation is at the point where it has lost its vitality to be a part of the advocacy efforts on behalf of gifted students” (p. 23). Researched guidelines for effective cluster grouping. While cluster grouping, as a model for serving the gifted, is a practical means to an end, maintaining recommendations from past research are believed to increase the model’s effectiveness. First, students should be clustered with their intellectual as well as same-age peers (Bryant, 1987; Delcourt & Evans, 1994; Hoover, Sayler, & Fedlhusen, 1993; McInerney, 1983; Oakes, 1985; Rogers, 1991; Slavin, 1999; Winebrenner, 1992). Secondly, cluster grouping provides for full-time gifted student services without requiring additional programming or staffing (Hoover et al., 1993; LaRose, 1986; Rogers, 1991; Winebrenner & Devlin, 1994). Third, the highest achieving, or highly gifted students, should be removed from general education classrooms so that other general education students can 64 emerge as intellectual leaders (Kennedy, 1989; Winebrenner, 1992). Fourth, the achievement levels within a single classroom are reduced to offer more differentiation opportunities for students at the upper levels of achievement (Coleman, 1995; Delcourt & Evans, 1994; Rogers, 1993). These historical findings have informed the cluster grouping model design of successful heterogeneous classrooms for the gifted as well as this research project’s participating school district. Curriculum that supports gifted students in a cluster grouped classroom. Junior Great Books “constitutes one of the most effective literature programs available for gifted learners” (Van Tassel-Baska et al., 2002, p. 32). It was highly rated as a curriculum exemplar by Aldrich and McKim (1992) in their programming review. This curriculum also offers “strong inquiry-based training programs for teachers with a central focus on improving students’ quality of discourse and enhancing their interest in literature” (Van Tassel-Baska et al., 2002, p. 32). A quality curriculum is essential to the success of a cluster group model. Studies of effective differentiation strategies for gifted learners have focused on: compacting, problem-based learning, inquiry approaches, and independent investigation. Van Tassel-Baska et al. (2002) recommended using the William and Mary curriculum because it “offers a rich set of applied research questions for exploration” (p. 33). Van Tassel-Baska et al. (2002) conducted separate analyses of curricula conducted at the unit level and across units with the William and Mary curriculum and found it can provide significant reading growth for gifted students. Most studies demonstrate positive effects, yet little evidence exists for how the strategies work in consort in the classroom. Few studies have utilized cross comparison studies of the effectiveness of these strategies and 65 how growth rates for gifted learners are maintained over time using this curriculum (Van Tassel-Baska et al., 2002). In summary, cluster grouping may not benefit all gifted students. Highly and profoundly gifted students may be better served in homogeneous classes. Since neither cluster grouping nor homogeneous classes requires additional staffing, school districts that utilize both models, as did the participating school district in order to meet the needs of the widest range of gifted students (Brulles & Winebrenner, 2011). Discussion of Recent Trends “Homogenization of educational experience is advocated primarily as a means to social change; the rush to heterogeneous grouping and cooperative learning for the gifted is probably heavily influenced by these same social and political value systems” (Reis, 2004, p. 84). Reis (2004) warned that the social-political demands that have served gifted students in heterogeneous classrooms may have detrimental effects. Cluster grouping may “detract from achieving what is basic to a quality gifted program, namely acceleration and constant ability grouping” (Reis, 2004, p. 70). “Acceleration and grouping are the lightning rod issues that test the level of endorsement that gifted programs enjoy in a local school district” (Reis, 2004, p. 70). The expanding range of academic readiness in most public schools has exacerbated the ability of teachers to effectively differentiate (Petrilli, 2011). “By the fourth grade, public-school children who score among the top 10 percent of students on the National Assessment of Educational Progress (NAEP) are reading at least six grade levels above those in the bottom 10 percent” (Petrilli, 2011, p. 49). “Even differences between students at the 25th and at the 75th percentile are huge – academic readiness is separated by at least three grade levels” (Petrilli, 2011, p. 49). De-tracking advocates 66 have claimed the victory in the classroom as cluster grouping and within classroom differentiation have gained acceptance while homogeneous grouping programs for the gifted have retreated. Meanwhile, in the classroom, the level of support needed by all students, even the gifted, has risen along with the increasing range of academic readiness. The promise of de-tracking relies on the argument of equity for all students, but the results have been raising the lowest-performing students, and not harming high-achieving students (Petrilli, 2011). Benefits of ability grouping extend to the secondary level. Brewer, Rees, and Argys (1995) analyzed test score results for high school students in tracked and detracked classrooms and found benefits of tracking for the advanced students. Brewer et al (1995) paraphrases those who advocate for de-tracking. De-tracking policy is often based on conventional wisdom that “students in low-track classes (who are drawn disproportionally from poor families and from minority groups) are hurt by tracking while others are largely unaffected” (Brewer et al., 1995, p. 210). However, this conventional wisdom is simply not supported by evidence (Brewer et al., 1995). In a study for the Fordham Institute, Loveless (2009) found a clear pattern that when states adopt accountability reforms, the performance of the lowest quartile students increases, while the achievement of the top 10% of students stagnates or declines. Few incentives are offered to districts at the federal or state level to accelerate the growth of top achieving students. Imberman, Kugler, and Sacerdote (2009) looked at the influx of Hurricane Rita students to Houston Independent School District and the peer effects on student achievement. They concluded, using a non-linear model, that high achieving Houston school natives were significantly and positively affected by high achieving evacuees and significantly and negatively impacted by low achieving evacuees 67 (Imberman et al., 2009). Imberman et al. cited peer effects of benefit when students are grouped with higher achieving peer, calling this effect the “boutique mode” of peer effects (Imberman et al., 2009). The “boutique mode” enables students to do their “best when the environment is made to cater to their type” (Imberman et al., 2009, p. 36). The conclusion is that “high achieving students do particularly well by having high-achieving peers; they are particularly harmed by low-achieving peers” (Imberman et al., 2009, p. 36). Reducing the range of academic readiness in a classroom would benefit gifted students and general education students. Differentiation as an instructional strategy has been advocated within the current political milieu. Yet, that places the possible successes of differentiation squarely on the shoulders of each classroom teacher who experiences an ever widening range of academic readiness. The differentiated model asks one teacher to instruct all students of any number of ability levels and manage multiple small customized clusters of students performing at the same approximate cognitive level in one classroom. Only some teachers are capable of the intensive effort to overcome the inherent difficulty and complexity of implementing a truly differentiated classroom (Petrilli, 2011). Assessing Gifted Students’ Growth with MAP Measuring gifted students’ growth is challenging since many normed assessments have had few gifted students as part of the total population pool and matched those gifted students’ performance with true comparison groups (McCoach et al., 2012). Gifted students may also perform in the high achieving range at the outset. “Regression to the mean, or the tendency for those with extreme initial scores to score closer to the same score on subsequent assessment, can bias growth measures, underestimating the growth of high achieving students” (McCoach et al., 2012, p. 61). This may be more pronounced 68 with gifted students who tend to score substantially above the mean (McCoach et al., 2012). “The amount of measurement error in gifted students’ scores is higher than the amount of measurement error in scores that are closer to the mean of the test” (McCoach et al., 2012, p. 61). Lohman and Korb (2006) agreed with McCoach et al. about the tendency of regression to the mean for top ability students. In the longitudinal study, Lohman and Korb found that the majority of students who scored in the top few percentiles (96% and above) on the Iowa Test of Basic Skills, an achievement test commonly used to identify gifted students in Grade 1, did not score in the same range by Grade 3 (Lohman & Korb, 2006). This tendency to score somewhat lower on a subsequent assessment is an example of regression to the mean and is more prevalent among gifted students than among students of the general population (Lohman & Korb, 2006). Adaptive tests provide more precise measurements for making individual decisions about high performing gifted students when compared with fixed-form alternatives (NWEA, 2012). Students’ adaptive testing scores are less likely to suffer from regression to the mean. Students within the same class often perform at different grade levels, and educators face the challenge of ensuring that every child experiences academic progress (NWEA, 2012). Academic growth should be expected for all students, including gifted and talented students. MAP scores of students’ academic growth over time contribute to the understanding of student academic progress. When discussing outcomes of gifted education programming, educators can see precisely how much academic growth has occurred with participating students (NWEA, 2012). Growth models, rather than achievement assessments, capture student achievement over time and can be used to evaluate both teacher and program effectiveness (NWEA, 2012). 69 Reading skills, in particular, tend to increase in the early elementary years (NWEA, 2012). However, the sharp upward growth levels off by the end of Grade 3. Therefore, according to N. Jensen, a NWEA research scientist in a personal communication on November 2, 2014, measuring academic growth for Grade 3 allows the researcher to better evaluate programming and the effects of the classroom (N. Jensen, personal communication, November 3, 2014). Below Grade 3 progress measures cannot always be attributed to teacher effectiveness because the learning to read process is highly accelerated, especially for gifted students. Therefore, measuring growth in Grade 3 reading is the most appropriate method and year in which to evaluate gifted students (N. Jensen, personal communication, November 3, 2014). Northwest Evaluation Association (NWEA, 2012) has the ability to measure a student’s academic growth across time. From data provided through MAP reports, educators can compare performances on specific reading skills (NWEA, 2012). In addition, using MAP’s growth model, educators can compare a gifted student’s performance against their expected performance. MAP assessments are computer adaptive and provide a useful tool to evaluate grade-independent academic progress (NWEA, 2013). The assessment dynamically adjusts to the performance level of each student by providing test items that are challenging even as that performance reaches above the student’s grade level. MAP is unlimited in terms of how difficult the items become to determine an individual student’s reading level. Because every MAP test item is anchored to a vertically-aligned equalinterval scale, MAP is an appropriate progress measurement tool to use with gifted students who typically outperform grade-level peers on criterion-referenced tests as well as on tests with grade level ceilings (NWEA, 2012). 70 The academic progress of gifted students at any grade level may be different than the general population. This issue was raised by McCoach, Rambo, and Welsh (2012) who debated the best statistical method to gauge gifted students’ academic growth. McCoach et al. (2012) cautioned that using even well-developed growth measures, such as MAP, can lead to erroneous conclusions. Such growth measures assume consistent growth across time and according to a standardized index. Acknowledging the many psychological and academic variables, it may be impossible to determine actual academic growth (McCoach et al., 2012). Before Grade 3, yearly academic gains measures are especially difficult to interpret because “reading skills tend to increase sharply in the early grades, when students are learning to read; then reading growth slows in later elementary grades” (McCoach et al., 2012, p. 57). This provides additional support for the Grade 3 formative progress measure used in this research project. In a research study of gifted student growth using MAP assessments, McCoach et al. (2012) hypothesized that gifted students grow less quickly during the school year than the average intelligence student. McCoach et al. also postulated that measurement issues, such as regression to the mean, conditional errors of measurement, ceiling effects, etc., affect gifted students on academic measurements. They discovered gifted Grade 3 students grew one-half of a point less than average students over the same time period. McCoach et al. concluded that gifted students had more reading growth preceding the Grade 3 school year than general education students. These authors used the MAP assessment, but they compared incoming Grade 3 gifted students to general education students which will not be the case for this current study. The current study as an action research project only used gifted students’ MAP reading growth measures served through 71 two different grouping models in the participating school district. Both comparison groups were identified gifted along the IQ continuum. Duffett, Farkas, and Loveless (2008) used national existing data obtained from the National Assessment of Educational Progress (NAEP) and discovered that Grade 8 high achieving students showed significantly less yearly gains. The average academic growth for these Grade 8 high achieving students generated only three points higher over the academic year versus the gains made by low achieving students at 16 points and higher over the academic year (Duffett et al., 2008). Duffet et al. concluded that high achieving students need to be addressed in the data as a separate cohort which this research project will do. Gifted education has labored under the misconception that accelerating gifted children too early may diminish the ability to maintain high academic performance throughout their academic career. Xiang et al. (2011) sought to answer this criticism: “Do students who outscore their peers on standardized achievement tests remain at the top of the pack year after year?” (p. 1). Xiang et al. used MAP achievement scores of students tracked from Grade 3 to Grade 8 and defined high achievers as students who scored in the 90th percentile or above. Xiang et al. used data from 4,800 school districts and approximately five million students and reported the following four major findings: 1. A majority of high flyers maintained their status over time, but substantial numbers lost altitude. Nearly three in five students identified as highachieving in the initial year of the study remained high-achieving in the final year with 55.9% doing so in reading. Roughly 30 to 50% of initial highachieving students lost their top-tier academic status over time 2. Students who did not maintain their high flying status didn’t fall far 72 3. High flyers grew academically at similar rates to low and middle achievers in math, but grew at slightly slower rates than low and middle achievers in reading. In reading, low and mid-achieving students demonstrated faster rates of improvement than high achievers 4. High-achieving students attending high-poverty schools made about the same amount of academic growth over time as their high-achieving peers in lowpoverty schools. The relationship between a school’s poverty rate and the growth of its highest-achieving students is weak (Xiang et al., 2011) Xiang et al. (2011) showed that the greatest movement, whether rising or descending, occurred between the third and fourth grades and between the seventh and eighth grades. In addition, “in reading, low and middle achieving students demonstrated faster rates of improvement than high achievers” (Xiang et al., 2011, p. 12). The ceiling effects of high achievement were relieved by the “adaptive nature of the MAP test where high-performing students received more items targeted to their current achievement levels than they would receive on a fixed-form assessment” (Xiang et al., 2011, p. 13). Annual growth in reading in Grade 3 using an exclusive gifted cohort, as this research project does, will mitigate the larger growth rate means observed with middle and low achieving students. In addition, using an annual formative progress measure, rather than a single achievement data point, will allow for the measurement of smaller nuances in change over time than a simple difference in achievement which may be smaller for gifted students of high ability. In the discussion, Xiang et al. (2011) postulated that the slowing of reading scores from elementary to middle school might possibly result from the acquisition of subject specific reading skills beyond elementary level. By middle school, the specialized 73 reading by many high achieving students becomes increasingly more subject specific rather than generalized. Xiang et al. recommended that students performing at or above the 90th percentile by Grade 3 need access to gifted programming, and the educational setting itself may need to maintain students’ growth over time. MAP supports use of the true gain score model, a growth measure that defines how much learning has occurred in the intervening time measure by the difference between test scores (NWEA, 2012). The resulting growth measure offers a direct measure of how much a student has progressed over a given time period. With MAP assessments, a student’s test score from one testing timeframe may be compared to their test score in the subsequent testing time frame (NWEA, 2012). The resulting gain score is called an absolute measure of the growth. While most gifted students achieve at the proficient and above proficient level on state assessments, few state assessments measure or indicate students’ academic growth. High achievement may not indicate academic growth especially prevalent among the gifted. Current models of growth focus on the gains students attain over a year or over multiple years. “A positive growth index value [on MAP] indicates how far above the norm the student’s growth is, a negative growth index value indicates how far below the norm the student’s growth is, and a zero growth index value indicates that the student’s growth is consistent with the norm group” (NWEA, 2013, p. 15). The norm group is determined by the student’s initial score on the Grade 3 reading assessment. The Bill and Melinda Gates Foundation commissioned a report in collaboration with Battelle for Kids to guide education in selecting growth measures (Battelle for Kids, 2011). Both simple and value-added growth models were reviewed. A simple growth model uses two or more data points to describe the difference between two or more 74 achievement test scores for the same student without any assumptions about influence or causation (Battelle for Kids, 2011). A value-added model attempts to adjust for influences of schools or teachers on academic growth (Battelle for Kids, 2011). The MAP assessment is a simple growth model. Advantages of MAP’s simple growth model relates to higher reliability. However, a simple growth model cannot account for factors that may influence the growth scores such as teachers, campus specific variables, emotional or social factors, etc. (Battelle for Kids, 2011). Growth models that measure student gains may help identify effective educator practices and provide evaluative data for academic programming (Battelle for Kids, 2011). Three criteria to be used when making decisions on assessment instruments for a growth measure are: valid and reliable, aligned to curriculum or standards, and offer sufficient stretch to measure advanced students (Battelle for Kids, 2011). The U.S. Department of Education (2013) specifically chose to use MAP because of its wisely used and available systems for assessment in determining the amount and effectiveness of classroom instruction and differentiation. The U.S. Department of Education study included 32 elementary schools in five Illinois school districts. The school districts participated in a two-year randomized controlled trial to assess the effectiveness of the MAP program and to offer teachers effective data to guide differentiation and student progress measurement (U.S. Department of Education, 2013). The results reported were that “teachers implemented MAP data programming with moderate fidelity and were not more likely than a control group of teachers to apply differentiated instructional practices” (U.S. Department of Education, 2013, p. xi). MAP programming did not have a statistically significant impact on students’ reading achievement for either Grade 4 or Grade 5 (U.S. Department of Education, 2013). The 75 results of the U.S. Department of Education study reflected those of differentiation in general: Few teachers implement differentiation with fidelity to improve student outcomes. A topic not often discussed regarding growth measures is the after effects of prior educational experiences on student achievement. Prior background and experience may affect students’ achievement scores, and little research has yet to be done on how student learning from a previous year impacts students’ current year gains or how long the growth effect of a prior year lasts (Battelle for Kids, 2011). Nonetheless, growth measures allow gifted students’ academic progress to be compared between students of similar or different achievement and abilities (Battelle for Kids, 2011). Summary of Literature Review In conclusion, the inherent democratic tension between excellence and equity in the classroom is far from resolved. In the politically charged educational environment, gifted educators have been urged to embrace the inclusive model with cluster grouping and a reliance on differentiation of both curriculum and instruction. Grouping practices for all students are under scrutiny as collaborative learning, as an outcome of schooling, is viewed as part of 21st century pedagogy along with increasing opportunities for student creativity and use of problem-based and inquiry-based learning. Teachers are asked to personalize learning for a larger range of abilities in the general education classroom. The current data driven models for educational success requires measuring student learning through grade level standardized testing, a testing program that limits measuring the progress in achievement of gifted students. Gifted programming models that previously offered homogeneous grouping through pull-out programs or full-time programs have been criticized as elitist, even 76 though the research results comparing homogeneous and heterogeneous grouping models as best practices for gifted students remain inconclusive. While the state of Texas supports gifted education and offers limited funding, it does not endorse a particular evidence-based program model. Therefore, districts have extended flexibility to construct program models that best suit the needs of their gifted and talented students and their communities. Rationale for Action Research The current study as an action research project was conducted to add to the body of research by looking at two grouping models for gifted students within the same district using annual growth in Grade 3 reading from MAP scores. Measuring student annual growth largely based on the difference in grouping might answer the programming decision for the participating school district and may inform others when designing their gifted programs. The lack of research directly comparing homogeneous and heterogeneous gifted student grouping offered an opportunity for the current study to add to the research and facilitate educational decisions leading to successful gifted student outcomes. Recommendations for measuring gifted students’ growth by the NAGC (2009) were followed in the current study based on the participating school district’s gifted and talented programming models:   Growth models need to reflect growth beyond proficiency. The term growth models should be clearly defined as measurement of academic success on the basis of how much student achievement improves and should be based on individual student gains. In its simplest form a student’s previous scores are used to create predicted scores for a given 77 year. The difference between the actual scores and predicted score is their growth score. (p. 2) 78 CHAPTER 3. METHODOOLOGY Introduction The purpose of the current study as a quantitative action research project was to examine academic growth differences by grouping of gifted and talented students. The participating school district’s gifted and talented students participated in one of two programming models based on identification criteria: homogeneous or heterogeneous classroom assignments. The differences were tested using the gifted and talented students’ Grade 3 reading Measures of Academic Progress (MAP) scores from the 20132014 academic school year. Two research questions were answered in the current study. Research Question 1 (RQ 1) Do Grade 3 gifted students enrolled in the homogenous elementary Leading Exceptional Aptitude and Performance (LEAP) reading program show different yearly academic growth, the dependent variable, measured by the MAP reading assessment than Advancing Creativity in Education (ACE) gifted students housed in 23 separate elementary schools in a heterogeneous cluster model that relies on classroom enrichment? Research Question 2 (RQ2) Do Grade 3 ACE program gifted students enrolled in 23 elementary schools show different growth rates, the dependent variable, from the fall to spring MAP scores by elementary school? 79 This chapter provides the research design, target population and sample, instrumentation, hypotheses, data collection, procedures, and data analysis. Research Design This research project was a quantitative causal-comparative study. The researcher sought to measure and compare student annual reading growth on the dependent variable, the conditional growth index (CGI) MAP yearly growth index in Grade 3 reading based on the independent variable, gifted student enrollment in either the LEAP or ACE program. These two groups possessed the similar characteristic of giftedness identified by the participating school district but in differing levels of giftedness. Highly gifted students were homogeneously grouped in the LEAP program and housed on a single campus. Gifted to moderately gifted students were cluster grouped on their home campuses in heterogeneous classrooms. Caution was used interpreting results as causation is difficult to infer. The research design was a quantitative action research project of a suburban school district’s gifted students. The participating school district’s data regarding its gifted and talented students’ growth rates for Grade 3 reading were utilized. To examine the gifted and talented students’ yearly academic growth differences by grouping, the 2013-2014 academic school year’s MAP scores for Grade 3 reading were tested for differences between students in the homogeneous LEAP programming and students in the heterogeneous ACE grouping. The common variance was the students’ status as gifted and talented in the participating school district. Setting The research project district was classified as a major suburban district and was contiguous to a major urban district located in the state of Texas. The student population 80 for the 2012-2013 school year was 26,325. The ethnic distribution of the district in 20122013 was 16.3% African American, 53.8% Hispanic, 17.2% White, 0.4% American Indian, 10.9% Asian, 0.1% Pacific Islander, and 1.3% two or more races (Texas Education Agency, 2013). The participating school district’s student population in 20122013 included 62.5% economically disadvantaged, 37.5% non-economically disadvantaged, 24.0% English language learners (ELL), and 45.8% considered at-risk (Texas Education Agency, 2013). The total gifted and talented population for 2012-2013 was 2,113, or 8.0%, of the total district population (Texas Education Agency, 2013). Target Population and Sample Grade 3 is pivotal in a child’s education because it is the year that simply decoding words transfers to informational texts (Wennersten, 2012). Therefore, the grade targeted was used to best represent the problem of measuring the nuanced growth of gifted students’ reading abilities. The sample of 19 LEAP Grade 3 students were served at a single elementary school. The sample of 138 ACE Grade 3 students were cluster grouped in 23 different elementary schools throughout the district. The small size of the LEAP group and the nature of the CGI as a variable that allowed for weighting to account for sample size biases. Therefore, neither random sampling nor equal comparison groups were recommended for the current study as action research (Northwest Evaluation Association [NWEA], 2013). Instrumentation and Measures The NWEA (2012) provides a measure of student academic growth across time via MAP reports. From data provided through MAP reports, educators can compare specific skill growth such as reading across the assessment data points from fall to spring. 81 Using MAP’s growth model, gifted and talented students’ actual performance can be compared with their expected performance. MAP assessments are computer adaptive and provide a useful tool to evaluate grade-independent academic growth (NWEA, 2013). The assessment dynamically adjusts to the performance level of each student by choosing items that are moderately challenging and at or above the student’s grade level. MAP is unlimited in terms of how difficult the items can become to determine an individual student’s ability level. Hence, MAP is an appropriate measurement tool for gifted and talented students who typically outperform their grade-level peers on criterion-referenced tests or on tests with low ceilings (NWEA, 2012). Every MAP test item is anchored to a vertically-aligned, equalinterval scale (NWEA, 2012). The MAP scores are established with a normed Rasch unit known as a RIT score (NWEA, 2012). The RIT score is an estimation of a student’s instructional level on an equal interval scale. Responses to items on a given student’s reading test are used to generate the student’s final RIT score. The numerical RIT score value represents the level of test item difficulty at which the student is capable of answering correctly approximately 50% of the time in comparison to other students of the same grade and at the same time of year (NWEA, 2012). The NWEA’s norms are based on grade-level samples from 20,000 students per grade (NWEA, 2012). The norming sample was randomly drawn from a pool of 5.1 million MAP-taking students representing over 13,000 schools in more than 2,700 school districts in all 50 states (NWEA, 2012). Rigorous post-stratification procedures were used to maximize the degree to which the growth norms represent the U.S. school-age population (NWEA, 2012). However, while RIT scores are expected to increase with 82 each test administration, “younger students show more growth in one year than older students, and students who test above grade level often show less growth” (NWEA, 2013, p. 25). Growth norms for reading RIT scores were recently updated in 2011 by NWEA. The 2011 norms enable flexible interpretations of academic growth. Typical academic growth is based on a student’s initial score and may be determined for any number of instructional weeks when using two different test intervals within a 12-month period. This flexibility allows educators to test students at pre-determined times (NWEA, 2012). Regardless of when a district offers MAP testing, the data aligns with norm-referenced interpretations and are consistent with the chosen test schedule (NWEA, 2012). The RIT scale is continuous across grades, making it ideal for tracking students’ academic growth over time and especially for tracking the growth of gifted students who are likely to demonstrate high achievement on academic assessments. With MAP assessments, students’ fall to spring gain scores may be compared with the average fall to spring gains made by students who share the same fall scores. The MAP reading assessment covers skill-based goal areas. Reading goal areas include word recognition, structure and vocabulary, and reading informational texts (NWEA, 2012). The conditional growth measure index indicates how far above or below the norm a student’s academic growth falls. A zero-growth index value indicates that academic growth is consistent with the norm group’s growth. A negative growth index value indicates that the student’s academic growth falls below the norm group. The MAP growth measure can show progress for all students, including high achieving students. MAP uses “all longitudinal data to measure growth between two points in time regardless of whether a student performs below, at, or above grade level” (NWEA, 2013, p. 51). 83 The high achievement scores from gifted students do not necessarily indicate academic progress but growth measures do. A higher ceiling in an assessment, such as MAP, is useful in assessing gifted students because it includes a sufficient number of above grade level and advanced knowledge questions (NWEA, 2013). In July of 2014, NWEA published the MAP’s Texas state alignment for reading. Therefore, the MAP assessment for Grade 3 reading is a valid instrument to measure yearly academic growth and progress for the participating school district’s Grade 3 gifted and talented students. “Each child should make a year’s growth for a year’s worth of instruction,” including gifted students (Sheldon, 2012, p. 12). Yearly academic gains are determined from a normative sample for that same student in the same grade so that each student is measured against his or her own starting level instead of a fixed minimum score (Sheldon, 2012). Growth measures offer a way to indicate whether individual gifted students are being served academically (Sheldon, 2012). Duffett et al. (2008) suggested that struggling students receive more of their teachers’ attention which leads to “high achievers making significantly less progress in reading than low achievers (e.g., +3 vs. +16 points at the fourth-grade level and +3 vs. +16 points at the eighth-grade level)” (p. 7). The MAP student academic growth summary report compares an individual student’s performance to growth norms using the NWEA 2011 norm standards using the CGI (NWEA, 2013). The CGI is the value that represented the dependent variable for the current research project. In a personal communication on November 3, 2014, N. Jensen, a NWEA research scientist, reported that the CGI is a normative growth metric and standardized index score. The CGI is a valid calculation that normalizes or standardizes scores so that a small group of students, such as those in the LEAP group in 84 this research project, and a larger group, such as the students of the ACE group, can be compared through statistical tests of mean differences (N. Jensen, personal communication, November 3, 2014). The CGI was designed for comparing students across grades, demographics, and different points along the achievement distribution. The CGI is more robust than the simple growth index statistic because a student’s CGI is based on an initial RIT score that is then compared with similar RIT scoring peers (N. Jensen, personal communication, November 3, 2014). The CGI mean, median, and mode are 0, so the CGI is effectively a standardized z score (NWEA, 2012). If a CGI is a +1, then the value is above the projection for the student’s growth. Likewise, if the CGI is -1, then the value is below the student’s projected growth. Data Collection The initial fall MAP data were collected by the district before the end of the first nine weeks of the academic year. The spring MAP data were collected by the district during the last nine weeks of the academic year. These points of collection represented the academic growth in one school year for the gifted students in the participating school district. The district administered the MAP via computer. Students sat at monitors in the campus computer lab to complete the test; when the test was completed, students pressed submit. The raw answers populate a database at NWEA. NWEA compiles the results for each student electronically and returns the results file to the district. The district distributes results to the students’ teachers. The 2013-2014 Grade 3 gifted students’ data were retrieved through the participating school district’s secured MAP electronic database. First, the data were organized using an EXCEL spreadsheet. The data identified students as LEAP Grade 3 85 students served at a single elementary school and as ACE Grade 3 students cluster grouped in 23 different elementary schools throughout the district. ACE student data included the elementary schools attended. Confidentiality was ensured by removing all identifying data, such as name, from each student’s line of data. The data were secured on a password-protected computer. The current study, as designed, was approved by the participating school district. The current study’s population included all Grade 3 students identified and served as gifted and talented by the participating school district. The Grade 3 student population included 19 LEAP students and 147 ACE students. LEAP Grade 3 students were served at a single elementary campus and were homogeneously grouped. ACE Grade 3 students were heterogeneously cluster grouped and served at their neighborhood elementary campuses. The number of ACE identified students varied at each of the 23 ACE-serving elementary campuses. The ACE and LEAP students who took the MAP growth assessments in the fall and spring of 2013-2014 formed the research project sample of 157 total students. Procedures and Data Analysis The first independent variable was dichotomous and categorical as LEAP or ACE gifted student grouping. The second independent variable was the elementary campus at which the ACE students were enrolled. The dependent variable was the CGI for the assessment administrations, identical for both ACE and LEAP student groups. In order to reject any of the null hypotheses, statistical significance required a p-value less than the .05 level. Effect size was measured by eta according to Cohen (1988). The hypotheses H10, H1, H20, and H2 were tested as part of answering the two RQs in the current study. The first null hypothesis addressed differences regarding Grade 86 3 gifted students enrolled in the homogeneous elementary LEAP reading program and ACE gifted student’s housed in 23 separate elementary schools via μLEAP = μACE. The MAP reading assessment’s CGI was used for the independent t test between LEAP and ACE students’ results. If the independent t test yields statistical significance, the Cohen d test of effect size was determined. The following null and alternative hypotheses were used to answer the first research question which asked if Grade 3 gifted students enrolled in the homogenous elementary LEAP reading program show different yearly academic growth measured by the MAP reading assessment than ACE gifted students housed in 23 separate elementary schools in a heterogeneous cluster model that relies on classroom enrichment: H10: Grade 3 gifted students enrolled in the homogeneous elementary LEAP reading program show no differences in yearly academic growth measured by MAP reading assessment than ACE gifted student’s housed in 23 separate elementary schools in a heterogeneous cluster model that relies on classroom enrichment, μLEAP = μACE. H1: Grade 3 gifted students enrolled in the homogeneous elementary LEAP reading program show differences in yearly academic growth measured by MAP reading assessment than ACE gifted student’s housed in 23 separate elementary schools in a heterogeneous cluster model that relies on classroom enrichment, μLEAP ≠ μACE. The second null hypothesis addressed differences between Grade 3 ACE program gifted students’ growth rates from the fall to spring MAP scores by the 23 elementary schools, μ1 = μ2 = . . . . = μ23. The MAP reading assessment’s CGI was used for the oneway analysis of variance (ANOVA) between the 23 ACE elementary programs. Prior to 87 conducting the ANOVA, a Levene's test for equal variances was conducted. If the ANOVA yielded a significant main effect and the Levene’s test yielded equal variances, the post-hoc Tukey for fixed effects was planned to be applied to the data. However, if the variances were not equal, the Welch post hoc test was planned to be used instead. The eta statistic was used as an indicator of effect size as well. The following null and alternative hypotheses were used to answer the second research question which asked if Grade 3 ACE program gifted students enrolled in 23 elementary schools show different growth rates from the fall to spring MAP scores by elementary school: H20: Grade 3 ACE program gifted students enrolled in 23 elementary schools show no differences in growth rates from the fall to spring MAP scores by elementary school, μ1 = μ2 = . . . . = μ23. H2: Grade 3 ACE program gifted students enrolled in 23 elementary schools show differences in growth rates from the fall to spring MAP scores by elementary school, μ1 ≠ μ2 ≠ . . . . ≠ μ23. Limitations This research project’s findings might not be representative of other districts and may not be generalizable. Gifted students were uniquely identified and served at each district. Student demographics might limit the current study as the participating school district represents a suburban area with low socioeconomic status and high minority populations. The results might be limited by differences between the smaller size of LEAP student sample located at a single elementary school and the larger number of ACE program students served through cluster grouping at 23 elementary schools. 88 Ethical Considerations Student names were not identified in the data for the research project. No campuses or teachers were identifiable in the data. All personal data were kept confidential. The data were held secured on a password-protected computer. Permission was granted to the researcher by the participating school district. The researcher also received approval from the Dallas Baptist University’s Committee on the Protection of Human Participants. 89 CHAPTER 4. RESULTS The purpose of the current study as a quantitative action research project was to examine academic growth differences by grouping of gifted and talented students. The participating school district’s gifted and talented students participated in one of two programming models based on the identification criteria of homogeneous or heterogeneous classroom assignments. This current study was quantitative and causalcomparative for measuring and comparing students’ academic growth on the dependent variable of the Measures of Academic Progress (MAP) conditional growth index (CGI) for Grade 3 reading. The independent variable was a gifted students’ enrollment in either the Leading Exceptional Aptitude and Performance (LEAP) or the Advancing Creativity in Education (ACE) program as well as based on enrollment in ACE at one of 23 elementary school campuses. This chapter provides the results of the data analysis and hypothesis tests for the research questions (RQ) following a description of the variables. Description of the Variables First, the MAP scores were based on the normed Rasch unit known as a RIT score (Northwest Evaluation Association [NWEA], 2012). The RIT score was used as an estimation of the gifted and talented students’ instructional levels on an equal interval scale. The numerical RIT score value represented the levels of test item difficulty the gifted and talented students were capable of answering correctly for approximately 50% of the time in comparison to other students of the same grade and at the same time of year (NWEA, 2012). 90 Second, the CGI, or the dependent variable for the current research project, was calculated for each student. The CGI was used as a standardized index to normalize or standardize scores so that the LEAP group and the larger ACE group could be compared through statistical tests of mean differences (N. Jensen, personal communication, November 3, 2014). The CGI was more robust than the simple growth index statistic because it is based on an initial RIT score and compared with similar RIT scoring peers (N. Jensen, personal communication, November 3, 2014). The CGI mean, median, and mode are 0, so the CGI is effectively a standardized z score (NWEA, 2012). If a CGI is a +1, then the value is above the projection for the student’s growth. Likewise, if the CGI is -1, then the value is below the student’s projected growth. The sample of ACE and LEAP students was 157. The number of ACE students in the sample was 19, and the number of LEAP students in the sample was 138. The CGI was the dependent variable for which normalcy was needed to conduct the tests of the hypotheses. For the CGI scores, the skewness statistic was .275, and the kurtosis statistic was -.169. These two statistical absolute values were close to zero; therefore, the distribution of the CGI scores was treated as a normally distributed set of data. The means, medians, modes, and standard deviations for the Grade 3 RIT reading scores and the CGI values for Fall 2013 and Spring 2014 are provided in Table 6. Figure 1 displays the histogram of the CGI values over laid by the normal curve to display the normalcy of the dependent variable for hypothesis testing purposes. Results for Research Question 1 The first RQ was the following: Do Grade 3 gifted students enrolled in the homogenous elementary LEAP reading program show different yearly academic growth, the dependent variable, measured by the MAP reading assessment than ACE gifted 91 students housed in 23 separate elementary schools in a heterogeneous cluster model that relies on classroom enrichment? The first null hypothesis tested for differences regarding Grade 3 gifted students enrolled in the homogeneous elementary LEAP reading program and ACE gifted student’s housed in 23 separate elementary schools, μLEAP = μACE. The MAP reading assessment’s CGI was used for the independent t test between LEAP and ACE students’ results. An assumption of the t test was that the distribution of CGI values was normal. Based on the skewness and kurtosis statistical values being close to zero and the histogram showing a normal distribution of scores presented with the data, the CGI was assumed to represent the normal distribution. Table 6 Descriptive Statistics for the Sample of 157 Gifted and Talented Students Difference in RIT Scores from Fall to Spring Statistic CGI Fall RITs Spring RITs Mean .371 208.76 217.78 9.02 Median .300 208.00 218.00 8.00 Mode -.260 215 218 0 Std. Deviation 1.192 10.134 9.61 6.75 Minimum -3.000 176 179 -7.00 Maximum 3.000 236 245 26 The first research question asked if Grade 3 gifted students enrolled in the homogenous elementary LEAP reading program showed different yearly academic growth measured by the MAP reading assessment than heterogeneously grouped ACE gifted students housed in 23 separate elementary schools. The null and alternate hypotheses for the first research question were: 92 H10: Grade 3 gifted students enrolled in the homogeneous elementary LEAP reading program show no differences in yearly academic growth measured by MAP reading assessment than ACE gifted student’s housed in 23 separate elementary schools in a heterogeneous cluster model that relies on classroom enrichment, μLEAP = μACE. H1: Grade 3 gifted students enrolled in the homogeneous elementary LEAP reading program, one category of the independent variable, show differences in yearly academic growth measured by MAP reading assessment, the dependent variable, than ACE gifted student’s housed in 23 separate elementary schools in a heterogeneous cluster model that relies on classroom enrichment, μLEAP ≠ μACE. Figure 1. Histogram of CGI values for the gifted and talented student sample. The ACE and LEAP students were separated into two groups to test the null hypothesis. The two groups’ CGI means, medians, and standard deviations appear in 93 Table 7. The means and standard deviations for the fall and spring RIT scores for the ACE and LEAP students appear in Table 8. Table 7 CGI Descriptive Statistics for ACE and LEAP CGI Group n M SD ACE 138 .406 1.189 LEAP 19 .122 1.211 Table 8 RIT Score Descriptive Statistics by ACE Versus LEAP Grouping ACE M Spring RITs Difference in RIT Scores from Fall to Spring 207.37 216.51 9.14 138 138 138 SD 9.548 9.126 6.770 M 218.84 227.00 8.16 19 19 19 8.604 7.965 6.760 n LEAP Fall RITs n SD Before reading the t test result, the Levene’s statistic for the equality of the variances between the two groups had to be assessed. The Levene’s statistic was not significant, F = .031, p= .86, and the variances of the ACE and LEAP groups were assumed to be equal. The t statistic for the test was .974, and the degrees of freedom (df) were 155 because of the sample size of 157 minus two groups. The p value for the test 94 was .332 and much larger than .05, the significance level required in Chapter 3. The p needed to be less than .05 to provide 95% confidence that the two groups generated different CGIs. However, the p indicated the probably of error to be 33%, which was too high for differences to occur beyond chance. These results appear in Table 9. Figure 2 displays the comparison of the data points for RIT scores between fall and spring by ACE and LEAP groupings. The ACE and LEAP groups did not display a significant difference for their CGI values measuring their fall to spring reading growth. Because the independent t test failed to yield statistical significance, the researcher failed to reject the null hypothesis. The Cohen d effect size was small at .237, further supporting the failure to reject the null hypothesis. Table 9 Independent Samples Test for Hypothesis 1 Levene's Test F p t test t df 95% CI p M Diff. SE Diff. Lower Upper Equal variances assumed .031 .860 .974 155.00 .332 .284 .292 -.292 .860 Equal variances not assumed .961 23.04 .347 .284 .296 -.328 .896 Results for Research Question 2 The second RQ was the following: Do Grade 3 ACE program gifted students enrolled in 23 elementary schools show different growth rates, the dependent variable, from the fall to spring MAP scores by elementary school? The second null hypothesis tested for differences between Grade 3 ACE program gifted students’ growth rates from the fall to spring MAP scores by the 23 elementary schools, μ1 = μ2 = . . . . = μ23. The 95 MAP reading assessment’s CGI was used for the one-way analysis of variance (ANOVA) between the 23 ACE elementary programs. The following null and alternative hypotheses were used to answer the second research question which asked if Grade 3 ACE program gifted students enrolled in 23 elementary schools show different growth rates from the fall to spring MAP scores by elementary school: Figure 2. Comparison of the data points for RIT scores between fall and spring by ACE and LEAP groupings. H20: Grade 3 ACE program gifted students enrolled in 23 elementary schools show no differences in growth rates from the fall to spring MAP scores by elementary school, μ1 = μ2 = . . . . = μ23. H2: Grade 3 ACE program gifted students enrolled in 23 elementary schools show differences in growth rates from the fall to spring MAP scores by elementary school, μ1 ≠ μ2 ≠ . . . . ≠ μ23. The ACE students were tested by school attended to test the null hypothesis by ANOVA. The 23 schools’ means, medians, and standard deviations appear in Table 10. 96 Prior to conducting the ANOVA, a Levene's test for equal variances was conducted. The Levene’s test of equality of variances for the 23 schools generated statistical significance, F (22/115) = 1.912, p = .015, because the p-value was smaller than .05. Therefore, the variances were not considered equal for post hoc testing. However, the ANOVA did not yield statistical significance between the 23 schools. Table 10 Descriptive Statistics by ACE Campus School M SD n School 1 -.0375 .89444 4 School 2 1.0650 1.37569 6 School 3 .0643 .45107 7 School 4 .0560 .72414 5 School 5 .2280 .75539 5 School 6 .7250 1.33643 2 School 7 -.2986 .65552 14 School 8 -.2460 1.02539 5 School 9 .6183 .33855 6 School 10 .8988 1.36988 8 School 11 1.0750 .75067 4 School 12 .4808 1.09982 12 School 13 .7133 1.03925 3 School 14 .8417 1.10162 6 School 15 1.0350 1.21601 6 School 16 .2325 1.28165 4 School 17 -1.1200 .72125 2 School 18 .8588 1.58094 8 School 19 -.3900 2.04607 4 School 20 -1.3150 .75660 2 School 21 .8357 1.83665 7 97 School 22 .3350 .75284 10 School 23 .8613 1.64553 8 Total .4057 1.18919 138 The ANOVA results appear in Table 11. The F-value was 1.36 with the df = 22, p = .15. The p was larger than .05, and the researcher failed to reject the null hypothesis for yearly reading growth between the 23 ACE groups. The effect size eta-squared was calculated as part of conducting the ANOVA, but was small at .206, further supporting the failure to reject the null hypothesis. Because the ANOVA showed no significant main effect, the Levene’s test yielding unequal variances did not affect post hoc testing. Consequently, the Welch post hoc test was not used. Table 11 ANOVA Results for Hypothesis 2 Test Between the 23 ACE Campuses Source SS df MS F p Partial η2 Corrected Model 40.003a 22 1.818 1.360 .150 Intercept 11.267 1 11.267 8.428 .004 .068 ACE Campus 40.003 22 1.818 1.360 .150 .206 Error 153.739 115 1.337 Total 216.450 138 Corrected Total 193.741 137 a 2 .206 2 Note. R = .206, Adjusted R = .055. Summary The purpose of the current study as a quantitative action research project was to examine differences in academic growth based on the grouping of gifted and talented students. The participating school district’s gifted and talented students participated in one of two programming models: homogeneous LEAP or heterogeneous ACE classroom 98 assignments. The first null hypothesis was: Grade 3 gifted students enrolled in the homogeneous elementary LEAP reading program show no differences in yearly academic growth measured by MAP reading assessment than ACE gifted student’s housed in 23 separate elementary schools in a heterogeneous cluster model that relies on classroom enrichment, μLEAP = μACE. It was tested using the independent t test and was not rejected. Students in the LEAP and ACE groups showed similar growth in Grade 3 reading. The second null hypothesis was: Grade 3 ACE program gifted students enrolled in 23 elementary schools show differences in growth rates from the fall to spring MAP scores by elementary school, μ1 ≠ μ2 ≠ . . . . ≠ μ23. It was tested by ANOVA and was not rejected. All 23 ACE groups demonstrated equal growth in Grade 3 reading. Chapter 5 presents the discussion, implications, and recommendations that follow from these results. 99 CHAPTER 5. DISCUSSION, IMPLICATIONS, RECOMMENDATIONS Introduction The difference in Grade 3 yearly academic reading growth data as a result of the variation in the grouping practices of two populations of gifted students within the same distinct is discussed. The two gifted student groups were identified as homogeneously grouped highly gifted and heterogeneously grouped gifted and moderately gifted. This chapter of the action research project includes the summary of the current study, limitations of the project, implications of the current study, recommendations for further research, and conclusions. Summary of Study The purpose of the current study as a quantitative action research project was to examine academic growth differences by grouping of gifted and talented students. The research problem was that the participating school district did not know whether the homogenous or heterogeneous gifted programming model offered their gifted students the strongest opportunity for yearly academic growth. In order to answer this problem, the current study was focused on gain scores in Grade 3 reading for students in the two different gifted programs. The current study’s results evaluated the programming models used with the gifted students for the participating school district. The current study was conducted to help the district evaluate its own gifted programming practices. The research design followed an action research model within a large suburban school district and was causal-comparative for measuring and comparing 100 students’ academic growth on the dependent variable, the Measures of Academic Progress (MAP) conditional growth index (CGI) for Grade 3 reading. The independent variable was assessed based on gifted students’ enrollment in either the Leading Exceptional Aptitude and Performance (LEAP) or the Advancing Creativity in Education (ACE) program. The current study was noteworthy for its use of a formative growth measure without an upper achievement limit for making decisions about grouping practices for gifted students rather than the use of a criterion-referenced summative achievement assessment. This measure mitigates the ceiling effect often found in assessing advanced readers. The importance of Grade 3 reading scores in predicting future academic outcomes made this grade level ideal for gifted students whose progress is now a part of the state of Texas’ district accountability ratings. The participating suburban school district’s fall to spring data regarding its gifted and talented students’ growth rates for Grade 3 reading were utilized. To examine the gifted and talented students’ yearly academic growth differences by grouping, the 20132014 academic school year’s MAP scores for Grade 3 reading were tested for differences between students in the homogeneous LEAP programming for the highly gifted and students in the heterogeneous cluster grouped ACE classrooms for gifted and moderately gifted. The common attribute was the students’ status as identified gifted and talented in the participating school district. Two research questions (RQ) were answered in the current study. Research Question 1 (RQ1) Do Grade 3 gifted students enrolled in the homogenous elementary LEAP reading program show different yearly academic growth, the dependent variable, measured by the 101 MAP reading assessment than ACE gifted students housed in 23 separate elementary schools in a heterogeneous cluster model that relies on classroom enrichment? Research Question 2 (RQ2) Do Grade 3 ACE program gifted students enrolled in 23 elementary schools show different growth rates, the dependent variable, from the fall to spring MAP scores by elementary school? Summary of Findings and Interpretation of Results The first null hypothesis was: Grade 3 gifted students enrolled in the homogeneous elementary LEAP reading program show no differences in yearly academic growth measured by MAP reading assessment than ACE gifted student’s housed in 23 separate elementary schools in a heterogeneous cluster model that relies on classroom enrichment, μLEAP = μACE. It was tested using the independent t test and was not rejected. LEAP and ACE groups showed similar growth in Grade 3 reading. The similar growth shown by both groups of gifted students reveals that both grouping practices of homogeneous and heterogeneous produced increases in reading for Grade 3 gifted students. The mean difference in RIT scores from fall to spring was 9.02 for both populations combined. The CGI values formed a normal bell curve as seen in Figure 1 to validate the assumption of normalcy. The difference in RIT scores from fall to spring was 8.16 for the LEAP gifted and 9.14 for the ACE gifted students. These comparable increases occurred even though the average fall RIT for LEAP at 218.84 was higher than ACE at 207.37. Figure 2 offered a graphic illustration of the data points for both groups that highlighted the different levels of achievement but similar upward growth patterns. The two groups of gifted students had different beginning fall RIT reading scores with highly gifted students outperforming 102 gifted and moderately gifted. This difference in achievement informed the researcher’s intention to measure formative growth over summative achievement as a stronger measure of program effectiveness. The results for the LEAP and ACE groups did not display a significant difference in their CGI values measuring their fall to spring reading growth for Grade 3. Both groups reached similar academic growth as an outcome of two different programming options. Neither gifted group experienced a slower rate of growth as a result of grouping differences. Growth occurred for both groups in a similar pattern. The second null hypothesis was: Grade 3 ACE program gifted students enrolled in 23 elementary schools show differences in growth rates from the fall to spring MAP scores by elementary school, μ1 ≠ μ2 ≠ . . . ≠ μ23. It was tested by ANOVA and was not rejected. All 23 ACE groups demonstrated equal growth in Grade 3 reading. This further test was performed to determine if ACE students showed stronger or weaker growth rates dependent on their home campus. The result was no statistical significance between the 23 ACE schools. The descriptive statistics of each ACE campus is listed in Table 10. This, when combined with the former finding of no significant difference in yearly growth between LEAP and ACE, reveals two consistent program models for each gifted group with similar outcomes. The lack of difference in growth rates among the 23 elementary campuses that serve their gifted students through heterogeneous cluster grouping reveals an enriched curriculum program that is consistent across the district. Since no elementary campus outperformed another campus, the positive student growth outcomes were reliable not only across campuses but regardless of teacher. Neither campus nor teacher had a larger effect on gifted student growth than the other. This reliability revealed the strength of the 103 district in meeting the needs of its gifted and moderately gifted students through a welldesigned cluster grouping model. Separate classes for the highly gifted LEAP students did not produce superior gains over the cluster grouped gifted and moderately gifted students in the general education classes. The instructional practice of accelerated curriculum in the homogeneous classes did not outperform enriched and differentiated general education curriculum for gifted students. However, it can be said that both groups of gifted students benefited from being grouped with their academic peers. Implications Adaptive assessments, like MAP used in this research project, provide more precise measurements for evaluating high performing gifted students when compared with fixed-form alternatives (Northwest Evaluation Association [NWEA], 2012). The mean fall RIT scores for the highly gifted LEAP students at 210.84 compared to the mean fall RIT of gifted and moderately gifted ACE students at 207.37 (as seen in Table 9) indicate that without using an adaptive and above grade level assessment tool, advanced students’ annual growth cannot be adequately measured. If a grade level summative achievement measure had been used, less would have been revealed about the nuanced academic growth in the educational setting for students who already perform above grade level (NAGC, 2009). Using merely summative criterion based state assessments, such as State of Texas Assessments of Academic Readiness (STAAR), would not have been appropriate to indicate growth among gifted students. The growth measure has been added as part of the Texas accountability system might adequately reflect the annual growth of general education students from year to year but not of gifted students who might hit the 104 assessment’s ceiling each year. Aligning itself with the state accountability trend, the NAGC shifted focus to student growth measures. In order to adequately identify academic progress, the NAGC recognized the problem of measuring gifted students’ yearly academic growth using traditional measurements and strongly urged educators to use off-level standardized assessments to measure the academic progress of gifted students (2009). While the focus on accountability for high achieving students suggests a positive direction for Texas, the STAAR assessment instruments as an indicator of growth might be inherently flawed for gifted students. Educational experiences that enhance academic growth are numerous. The effect of grouping, curricula, prior knowledge, campus leadership, teacher knowledge, and experience, and even home factors cannot be isolated. However, the expectation for annual growth for gifted students requires educators to review formative data. Formative data use might show the growth of high achievers through an assessment without a grade level achievement ceiling. Westberg and Daoust (2003) replicated Archambault’s (1993) study in order to document if limited differentiation continued for gifted students in heterogeneous classrooms a decade later after Archambault’s study. Unfortunately, Westberg and Daoust generated similar findings to Archambault’s orginal results. Third and fourth grade teachers offered relatively insignificant efforts at differentiated instruction or content acceleration of curriculum to meet the needs of gifted students clustered in general education classrooms (Westberg & Daoust, 2003). However, other researchers found that effective differentiated instruction tends to maximize annual growth among gifted and talented children (Gentry & Mann, 2008). 105 The results showed similar yearly growth in Grade 3 reading for students in selfcontained gifted classes and for gifted students in a cluster grouped model within the general education classroom. This research project did not address the amount of time nor the number of times gifted students received differentiated instruction with a more complex curriculum. Therefore, Westberg and Daoust’s (2003) findings cannot be supported or denied with the current study’s results. Delacourt and Evans (1993) compared four different models for gifted programming that included full-time special schools, homogeneously grouped classes, pull out programs, and within-class cluster grouping. Gifted students in the special schools, homogeneous classes, and pull-out programs showed substantially higher levels of achievement than students in the cluster groups (Delacourt & Evans, 1993). Delacourt and Evans (1993) concluded that if not effectively executed, within-class cluster grouping offers no specific or targeted programming for gifted students. In addition, Reis (2004) only compared two program models: full-time homogeneous programming for Grade 3 reading and a cluster-group model. Reis found “grouping [gifted students] heterogeneously and even providing cooperative learning in flexible groups tends to lower achievement and motivation as well as increase poor attitudes toward school” (p. 81). The current study does not support the findings of Delacourt and Evans nor of Reis in regard to decreased performance for gifted students in a cluster group model because of the similar average reading growth that occurred in both groups. While cluster grouping, as a model for serving the gifted, is a practical means to an end, maintaining recommendations made by past researchers may increase the model’s effectiveness such as in the participating school district of the current study. First, students should be clustered with their intellectual as well as same-age peers (Bryant, 106 1987; Delcourt & Evans, 1994; Hoover, Sayler, & Fedlhusen, 1993; McInerney, 1983; Oakes, 1985; Rogers, 1991; Slavin, 1999; Winebrenner, 1992). Secondly, cluster grouping provides for full-time gifted student services without requiring additional programming or staffing (Hoover et al., 1993; LaRose, 1986; Rogers, 1991; Winebrenner & Devlin, 1994). These historical findings have informed the cluster grouping model design in successful heterogeneous classrooms serving the gifted as well as in the current study’s participating school district. The current study’s findings of equal annual growth in Grade 3 reading for cluster grouped gifted students and homogeneously grouped gifted students supports continuing these best practices in the participating school district. Academic effects for robust differentiation in a cluster grouped class can be substantial depending upon the amount of compacting and enrichment that actually occurs for learners. Some researchers, such as Gentry and Mann (2008), believe that differentiated instruction for gifted students in a cluster grouped model tends to maximize annual growth. Because enrichment and differentiation is largely left up to the individual classroom teacher to implement, the difference between ideal and actual effectiveness is recognized. Gentry and MacCougall (2008) argued for the following guidelines: Curricular differentiation is more efficient and likely to occur when a group or cluster of high-achieving students is placed with a teacher who has expertise, training, and a desire to differentiate curriculum than when these students are distributed among many teachers. (p. 12) The participating school district’s cluster grouping model for gifted learners reflects the guidelines offered by Gentry and MacCougall. Under ideal circumstances, gifted students in cluster grouped gifted classes can gain from 1.5 years to 1.75 years of growth in the content area for which they are 107 grouped (Rogers, 2007). The current study did not include annual reading growth as a variable in the same manner as it was used by Rogers (2007). However, the similar growth of cluster grouped gifted students who received enriched differentiation in the classroom was equal to the reading growth of highly gifted students in a separate homogeneous classroom. Therefore, reading growth for students who only received differentiation in the general education classroom was not diminished by the program model. Junior Great Books “constitutes one of the most effective literature programs available for gifted learners” (Van Tassel-Baska et al., 2002, p. 32). Aldrich and McKim (1992) in their curriculum review for gifted language arts programs, rated Junior Great Books as exemplary (p. 37). Junior Great Books also offers “strong inquiry-based training programs for teachers with a central focus on improving students’ quality of discourse and enhancing their interest in literature” (Van Tassel-Baska et al., 2002, p. 32). A quality curriculum is essential to the success of a cluster group model. The current study’s findings revealed similar reading growth between the cluster grouped gifted model using the Junior Great Books as the differentiating curriculum used for students of ACE cluster group program; therefore, the Junior Great Books cannot be determined to cause of the positive results observed for the cluster grouped children. The positive growth measured in the LEAP highly gifted homogeneously grouped students from the current study support the findings of Van Tassel-Baska et al. (2002). As in the current study, Van Tassel-Baska et al. used the William and Mary curriculum that “offers a rich set of applied research questions for exploration” (p. 33). The current study’s findings support the continued use of the William and Mary curriculum for highly 108 gifted students. Van Tassel-Baska et al. (2002) concluded that a robust curriculum over grouping models provides stronger academic gains for gifted students. The current study supports the use of the William and Mary curriculum for gifted students in English language arts; however, whether the curriculum or the grouping accounted for the academic growth observed in the LEAP students remains unknown. The current study’s findings cannot account for whether content acceleration or curriculum enrichment offers the higher level of growth for gifted students. The data did support content acceleration for highly gifted students and curriculum enrichment for moderately gifted and gifted students as offering similar academic growth during Grade 3 reading. The current study’s findings add credence to the continuing dilemma over which grouping model offers the optimal environment for academic growth for gifted students. As shown in Table 5, Rogers’ (1993) study of the effect sizes for different program options for gifted students revealed a .33 academic effect size for both cluster grouping (heterogeneous) and ability grouped (homogeneous) classrooms. The .33 effect size did meet the threshold for practical significance. However, even Rogers’ findings showed neither grouping model to be more beneficial over the other for ensuring student growth. The action research findings of the current study confirm the academic benefits of cluster grouping or homogeneous grouping to be similar for gifted students during Grade 3 reading. Likewise, the small effect size in the current study of .237 did not reveal a difference in the academic benefits between either the LEAP or ACE grouping pattern. Therefore, the effects of both are similar, as Rogers’ (1993) study revealed. But, like Rogers, this researcher believes that a larger effect size does not necessarily identify a 109 particular grouping pattern as superior to another group pattern. The success of a gifted program lies in a multitude of variables that are difficult to measure. Such variables include teacher acceptance of gifted students, students’ prior experiences, and family factors that do or do not support gifted learning. Colangelo et al. (2004) encouraged educators to match the level, complexity, and pace of the curriculum to the readiness and motivation of the targeted students. Educational equity respects individual differences. Gifted students have their own inherent variations in ability and readiness (Colangelo et al., 2004, p. 2). Colangela et al.’s (2004) conclusions as well as the impetus for individualization, even within gifted programs, was echoed by Rogers (2007) who challenged gifted and talented programs to personalize instruction with differentiated pacing regardless of the grouping model. The two-tiered approach to gifted programming in the participating school district might offer the best answer to the wide variance of ability levels even within the gifted population. The variances in readiness and ability to progress at an accelerated rate increase as a student’s IQ increases. Having a separate program for the highly gifted seems to serve the higher level gifted population needing the most differentiated curriculum. Figure 2 graphically revealed the difference in reading scores of the highly gifted in comparison to the gifted and moderately gifted. There was little overlap in achievement between the two groups. The figure supported using two separate grouping models in the same school district as a method for addressing the entire gifted IQ continuum. Nevertheless, homogenous programming is the grouping model historically disparaged as elitist and is therefore largely ignored by most school districts (Weinebrenner, 1992). 110 Some unequivocal support of homogenous grouping has come from respected researchers in the field. For example, Gagne (2007) who bid “educators to aim as much as possible for full-time grouping of gifted students” (p. 109). Gagne provided this call to arms only after the findings of earlier studies showed greater academic gains occurred with gifted students who were grouped homogeneously. “Full time programs, whether they involve special schools or a school-within-a-school, give students maximal exposure to intellectual peers and thus peer support for high achievement” (Olszewski-Kubilius & Limburg-Weber, 2014, p. 3). Gagne and Olszewski-Kubilius et al.’s (2014) calls for full time homogeneous classes for the gifted have largely gone unheeded in the political climate of No Child Left Behind. The current study does not support the recommendations of Gagne (2007) nor the conclusions of Yiping et al. (2014) indicating “a slight superiority of homogeneous ability groups over heterogeneous ability groups in promoting student achievement” (p. 445). Yiping et al.’s effect size was .36 for reading but did not discuss the level of differentiation within the general education classroom for gifted students nor the curriculum used to differentiate. Yiping et al. even described their findings as slight and not overwhelming or definitive. Financial and political concerns about separate programming models being used with gifted students continue to raise the issue of appropriate grouping practices. Research studies of academic progress by gifted grouping thus far remain inconclusive and have failed to guide districts to be confident in decisions about the programming models to use with gifted students. The participating school district’s unique two-tier programming for gifted students that allows for using two different grouping practices offered the researcher an opportunity to isolate instructional grouping model as a factor 111 for student reading growth. The lack of research directly comparing homogeneous and heterogeneous gifted student grouping models offered an opportunity for the current study to add to the body of research and facilitate educational decisions leading to successful gifted student outcomes. Limitations The current finding’s results might be limited to the host school district. The hosting school district uses specific programming, curriculum, and professional development with its teachers to address the needs of the gifted in both homogenous and heterogeneous classrooms. The current study’s time frame was limited to a one-year academic growth rate in Grade 3 reading and did not track a cohort of gifted students over several years. The current study did not discriminate between students who entered the district’s gifted program from kindergarten and who entered it at either first, second, or third grade. Students were not grouped for the current study based on their date of gifted identification. Even with the district guidelines for identifying students as gifted, moderately gifted, or highly gifted, disparity in academic readiness and performance exists within the gifted student population as it does in the general education population. The two gifted student groups were not disaggregated by sex, race, or socioeconomic status. Three students who did not complete a fall and a spring MAP reading assessment were eliminated from the current study. Student reading progress was not disaggregated based on the number of years of experience of the teacher nor by the years of experience teaching gifted students. Neither professional development nor gifted certification of the teacher of record was included as a variable in the current study. Qualitative data such as a student’s perceived reading 112 progress or the teachers’ assessments of their students’ reading progress were not included in the current study. A one-year growth measure does not capture the cumulative impact of the educational setting on the student. Previous instruction and experiences outside of the educational setting most certainly affect a student’s early reading performance and cannot be tied directly to the classroom grouping model. The wide array of influences on a child’s reading ability does not lend itself to a direct cause and effect conclusion. Grade 3 is considered critical to a student’s future academic success. Hernandez (2011) correlated Grade 3 reading to high school graduation and showed “students who fail to reach the critical milestone of mastering reading by the end of third grade often falter in the later grades” (p. 3). Hernandez’s data did not specifically identify gifted students. Because gifted students often have an accelerated reading learning curve, Grade 3 may or may not represent the most pivotal grade to evaluate reading growth. The particular significance of Grade 3 reading is acknowledged because it is the time when a general education child “shifts from learning to read and begins reading to learn” (Hernandez, 2011, p. 4). This same assumption might not apply to gifted students. The current study did not address other models for gifted programming such as pull-out, push-in, no programming at all, or combining highly gifted with gifted and moderately gifted students. The gifted models were offered in Table 3 with the pull-out model dominating elementary programming at 48% (Van Tassel-Baska, 2006). Cluster grouping was used by 36% of the school district sample (Van Tassel-Baska, 2006). Separate gifted classrooms (homogeneous grouping) were only offered in 7% of surveyed districts (Van Tassel-Baska, 2006). The limited opportunity for highly gifted homogeneous classrooms in the current study’s participating school district allows the 113 findings of the current study to be unique and significant to the gifted community especially when compared with another in-district cluster grouped models. Programs that constitute substantial adjustments to curriculum for academic readiness produce positive effects (Reis, 2004). However, the current study’s findings cannot support Reis’ (2004) findings regarding homogeneous grouping having the largest effect on students’ yearly academic growth. The academic effects in the current study were similar for both heterogeneous and homogenous grouping. The explaining the similarities in the effect sizes falls outside of the scope of the current study as an action research project, and further study is needed. Recommendations The current study could be replicated by other school districts using the two grouping models with their gifted students. If a district does not have differences in grouping for gifted students, a yearly growth measure should be conducted and compared against the results observed in the current study. The assessment instrument for ascertaining growth measures needs to offer an extended achievement ceiling for gifted students who may enter at a higher reading achievement than other students. A study that includes a comparison between the annual growth rates in Grade 3 reading for general education students versus gifted students could be helpful to evaluate the differences in growth of gifted students. Continued research addressing the effects of grouping patterns on gifted students’ academic growth deserves the attention of other researchers. Simple assumptions that gifted students grow during Grade 3 reading at the same rate regardless of the grouping practice within a district are not appropriate based on the current study’s results. Tracking the reading growth of gifted students at multiple grade levels may provide 114 insight into the pattern of reading development of high achieving students which may or may not be similar to the general education student population. Finally, the effects of different reading curricula on the reading growth for gifted students should be studied. The participating school district adopted Gagne’s (2007) advice to offer one program for mildly and moderately gifted students who comprise 90% of the gifted population and another program for the remaining group of highly gifted students. The results support the current bifurcated programming model in producing academic growth within all gifted students. Other districts might consider this plan based on the positive results of the current study. Conclusions A growth model for accountability measures performance gains rather than performance against a criterion referenced grade level standard assessment. This measurement ensures that gifted students’ learning is as valued as every other student’s learning. This performance gains model for accountability may shift the focus back to educating students to their full potential and not simply educating students to reach the same expected measurement of success. Educators’ overriding concern for gifted students are their students’ ability to be challenged with the opportunity for continuous academic growth. The current study’s results report that gifted students continue to grow in academic skills in both grouping models. They do not suggest that grouping does not matter in academic growth. The results do indicate that both homogenous and heterogeneous classes produce academic growth. Perhaps the best conclusion is that specifically for the highly gifted student, a homogenous grouped classroom offers the same growth as gifted and moderately gifted 115 students achieve in a heterogeneous cluster grouped model. Homogenous classrooms may be the strongest model for highly gifted, and cluster grouping may be the strongest model for gifted and moderately gifted students. The gifted continuum may require different grouping practices to maintain the same growth measures between models. The two programs perform equally well with their differently identified gifted populations and can be judged as equally effective in producing academic growth in Grade 3 reading. The findings from the current study should not dishearten educators of the gifted. The problem of lack of academic progress or gifted student stagnation was not borne out by the data in the current study. All levels of gifted students showed positive academic gain. The highly gifted homogeneously grouped LEAP students achieved at higher initial levels as seen in Figure 2. The RIT scores of both LEAP and ACE students represented jaggedly parallel positive trajectories regardless of any differences between the two groups’ initial RIT scores. However, the LEAP students grew academically in Grade 3 reading at the same rate as the cluster grouped ACE students. While both programs showed relatively equal academic progress, and no significant cause and effect between the grouping models occurred. The current study’s findings offer optimistic affirmation that specific curriculum, grouping, and evaluation are needed for effective gifted programming. Resource allocation is to be considered for any educational program. Cluster grouping is considered cost neutral since the enrichment of curriculum occurs within the general education classroom. A homogeneous classroom may require a school district to increase gifted programming funding. The participating school district’s commitment to 116 gifted programming, as part of its overall mission, draws to itself the gifted students and parents committed to growing. The current study’s findings should encourage school districts who are currently evaluating their own programming for gifted students. Several models may meet the needs of gifted students and the one best model theory for gifted programming may be a myth. 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